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             \author[1]{Dr. Jagwinder  Singh}

             \affil[1]{  Dr B R Ambedkar National Institute of Technology Jalandhar}

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\date{\small \em Received: 14 December 2010 Accepted: 3 January 2011 Published: 16 January 2011}

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\begin{abstract}
        


India is one of the fastest growing markets of the world. The potential not only lies in the urban India but in the rural India also. The study has been carried out to differentiate the buying behaviour of rural households from that of urban households. Three durable goods from three different product categories; Television (entertainment product), Refrigerator (home appliance), and an Automobile (twowheeler, motorcycle and car/jeep) have been selected for study. A sample of 411 (204 from urban and 207 from rural areas) households across the Punjab state (India) have been selected on the basis of nonprobability convenience sampling. Overall no significant differences could be observed between rural and urban consumers in terms of their; timing of purchase, buying the same brand of other durable, number of items, and duration of planning before buying. Habitat (rural or urban) has a relation with income for the timing of buying a television, refrigerator, and automobile except in case of buying of an automobile on festive / special occasion, where the income had no relation with habitat. There is a relation between habitat and income in terms of duration of planning for different time periods before the buying of a television and refrigerator. The habitat also reveals association with income in terms of planning for months before buying an automobile. No association has been observed between habitat and income in case of planning for few days, few weeks and years before buying an automobile.

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\keywords{Rural, Urban, Need, Income, Family Size}

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\let\tabcellsep& 	 	 		 
\section[{INTRODUCTION}]{INTRODUCTION}\par
ndia is the world's 12 th largest consumer market. By 2025, it is projected to be ahead of Germany, the fifth largest, according to a recent \hyperref[b7]{McKinsey (2007)} survey. The biggest strength of Indian markets lies in the size, not in individual spending. With the rise in income, over 291 million people will move from desperate poverty to a more sustainable life, and India's middle class will increase incredibly by over ten times from its current size of 50 million to 583 million people. There had been a strong misperception about the rural markets. One that rural India is poor and there is a lack of adequate infrastructure. Second, rural India depends upon agriculture as a sole source of subsistence. But the reality is different.  {\ref MART (2005)}, the specialist rural marketing and rural development consultancy agency, has found that rural India accounts for 46 per cent of soft drinks sales and 49 per cent of motorcycle sales. Out of two million BSNL mobile connections, About : Dept. of Management, Dr B R Ambedkar National Institute of Technology Jalandhar-144011, Punjab, India. Tel: 91-95011-03708 E-mail: jagwinpandher@yahoo.co.in subscription from small towns and villages accounts one-half of it. The states like Punjab and Haryana get a favourable ranking in terms of ownership of assets, consumer durables, two-wheelers, and cars in rural areas. In rural Punjab there are many families particularly from Doaba region, whose one or more family members have gone abroad. Their standard of living is even far better than many of the urban residents. According to \hyperref[b11]{Sinha (2005)}, rural India in which more than 74 per cent of the population of the country resides; generates onethird of country's GDP, and accounts for 38 per cent of two-wheelers sales of the country. All people are not engaged in agriculture; about 25 per cent have nonfarm occupations. Disposable income again is not low. Per capita annual income in rural area is Rs.9481 as against Rs.19,407 of urban areas. Rural people have the advantage, as they need not to bear expenses like rent, and water bills etc. The number of middle-class households are 15.6 million in rural areas, and 16.4 million in urban. The rural market for durables is Rs. 5000 crore, for tractors and agricultural inputs Rs. 45,000 crore (1 crore = 0.1 billion) and two and fourwheelers, Rs. 8000 crore. In total, it has a potential of Rs. 1,23,000 crore. The understanding of rural behaviour, appropriate pricing and distribution may help marketers to increase its potential. The Federation of Indian Chambers of Commerce and Industry \hyperref[b3]{(FICCI, 2005)} has carried out a comprehensive Survey of industries in the consumer durable goods sector. The survey which; is based on feedback and interaction with representatives of consumer durables industry, allied industry organizations, associations, government agencies, and public sector undertakings; reveals that the sector is poised for a wide jump due to technological improvisation, falling prices due to competition, aggressive marketing, and declining import tariffs. There is a dramatic change in the behaviour of the consumer with the increase in their disposable incomes. The consumers have started perceiving many of the luxury goods as necessities. 
\section[{II.}]{II.} 
\section[{LITERATURE REVIEW}]{LITERATURE REVIEW}\par
Consumer durable is a product that must be durable in use and must be expensive relative to income. An item may be durable for a working class family and at the same time may not necessarily be durable for upper middle class consumer. However, there is hardly any argument for items like cars and I refrigerators and there are not many marginal items. Durable purchases by and large are group decisions for the three reasons: one it involves the considerable outlay of the family; second the user of the person may not necessarily be the one who actually pays for it; and third it is bought for the use of several members of the family. However, in certain cases unilateral decisions for the buying of durable item are taken by one member of the household, but it is not common. The buying decisions of such items are generally unique and irrevocable. These decisions are not taken frequently, rather taken very rarely, perhaps once and twice in one's life. The buying decisions of durables are by and large group decisions; complex ones; and more concentrated amongst the upper-income groups. The durable goods are mass-produced in anticipation to consumers' demand and involve huge capital cost \hyperref[b2]{(Downham and Treasure, 1956)}.\par
Economic reforms of 1979 brought a great change in the consumption patterns in China. Durable goods particularly experienced a great change both in variety and quantity. The possession of certain durable goods in the past has affected the possession of the same durable during the current period and the possession of certain durables has affected the purchase of other durables. Prior to the reform period, the products like washing machines and refrigerators were scarcely produced domestically. Also there were no provisions of installment plans, credit cards or any other form of consumer loans. The increased consumption of durable goods has occurred as a result of several factors including increase in per capita consumption. The data collected by Statistics Bureau, Tianjin Municipal Government in 1984 reveals that household income had a statistically significant positive effect on all consumer durables except the electronic watch. This was so because wrist-watch being low priced item did not account for a considerable share of household budget. The household size was positively related to the ownership of bicycle, electric fan and the record player. But it showed a negative effect for the purchase of television sets. Ownership of washing machine and refrigerator was also found affected by living space and the supplementary area, as these items are physically large. Age did not affect the consumption of large number of items except bicycle and transistor radios, which were relatively old-type durables. Education had a positive effect on purchases of refrigerators and record players. Most Chinese households perceived that one is enough for most durables \hyperref[b4]{(Hu et al, 1989)}. Indian middle class also consider these items of infrequent purchase as revealed by the study of  {\ref Rahman and Bhattacharyya (2003 a)}. The average of kitchen refrigerator was five-and-a-half years and for a colour TV was five years as per the exploratory study conducted in the campus of Indian Institute of Technology Roorkee  {\ref (Rahman and Bhattacharyya, 2003 b)}. It had been found that the tendency of the households toward the buying of oldtype durables (e.g. bicycles, sewing machines, black and white televisions) decreased considerably than to modern ones (e.g. washing machines, colour televisions and cameras). The possession of durables reduced the probability of purchasing another one of the same type except for refrigerators and watches. The study further explores that the last period possession of a refrigerator had a positive effect on that period purchase of washing machine, but no effect on the purchase of the colour television. Similarly last purchase of washing machine increased the probability of purchase of refrigerator during the period of study, but remained neutral to the purchase of colour television. The last purchase of colour television did not affect the purchase of either refrigerator or washing machines. This implies that both washing machine and refrigerators were complimentary to each other. The current purchase of washing machine increased the probabilities of current purchases of both a refrigerator and colour television. Similar was the effect of current purchase of colour television on both refrigerator and the washing machine. But the current purchase of refrigerator was found indifferent to the current purchase of both washing machine and a colour television \hyperref[b4]{(Hu et al, 1989)}. Two-wheelers have become more important particularly among middle income group of consumers in India. Consumers consider comfort, price, maintenance, fuel efficiency, appearance, durability, and resale value as important attributes while buying twowheelers. The study carried out in Tamil Nadu (India) reveals that there was 100 per cent brand loyalty for 'Bajaj Chetak' scooter, followed by 'Hero Honda' and 'TVS Champ', in which it was 93 per cent. The brands Hero Puch and Yamaha had 83 per cent whereas 'Bullet' had 82 per cent brand loyalty. The study did not find any significant difference between source of information and income of consumers. A significant difference was observed as regards to source of information (newspaper, hoarding and posters) and the age of the respondents. The study further reveals that 53 per cent of the respondents considered only one brand ignoring all others. Factor analysis yielded five factors that motivated the consumers in their purchase decisions. These were fuel efficiency, maintenance cost, price, image and warranty. Cost, image, and service influenced the selection of motorcycles. But all variables were rated equally in case of scooters and mopeds. In terms of total satisfaction, all mobike owners were found fully satisfied with style, scooter users with durability, and moped-owners with break conditions. The job knowledge of the mechanics was the most significant consideration for selecting dealer or non-dealer service centers \hyperref[b0]{(Ahmed, 2001)}. 
\section[{Socioeconomic conditions}]{Socioeconomic conditions}\par
considerably affect consumer behaviour \hyperref[b5]{(Kim et al, 2002)}. Income affects the buying behaviour in terms of amount, type and prices of products purchased. High-income consumers put in more effort in information search. Utilitarian evaluation criterion is inversely related to income. Income is more important in the buying of low social value product \hyperref[b12]{(Williams, 2002)}. When the income of the consumer is low, the consumer largely tends to focus on price and performance attributes and with the increase in income the consumer becomes more hedonic and may start desiring goods from western nations \hyperref[b5]{(Kim et al, 2002)}.\par
There are noticeable differences in purchase decision times for new cars and major household appliances. The study was conducted on 1300 households of US who had purchased one or more products of study before August 1968. The decision times were found to vary widely. About half of the buyers took two weeks or less while a third took six months or more. The distribution for cars and major household appliances were similar. The study reveals that the purchasers satisfied with their old products were found engaged in less information seeking than those who either were not fully satisfied with their old products or did not have regular use of the product. Moreover the satisfied users were able to gather required information in less time than other types of buyers. The satisfied users, whose products had already expired their life, took less time than those satisfied users with their products in working conditions. Similarly the buyers who had extensive purchase experience in the past took less time than those who had not much experience. Even the highest income households lacking buying experience took more time than any other income group. Also the increased information seeking activity was associated with longer decision times  {\ref (Newman and Staelin, 1972)}. These households might have remained dependent on others for procuring information but assessed its credibility themselves. The stages in the life-cycle also play a considerable role. As families grow, size and the characteristics of the product that was last purchased, change. The average satisfied user of his old product who was giving considerably high importance of out-ofstore information seeking took greater time than the average buyer who was either dissatisfied with his earlier purchases or did not have regular use of that kind product. There had been contrasting result to Ferber's hypothesis that 'larger the size of planned purchase, the longer the purchasing horizon is likely to be' as the same was not observed for cars, the average duration of which was not much longer than that for appliances. The study concludes that the decision times are not affected by traditional demographic variables, rather these depend upon condition of old product, ability to judge the product well, and prior experience  {\ref (Newman and Staelin, 1971)}. Stages in the life-cycle also play a significant role as with the growth in the family, needs change and therefore, family may have to buy a different appliance than they earlier bought \hyperref[b8]{(Newman and Staelin, 1970)}.\par
Gift giving to the children is a strong feature of Christmas in the western countries. It is a unique, multifaceted, and ritualistic consumption occasion suggesting that the season is peak in consumption in western cultures and gift giving on this occasion is a hedonistic behaviour and it is a traditional Christmas ritual. The previous studies reveal that people seem to spend quite freely on the preparation and the enjoyment of the Christmas period. This period is an important occasion not only for business but for those who make purchases to participate in Christmas activities. This exploratory study measures the feelings (affect) and the evaluations (cognitions) as the valid elements of the Christmas spirit construct. Social values of the consumption objects are associated with various social and cultural aspects. The affective judgments directly and subjectively relate the person to the objects of interest more than the effects of cognitive appraisals. Some studies have pointed out that though both affective and cognitive elements act independently yet they are significantly related to actions and behavioural intentions. The other studies reveal that the differences between affect and cognition are minor and exist due to their interwoven nature. Affect include multifaceted associations about internal and primal reactions of emotions and feelings as well as emotions and moods. Cognitions on the other hand refer to thoughts, beliefs, and perceptions and is a response to the environment brought about by the evaluation of the consumption object. The basis of cognition is the utility of the consumption objects. A family ritual is a highly stylized cultural performance involving several family members and is a symbolic behaviour. Rituals artifacts communicate specific symbolic messages, guide the artifacts and identify when to use what icon or symbol. Christmas season is time of tradition and ritual. It can be personalized to create an individualized custom ritual. Christmas is a consumption object like an advertisement and there can be an upbeat, and warm feelings toward Christmas. The study concludes that the high regard or spirit does not necessarily embrace materialistic indulgence. Christmas spirit is an attitude to a season not to the materialism. However brands can be integrated with Christmas rituals, artifacts and script \hyperref[b1]{(Clarke, 2007)}.\par
Many companies of consumer products (both durable and non-durable) are making their efforts in rural areas. This is so because of increase in rural purchasing power over the past decade due to increase in support prices for the farm produce. Increase in infrastructure and change in lifestyle due to proliferation of television have changed the buying habits of the rural people. The study carried out in rural Pondicherry to understand the buying behaviour on two products -wristwatches and footwear reveals that rural consumers consider only one brand and visit one shop before making a purchase decision. Though buyer himself takes decision for buying watches, yet retailers and advertisements have been found important influencers. Unlike urban areas, where watches are treated as gift items, these are bought as and when necessity is felt. Brand name and price were the important considerations in buying watches. Utility and longevity (quality) were the prime considerations for footwear and no significant influence of brand was observed in this category. They used to buy both the items based on the necessity felt rather than waiting for any offer or festive season \hyperref[b10]{(Shivakumar and Arun, 2002)}.\par
III. 
\section[{METHODOLOGY}]{METHODOLOGY}\par
The study, which is descriptive in nature, has been carried out in Punjab state (India). Three durable goods from three different product categories Television (entertainment product), Refrigerator (home appliance), and an Automobile (two-wheeler, motorcycle and car/jeep) have been selected for study. A sample of 411 (204 from urban and 207 from rural areas) households across the state have been selected on the basis of non-probability convenience sampling. The data about current ownership or likelihood of purchases in the next 24 months on the select durable goods (television, refrigerator and any type of automobile) were obtained. In case of additional purchase/replacement or their likelihood in near future about the select items, the respondents were asked to give their responses only to the latest/likely buying. All respondents had been found possessing at least one item of each select product. The main objectives of the study are as under:\par
? To compare rural and urban habitants for their; timing of purchase, buying the same brand of other durable, number of items, and duration of planning before buying.\par
? To analyze an association between habitat and income, and habitat and family size for the select variables.\par
The study has been based on both primary as well as secondary data. In-depth interviews have been conducted to look into insights of the consumers' behaviour with the help of a pre-tested bilingual questionnaire that was served to the respondents to obtain important information as regards to the prime objectives of the study. 'Buying the same brand of other durable' has been studied only for television and refrigerator. This is so because that the marketers of these products are less likely to engage in the marketing of automobiles and vice versa.\par
The p-values have been calculated for the select variables and on comparing with central value their significance has been checked at 95\% confidence level.\par
Similarly p-values have also been calculated to observe the significance (95\% confidence level) of differences between the responses of rural and urban consumers. Discriminant analysis has also been carried out to observe the differences between rural and urban consumers in terms of their buying patterns. Chi square distribution has been used to test an association between habitat and income, and habitat and family size.\par
IV. 
\section[{LIMITATIONS OF THE STUDY}]{LIMITATIONS OF THE STUDY}\par
The sample size is too small to generalize the findings. Moreover only three products (only one product from three categories) have been selected. However there are large number of consumer durables such as washing machines, water purifiers, air conditioners, generator sets, and kitchen appliances etc. There is again a variety of items within a product category and they carry different utilities at different values for different strata of consumers. The study needs to be further extended in terms of other variables such as differences in the behaviours of different socioeconomic groups of rural and urban consumers and other demographic considerations. Also more predictors can be added in further studies. Similarly, similarities and dissimilarities among different occupational categories of rural and urban consumers can be considered in terms of their behaviours towards consumer durables.\par
Also only those households have been considered for study that had either all the three items (television, refrigerators and any type of automobile) or they were likely to buy in near future. There are many households which may have not any one or more of these select items and they were also not likely to buy in near future. Some households had possessed some of the select durables for a long time. The consumers' preferences, considerations, and family life-cycle since then might have changed and the behaviour particularly as regards to the influences within the household might be different as compared to the time of acquisition of that durable. Therefore, the likely buying of next 24 months has been made the part of the study to minimize the impact of this limitation.\par
V. 
\section[{DATA PRESENTATION AND ANALYSIS}]{DATA PRESENTATION AND ANALYSIS}\par
The results are summarized here as under: a) Television Table T 1 reveals that no significant difference could be observed between rural and urban consumers as regards to timing of buying a television (X 1). A large majority of both rural and urban consumers had preferred to buy a television in case of need. There had been significant differences between rural and urban consumers as regards to the buying of same brand of television as that of refrigerator (X 2). Eighty four per cent of the rural consumers had preferred to buy the same brand of television as that of refrigerator whereas; 69 per cent of the urban consumers preferred the same (Table \hyperref[tab_0]{T} 2). This reveals that both rural and urban consumers had preference to buy the same brand of television as that of refrigerator or vice-versa. There had been significant differences between rural and urban consumers as regards to the one and two number of television sets in a household (X 3). Sixty eight per cent of the rural households had only one television set whereas; urban households with only one television set had been found to be 53 per cent. On the other side 39 per cent of the urban households had two televisionsets whereas; only 25 per cent of the rural households had the same number of television sets (Table \hyperref[tab_0]{T} 3). This implies that majority of both rural and urban households had only one television set. No significant difference could be observed between rural and urban consumers as regards to three and four television sets in a household. There had been no significant differences between rural and urban consumers as regards to the duration of planning before buying a television set (X 4). Maximum numbers of consumers have planned for few days before the buying of a television set (Table \hyperref[tab_4]{T 4}). The structure matrix reveals X 2 as the most discriminating variable followed by X 3 and X 4. The classification results reveal the correct classification of 66.7 per cent of original as well as cross-validated groups (Table \hyperref[tab_4]{T 5}).\par
There had been no significant difference between rural and urban consumers of both the income groups ('upto Rs. 2.5 lakh' and '>Rs. 2.5 lakh') in terms of timing of purchase. Majority of the consumers of these groups had bought a television set at the time of need. The significant value of chi square indicates an association of the habitat (rural and urban) with income in terms of timing of buying of a television set (Table \hyperref[tab_0]{T} 6). There had been significant differences between rural and urban consumers of income group 'upto Rs.  The significant differences between rural and urban consumers had only been found in the income group '>Rs. 1.5 lakh to Rs. 2.5 lakh' as regards to the duration of planning of few days, and weeks; before buying the television sets. Thirty eight per cent of the urban consumers and 12\% of the rural consumers of this income group planned for few days before the buying of a television set. Thirty four per cent of the urban consumers and 67 per cent of the rural consumers of the said income group planned for few weeks before the buying of a television set. Maximum number of consumers of both the consumer groups belonging to all the income groups had planned for few days before the buying of a television set. The chi square had been found significant for all three durations indicating an association of habitat (rural and urban) with income in terms of their duration of planning before buying a television set (Table  {\ref T 8)}. There had been significant differences between rural and urban consumers of family size 'upto 4' as regards to the one and two television sets per household. Forty nine per cent of the urban consumers and 83 per cent of the rural consumers of the said family size had only one television set. Thirty nine per cent of the urban consumers and 10 per cent of the rural consumers of the said family size had two television sets. However no difference could be observed for this family size for the three or more number of television sets per household. Also no significant difference could be observed between these consumer groups of family size 'greater than four' for any number of television sets. Majority of the consumers of all the groups of select family sizes belonging to both rural and urban residents had only one television. The chi square had been found significant for any number of television sets per household indicating an association of family size with habitat for the possession of number of television sets per household (Table  {\ref T 9)}. 
\section[{b) Refrigerator}]{b) Refrigerator}\par
Table R 1 reveals no significant difference could be observed between rural and urban consumers as regards to buying a refrigerator in case of need (X 1). Eleven per cent of the rural consumers had preferred to buy a refrigerator on special occasion, which is significantly greater than the preference of only 3 per cent urban consumers. However during festive seasons, the urban consumers (16 per cent) had greater tendency to buy the same as compared to their rural counterparts (10 per cent). A large majority of both rural and urban consumers had preferred to buy a refrigerator in case of need. There had been significant differences between rural and urban consumers as regards to the buying of same brand of refrigerator as that of television (X 2). Eighty per cent of the rural consumers had preferred to buy the same brand of refrigerator as that of television whereas; 69 per cent of the urban consumers preferred the same (Table \hyperref[tab_6]{R} 2). This reveals that both rural and urban consumers had preference to buy the same brand of television as that of refrigerator or viceversa. There had been significant differences between rural and urban consumers as regards to the one and two or more number of refrigerators in a household (X 3). Eighty six per cent of the rural households had only one refrigerator whereas; urban households with only one refrigerator had been found to be 77 per cent. On the other side 23 per cent of the urban households had two or more refrigerators whereas; only 14 per cent of the rural households had the same number of refrigerators (Table \hyperref[tab_7]{R 3}). This implies that majority of both rural and urban households had only one refrigerator. Table R 4 reveals that there had been no significant differences between rural and urban consumers as regards to the duration of planning before buying a refrigerator (X 4). Maximum numbers of consumers have planned for few weeks before the buying of a refrigerator. The structure matrix reveals X 2 as the most discriminating variable followed by X 3 and X 1. The classification results reveal the correct classification of 64.5 per cent of original as well as cross-validated groups (Table \hyperref[tab_6]{R}  had been no significant difference between rural and urban consumers of both the income groups ('upto Rs.2.5 lakh' and '>Rs.2.5 lakh') in terms of timing of purchase. Majority of the consumers of these groups had bought the refrigerator at the time of need. The significant value of chi square indicates an association of habitat with income in terms of their timing of purchase (Table \hyperref[tab_7]{R 6}). There had been significant differences between rural and urban consumers of income group 'upto Rs. 1.5 lakh' as regards to the buying of number of refrigerators. Eighty one per cent of the urban and 93 per cent of the rural consumers had only one refrigerator. Rest of the consumers had two or more refrigerators. No such difference had been observed for other income groups. Majority of the of all these income groups belonging to both rural and urban residents had only one refrigerator. The higher and significant value of chi square in case of refrigerator indicates an association of consumer groups (rural and urban) with their different income groups. However in case of two or more refrigerators, the low and non-significant value of chi square indicates that income had the relation with the possession of number of refrigerators among habitant groups (Table  {\ref R 7)}. There had been no significant difference between rural and urban consumers in any of the select income group as regards to duration of planning before buying the refrigerators. Maximum number of consumers of both the consumer groups belonging to all the income groups had planned for few weeks before the buying of a refrigerator. The chi square had been found significant for all three durations indicating an association between the habitat (rural and urban) and income in terms of their duration of planning before buying a refrigerator (Table \hyperref[tab_7]{R 8}).There had been no significant differences between rural and urban consumers of any of the select family size as regards to the number of refrigerators per household. Majority of the consumers of all the groups of select family sizes belonging to both rural and urban residents had only one refrigerator. The chi square had not been found significant for any of the number of refrigerators per household, indicating that the family size had no relation with the possession of number of refrigerators among habitant groups (Table \hyperref[tab_6]{R}  Table A 1 reveals that there had been significant differences between rural and urban consumers as regards to timing of buying an automobile in terms of buying at the time of need and on special occasions (X 1). Seventy nine per cent of the urban consumer s and 90 per cent of the rural consumers had preferred to buy their automobile at the time of need whereas; 15 per cent of the urban consumers and 8 per cent of the rural consumers had preferred to buy on special occasions. A 2 reveals that there had been significant differences between rural and urban consumers as regards to number of motorcycle only, scooter plus car, and scooter plus motorcycle plus car (X 3). Twenty six per cent of rural consumers and only 8 per cent of urban consumers had motorcycle only. Twenty three per cent of urban consumers and 14 per cent of rural consumers had scooter plus car. Twenty two per cent of urban consumers and 12 per cent of rural consumers had scooter plus motorcycle plus car. There had been significant differences between rural and urban consumers as regards to the possession of scooters, motorcycles and cars. Eighty two per cent of the urban and 62 per cent of the rural consumers had scooters whereas; 48 per cent of the urban consumers and 58 per cent of the rural consumers had motorcycles. Fifty four per cent of the urban consumers and 39 per cent of the rural consumers had cars (Table \hyperref[tab_8]{A} 2.1).There had been no significant differences between rural and urban consumers as regards to the duration of planning before buying an automobile (X 4). Maximum numbers of consumers had planned for months before the buying of an automobile (Table \hyperref[tab_8]{A} 3).\par
The structure matrix reveals X 3 as the most discriminating variable followed by X 1. The classification results reveal the correct classification of only 57.7 per cent of original and 57.2 per cent of crossvalidated groups (Table A 4).  
\section[{April 2011}]{April 2011}\par
There had been significant differences between rural and urban consumers of income group 'upto Rs. 2.5 lakh' in terms of timing of purchase. Eighty eight per cent of the rural consumers and 68 per cent of urban consumers had preferred to buy an automobile at the time of need whereas; 32 per cent of urban consumers and only 12 per cent of rural consumers had preferred to buy at on special occasions / festivals.No significant differences had been observed between rural and urban consumers of income group '> Rs. 2.5 lakh' in terms of timing of purchase. Majority of the both rural and urban consumers belonging to the select income groups had bought an automobile at the time of need. The significant value of chi square indicates that the habitant groups (rural and urban) were dependent on their income levels in terms of their purchase at the time of need. The low and non-significant value of chi square indicates that the income had no relation with the buying of habitant groups on special occasion / festival (Table \hyperref[tab_14]{A 5}).There had been no significant difference between rural and urban consumers in any of the select income group as regards to duration of planning before buying the automobiles. Maximum number of consumers of both the consumer groups belonging to all the income groups had planned for few months before the buying of an automobile. The chi square had been found low and non-significant for all other durations except planning few months before buying, where it had been found significant. This indicates that the income had no relation with habitat for these durations (except few months) of planning before buying an automobile (Table \hyperref[tab_14]{A 6}). In the income group of 'upto Rs. 2.5 lakh', no significant differences had been observed in terms of possession of scooter only, scooter plus motorcycle, motorcycle plus car, and scooter plus motorcycle plus car. However significant differences had been observed in the possession of motorcycle only and motorcycle plus car. Thirty four per cent of the rural consumers and only 11 per cent of the urban consumers had the possession of motorcycle only. In this income group, 19 per cent of urban consumers and only 8 per cent of the rural consumers had scooter as well as car. However no such differences had been observed in the income group of '>Rs. 2.5 lakh'. The chi square had been found significant only in case of 'scooter only', revealing no relation of income with habitat for the possession of all other combinations of automobiles (Table \hyperref[tab_8]{A}  There had been significant differences between rural and urban consumers belonging to income group 'upto Rs. 2.5 lakh' as regards to the type of vehicles among households. Sixty five per cent of the urban households and 79 per cent of the rural households had only twowheelers. On the other side, 35 per cent of the urban households and 21 per cent of the rural households had both two-wheelers as well as cars. In the income group of '>Rs. 2.5 lakh', no significant differences had been found between rural and urban consumers in terms of types of vehicles. Seventy four per cent of the urban consumers and 71 per cent of the rural consumers had both 'two-wheelers' as well as 'cars'. The value of chi square had been found significant in case of possessions of 'two-wheelers only' indicating an association of habitat with income. In case of possession of both the two-wheelers and cars, the value of chi square had been observed non-significant indicating no relation of income with habitat for such possessions of automobiles (  
\section[{April 2011}]{April 2011}\par
In both the income groups ('upto Rs. 2.5 lakh' and '>Rs.\par
2.5 lakh'), there had been significant differences between rural and urban consumers as regards to the possession of scooters. In the income group of 'upto Rs. 2.5 lakh', 83 per cent of urban consumers and in the income group of '>Rs. 2.5 lakh', 81 per cent of the urban consumers had the possession of scooters. On the other side, 58 per cent and 68 per cent of the rural consumers belonging to these income groups respectively had the possession of scooters. In the income group of 'upto Rs. 2.5 lakh', there had been the significant differences between rural and urban consumers as regards to the possession of cars. Thirty five per cent of urban consumers and 21 per cent of rural consumers of this income group had cars. In the income group of '> Rs. 2.5 lakh', there had been the significant differences between rural and urban consumers as regards to the possession of motorcycles. Fifty one per cent of the urban consumers and 68 per cent of the rural consumers had been found using motorcycles. The chi square had been found significant for the possession of scooters and motorcycles, indicating an association of habitat with income. In case of possession of cars, the chi square had been found non-significant indicating independence of habitat of income (Table \hyperref[tab_8]{A}  In case of family size of 'upto 4', there had been significant differences between rural and urban consumers in terms of their possessions -motorcycle only, scooter plus car only, and scooter plus motorcycle plus car. Thirty per cent of the rural households and 11 per cent of the urban households of this family size had motorcycles only. Twenty four per cent of urban households and 8 per cent of rural households had scooter plus car. Twenty five per cent of urban households and 8 per cent of rural households had scooters plus motorcycles plus cars. In the family size of '>4', there had been significant differences between rural and urban consumers in terms of the possessions of motorcycles only. Twenty three per cent of the rural consumers and 6 per cent of the urban consumers had only motorcycles. The chi square had been found nonsignificant in the cases of possessions of scooter only, motorcycle only, scooter plus motorcycle, and motorcycle plus car. This shows no relation of family size with habitat for these possessions of vehicles. The chi square had been significant in the cases of possessions of scooter plus car, and scooter plus motorcycle plus car. This shows an association of habitat with income in these possessions (Table \hyperref[tab_14]{A 8}). There had been significant differences between rural and urban consumers of family size 'upto 4' members as regards to the types of automobiles among households.\par
Thirty nine per cent of the urban consumers and seventy per cent of rural consumers of this family size had only two-wheelers. On the other side sixty one per cent of the urban consumers and 30 per cent of the rural consumers had both cars and twowheelers. In case of family size '>4' members, no significant difference had been seen between rural and urban consumers. The chi square had not been found significant for the possession of two-wheelers only, indicating no relation of family size with habitat for these possessions (rural and urban). However, chi square had been found significant for the possession of both twowheelers and cars indicating an association of habitat with their family size (Table A 8.1). 
\section[{VI.}]{VI.} 
\section[{DISCUSSION AND CONCLUSIONS}]{DISCUSSION AND CONCLUSIONS}\par
Overall there have been moderate differences for television and refrigerators and low differences for automobiles between rural and urban consumers in terms of timing of purchase, buying the same brand of other durable, number of items, and duration of planning before buying. A large majority of rural and urban consumers have a tendency to buy an item in case of need. There are differences between rural and urban consumers in terms of buying of a refrigerator festive season and on special occasion. In the former case, the urban consumers and in the later case, the rural consumers have the greater tendencies than their other counterparts. Similarly, there are differences between rural and urban consumers in terms of buying of an automobile in case of need and on special occasion. In the former case, the rural consumers while in the later case, the urban consumers have greater tendencies than their other counterparts.The differences exist between rural and urban consumers of income group 'upto Rs. 2.5 lakh only' in terms of timing of purchase of an automobile. In case of need, rural consumers whereas; on festive or special occasions the urban consumers have greater tendencies to buy as compared to their counterparts. This is in conformity to the findings of Shivakumar and Arun (2002) that rural consumers have a tendency to buy when necessity is felt rather than waiting for a festive season. Both rural and urban consumers have a tendency to buy the same brand of television as that of refrigerator or vice versa. Such tendency is greater among rural consumers than their urban counterparts. This is so because urban consumers have relatively greater tendency to change brands for the sake of variety and novelty as compared to their rural counterparts. There are similar trends among the rural as well as urban consumers in terms of buying the number of televisions and refrigerators. The rural households exceed urban households in terms of possession of single television or refrigerator. On the other side, urban households exceed rural households in terms of possession of two or more televisions or refrigerators. This is probably due to the income disparities between rural and urban consumers. However large majority of both rural and urban households have one television or refrigerator per household. But in the income group of 'Rs. >3.5 lakh', maximum number of urban households have two or more television sets per household. There are differences between rural and urban consumers in the income group of 'upto Rs. 1.5 lakh' in terms of possession of number of televisions and refrigerators per household. Rural households exceed urban households in case of one item (television and refrigerator) per household whereas; urban households exceed rural households in case of two or more items per household. The differences between rural and urban consumers also exist in the income group of 'upto Rs. 2.5 lakh' in terms of possession of 'motorcycle only' and 'scooter plus car' per household. In the former case, rural households and in the later case, the urban households have greater possessions as compared to their counterparts. Urban households exceed rural households among all the income groups in terms of possession of scooter. The rural households of the income group '>Rs.2.5 lakh' and the urban households of income group 'upto Rs. 2.5 lakh' exceed their counterparts in terms of possessions of motorcycles and cars respectively. The differences further exist in the income group of 'upto Rs. 2.5 lakh' and family size of 'up to 4 members' in which rural households exceed urban counterparts in the possession of 'two-wheeler only' whereas; the urban households exceed rural households in terms of possession of car plus twowheeler. This is probably because of income disparities between rural and urban consumers. In the family size of 'upto 4 members', the differences exist between rural and urban consumers in terms of possession of 'motorcycles only', 'scooters plus cars', and 'scooters plus motorcycle and car'. In the first case, the rural households exceed whereas; in the later two cases, the urban households exceed their counterparts. In the family size of '>4 members', the differences exist for possession of 'motorcycle only', in which rural households exceed the urban households. The differences exist between rural and urban consumers in terms of possessions of scooters, motorcycles, and cars. Urban consumers have greater tendency to buy scooters than rural consumers. This is so because that the urban women and urban student go to their job place or educational institution independently and urban woman and urban girl student prefer to buy scooter. On the other side, the rural households have greater tendency to buy motorcycles than urban consumers. This is probably due to bumpy roads in the rural areas and the better fuel efficiency of the motorcycles as compared to scooters. The urban households have more number of cars than their rural counterparts. The urban households exceed rural ones in terms of ownership of 'scooter plus cars' and 'scooter plus motorcycle plus car' per household'. This is probably due to income disparities between rural and urban groups. In case of ownership of televisions among both the select family sizes; maximum numbers of rural households have one television. Maximum numbers of urban households have two or more television sets in the family size of 'upto 4 members'. However in the family size of '>4 members', maximum numbers of urban households have one television. In terms of ownership of refrigerators, both rural and urban households have one refrigerator in maximum number among both the select family sizes. Maximum number of both rural and urban households plan few days before the buying of television, few weeks before the buying of a refrigerator and few months before the buying of an automobile. However in the income group of 'Rs. >3.5 lakh', maximum number of rural households have a propensity to plan few days before the buying of a refrigerator. The differences exist between rural and urban consumers of income group '> Rs. 1.5 lakh to Rs. 2.5 lakh' only in terms of buying a television. Urban consumers exceed rural consumers and rural consumers exceed urban in terms of duration of planning of days and weeks respectively buying a television. No difference exists among the different income groups of rural and urban consumers as regards to duration of planning before buying an automobile.\par
Habitat (rural or urban) has a relation with income for the timing of buying a television, refrigerator, and automobile except in case of buying of an automobile on festive / special occasion, where the income had no relation with habitat. An association has been revealed between habitat and income, and habitat and family size in terms of numbers of televisions per household. However in case of possessions of refrigerators, select habitant groups reveal no association with the family size of the household. The possessions of two or more refrigerators also reveal no association between habitat and income. There is a relation between habitat and income in terms of duration of planning for different time periods before the buying of a television and refrigerator. The habitat also reveals association with income in terms of planning for months before buying an automobile. No association has been observed between habitat and income in case of planning for few days, few weeks and years before buying an automobile. The habitat has no relation with income in the possessions of 'motorcycles only', 'scooter plus motorcycle', 'scooter plus car', 'motorcycle plus car', 'scooter plus motorcycle plus car', 'car', and 'car plus two-wheeler'. The habitat has been found associated with income only in terms of possessions of 'scooters only', 'scooters', 'motorcycles', and 'twowheelers only'. The habitat has no relation with family size of the household in the possessions of 'scooter only', 'motorcycle only', 'scooter plus motorcycle', 'motorcycle plus car', and 'two-wheeler only'. The habitat bears an association with family size only in terms of possessions of 'scooter plus car', 'scooter plus motorcycle plus car', and 'car plus two-wheeler'. 
\section[{VII.}]{VII.} 
\section[{MANAGERIAL IMPLICATIONS}]{MANAGERIAL IMPLICATIONS}\par
Since both rural and urban consumers have tendencies to buy the same brand of refrigerator as that of television; therefore, the companies can offer combo offers of television and refrigerators to both these types of consumers with greater emphasis on rural consumers. These may increase the one time expenditure of rural consumers who may find difficult to buy this offer due to income constraints; therefore, financing facilities at reasonable rates may also be provided in support of the same. Though both rural and urban consumers have the tendency to buy the same brand of television as that of refrigerator or vice versa, yet rural households have greater tendency than urban households. It is a good opportunity for the organizations manufacturing both these products. Such companies must keep track of these consumers by keeping their data base of those buy any of these two products. Both rural and urban consumers take long periods to plan before buying a high value product such as an automobile. Therefore, the marketers of such products must make rigorous follow up of such potential households through sustained communications.\begin{figure}[htbp]
\noindent\textbf{T} \par 
\begin{longtable}{P{0.37909407665505224\textwidth}P{0.011846689895470382\textwidth}P{0.13031358885017422\textwidth}P{0.06515679442508711\textwidth}P{0.05627177700348432\textwidth}P{0.2073170731707317\textwidth}}
\tabcellsep \tabcellsep \multicolumn{4}{l}{1 : Timing of Purchase}\\
Timing\tabcellsep of\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
Purchase\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Need\tabcellsep \tabcellsep 77\tabcellsep 78\tabcellsep -01\tabcellsep 0.9354\\
Festive season\tabcellsep \tabcellsep 17\tabcellsep 14\tabcellsep 03\tabcellsep 0.4593\\
Special Occasion\tabcellsep \tabcellsep 06\tabcellsep 08\tabcellsep -02\tabcellsep 0.3565\\
\multicolumn{6}{l}{Table T 2 : Same Brand as that of Refrigerator}\\
\multicolumn{2}{l}{Same Brand as}\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
that\tabcellsep of\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Refrigerator\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Yes\tabcellsep \tabcellsep 69\tabcellsep 84\tabcellsep -15\tabcellsep 0.0006\\
No\tabcellsep \tabcellsep 31\tabcellsep 16\tabcellsep 15\tabcellsep 0.0006\end{longtable} \par
 
\caption{\label{tab_0}Table T}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{T} \par 
\begin{longtable}{P{0.009219088937093275\textwidth}P{0.264587852494577\textwidth}P{0.16594360086767895\textwidth}P{0.04886117136659436\textwidth}P{0.061767895878524944\textwidth}P{0.07836225596529284\textwidth}P{0.10048806941431669\textwidth}P{0.1106290672451193\textwidth}P{0.010140997830802603\textwidth}}
\tabcellsep \multicolumn{6}{l}{Table T 6 : Timing of Purchase among Income Groups.}\\
\tabcellsep \multicolumn{3}{l}{Income Groups Urban/Rural}\tabcellsep \tabcellsep \tabcellsep Timing of Purchase\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Need}\tabcellsep Festival/Special\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Occasion\\
\tabcellsep \tabcellsep U (\%)\tabcellsep \tabcellsep \multicolumn{2}{l}{72}\tabcellsep 28\\
\tabcellsep Upto 2.5 lakh\tabcellsep R (\%)\tabcellsep \tabcellsep \multicolumn{2}{l}{70}\tabcellsep 30\\
\tabcellsep \tabcellsep U-R\tabcellsep \tabcellsep \multicolumn{2}{l}{02}\tabcellsep -02\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep \multicolumn{2}{l}{0.7391}\tabcellsep 0.7391\\
\tabcellsep \tabcellsep U (\%)\tabcellsep \tabcellsep \multicolumn{2}{l}{81}\tabcellsep 19\\
\tabcellsep >2.5 lakh\tabcellsep R (\%)\tabcellsep \tabcellsep \multicolumn{2}{l}{90}\tabcellsep 10\\
\tabcellsep \tabcellsep U-R\tabcellsep \tabcellsep \multicolumn{2}{l}{-09}\tabcellsep 09\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep \multicolumn{2}{l}{0.0932}\tabcellsep 0.0932\\
\tabcellsep \multicolumn{2}{l}{Chi Square (df=1)}\tabcellsep \tabcellsep \multicolumn{2}{l}{6.37}\tabcellsep 8.78\\
\tabcellsep \multicolumn{2}{l}{p value (chi square)}\tabcellsep \tabcellsep \multicolumn{2}{l}{0.0159}\tabcellsep 0.003\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{3 : Number of Televisions}\\
\tabcellsep Number\tabcellsep \tabcellsep U (\%)\tabcellsep \multicolumn{2}{l}{R (\%)}\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep 1\tabcellsep \tabcellsep 53\tabcellsep 68\tabcellsep \tabcellsep -15\tabcellsep 0.0012\\
\tabcellsep 2\tabcellsep \tabcellsep 39\tabcellsep 25\tabcellsep \tabcellsep 14\tabcellsep 0.0022\\
\tabcellsep 3\tabcellsep \tabcellsep 06\tabcellsep 04\tabcellsep \tabcellsep 02\tabcellsep 0.3416\\
\tabcellsep 4\tabcellsep \tabcellsep 02\tabcellsep 03\tabcellsep \tabcellsep -01\tabcellsep 0.7787\\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Table T 4 : Duration of Planning before Buying}\\
\tabcellsep \multicolumn{2}{l}{Planning before}\tabcellsep U (\%)\tabcellsep \multicolumn{2}{l}{R (\%)}\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep Buying\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Few Days}\tabcellsep 48\tabcellsep 40\tabcellsep \tabcellsep 08\tabcellsep 0.1048\\
\tabcellsep Weeks\tabcellsep \tabcellsep 31\tabcellsep 36\tabcellsep \tabcellsep -05\tabcellsep 0.251\\
\tabcellsep Months\tabcellsep \tabcellsep 17\tabcellsep 18\tabcellsep \tabcellsep -01\tabcellsep 0.7459\\
\tabcellsep Years\tabcellsep \tabcellsep 04\tabcellsep 06\tabcellsep \tabcellsep -02\tabcellsep 0.5235\\
\tabcellsep \multicolumn{6}{l}{Table T 5: Buying of Television (Discriminant Analysis)}\\
\tabcellsep \multicolumn{3}{l}{Variables Standardized}\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Structure Matrix\\
\tabcellsep \tabcellsep Canonical\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Canonical Discriminant}\\
S. No.\tabcellsep \tabcellsep \multicolumn{3}{l}{Discriminant Function}\tabcellsep \multicolumn{2}{l}{Function Coefficients}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep \tabcellsep \\
1\tabcellsep X 1\tabcellsep -0.792\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{-1.333}\tabcellsep X 2\tabcellsep 0.351\\
2\tabcellsep X 2\tabcellsep 1.157\tabcellsep \tabcellsep \tabcellsep 2.759\tabcellsep X 3\tabcellsep 0.241\\
3\tabcellsep X 3\tabcellsep 1.416\tabcellsep \tabcellsep \tabcellsep 1.996\tabcellsep X 4\tabcellsep -0.142\\
4\tabcellsep X 4\tabcellsep -1.581\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{-1.780}\tabcellsep X 1\tabcellsep -0.034\\
\tabcellsep Constant\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{-1.400}\end{longtable} \par
 
\caption{\label{tab_1}Table T}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.3151483050847458\textwidth}P{0.2521186440677966\textwidth}P{0.1548728813559322\textwidth}P{0.1278601694915254\textwidth}}
\multicolumn{4}{l}{A Comparison Of Rural And Urban Buying Of Consumer Durables}\\
\multicolumn{2}{l}{Income Groups Urban/Rural}\tabcellsep \multicolumn{2}{l}{Number of Televisions}\\
\tabcellsep \tabcellsep One\tabcellsep Two or more\\
\tabcellsep U (\%)\tabcellsep 56\tabcellsep 44\\
Upto 1.5 lakh\tabcellsep R (\%)\tabcellsep 76\tabcellsep 24\\
\tabcellsep U-R\tabcellsep -20\tabcellsep 20\\
\tabcellsep p value (two tailed)\tabcellsep 0.033\tabcellsep 0.033\\
\tabcellsep U (\%)\tabcellsep 54\tabcellsep 46\\
>1.5 to 2.5 lakh\tabcellsep R (\%)\tabcellsep 62\tabcellsep 38\\
\tabcellsep U-R\tabcellsep -08\tabcellsep 08\\
\tabcellsep p value (two tailed)\tabcellsep 0.4111\tabcellsep 0.4111\\
\tabcellsep U (\%)\tabcellsep 66\tabcellsep 34\\
>2.5 to 3.5 lakh\tabcellsep R (\%)\tabcellsep 62\tabcellsep 38\\
\tabcellsep U-R\tabcellsep 04\tabcellsep -04\\
\tabcellsep p value (two tailed)\tabcellsep 0.6701\tabcellsep 0.6701\\
\tabcellsep U (\%)\tabcellsep 43\tabcellsep 57\\
>3.5 lakh\tabcellsep R (\%)\tabcellsep 61\tabcellsep 39\\
\tabcellsep U-R\tabcellsep -18\tabcellsep 18\\
\tabcellsep p value (two tailed)\tabcellsep 0.0914\tabcellsep 0.0914\\
Chi Square (df=3)\tabcellsep \tabcellsep 31.64\tabcellsep 18.40\\
p value (chi square)\tabcellsep \tabcellsep <0.0001\tabcellsep <0.0001\end{longtable} \par
  {\small\itshape [Note: Majority of the consumers of all these income groups belonging to both rural and urban residents had only one television set except in case of urban consumers belonging to income group of '>Rs. 3.5 lakh', where 57 per cent of the consumers had two or more television sets. The high and significant value of chi square indicates an association of the habitat (rural and urban) with income in terms of buying the number of television sets (TableT 7).Table T 7: Number of Televisions among Different Income Groups. ©2011 Global Journals Inc. (US)]} 
\caption{\label{tab_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{T} \par 
\begin{longtable}{P{0.19799196787148596\textwidth}P{0.2389558232931727\textwidth}P{0.1758032128514056\textwidth}P{0.11435742971887551\textwidth}P{0.12289156626506023\textwidth}}
\multicolumn{2}{l}{Income Groups Urban/Rural}\tabcellsep \multicolumn{3}{l}{Duration of Planning before Buying}\\
\tabcellsep \tabcellsep Few Days\tabcellsep Weeks\tabcellsep Months/Years\\
\tabcellsep U (\%)\tabcellsep 38\tabcellsep 28\tabcellsep 34\\
Upto 1.5 lakh\tabcellsep R (\%)\tabcellsep 47\tabcellsep 33\tabcellsep 20\\
\tabcellsep U-R\tabcellsep -09\tabcellsep -05\tabcellsep 14\\
\tabcellsep p value (two tailed)\tabcellsep 0.3649\tabcellsep 0.6376\tabcellsep 0.1185\\
\tabcellsep U (\%)\tabcellsep 38\tabcellsep 34\tabcellsep 28\\
>1.5 to 2.5 lakh\tabcellsep R (\%)\tabcellsep 12\tabcellsep 67\tabcellsep 21\\
\tabcellsep U-R\tabcellsep 26\tabcellsep -33\tabcellsep 07\\
\tabcellsep p value (two tailed)\tabcellsep 0.0028\tabcellsep 0.0009\tabcellsep 0.4666\\
\tabcellsep U (\%)\tabcellsep 52\tabcellsep 33\tabcellsep 15\\
>2.5 to 3.5 lakh\tabcellsep R (\%)\tabcellsep 38\tabcellsep 31\tabcellsep 31\\
\tabcellsep U-R\tabcellsep 14\tabcellsep 02\tabcellsep -16\\
\tabcellsep p value (two tailed)\tabcellsep 0.2452\tabcellsep 0.8267\tabcellsep 0.1118\\
\tabcellsep U (\%)\tabcellsep 59\tabcellsep 28\tabcellsep 13\\
>3.5 lakh\tabcellsep R (\%)\tabcellsep 58\tabcellsep 16\tabcellsep 26\\
\tabcellsep U-R\tabcellsep 01\tabcellsep 12\tabcellsep -13\\
\tabcellsep p value (two tailed)\tabcellsep 0.8942\tabcellsep 0.1848\tabcellsep 0.0835\\
Chi Square (df=3)\tabcellsep \tabcellsep 40.64\tabcellsep 15.08\tabcellsep 8.39\\
p value (chi square)\tabcellsep \tabcellsep <0.0001\tabcellsep <0.0001\tabcellsep 0.0038\end{longtable} \par
 
\caption{\label{tab_3}Table T 8}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{T} \par 
\begin{longtable}{P{0.16413793103448276\textwidth}P{0.23741379310344826\textwidth}P{0.12310344827586207\textwidth}P{0.17879310344827587\textwidth}P{0.14655172413793105\textwidth}}
Family Size\tabcellsep Urban/Rural\tabcellsep \tabcellsep \multicolumn{2}{l}{Number of Televisions}\\
\tabcellsep \tabcellsep One\tabcellsep Two\tabcellsep Three or more\\
\tabcellsep U (\%)\tabcellsep 49\tabcellsep 39\tabcellsep 12\\
Upto 4\tabcellsep R (\%)\tabcellsep 83\tabcellsep 10\tabcellsep 07\\
\tabcellsep U-R\tabcellsep -34\tabcellsep 29\tabcellsep 05\\
\tabcellsep p value (two tailed)\tabcellsep <0.0002\tabcellsep <0.0002\tabcellsep 0.3246\\
\tabcellsep U (\%)\tabcellsep 56\tabcellsep 40\tabcellsep 04\\
>4\tabcellsep R (\%)\tabcellsep 60\tabcellsep 34\tabcellsep 06\\
\tabcellsep U-R\tabcellsep -04\tabcellsep 06\tabcellsep -02\\
\tabcellsep p value (two tailed)\tabcellsep 0.5419\tabcellsep 0.3385\tabcellsep 0.6185\\
Chi Square (df=1)\tabcellsep \tabcellsep 36.03\tabcellsep 19.28\tabcellsep 17.46\\
\multicolumn{2}{l}{p value (chi square)}\tabcellsep <0.0001\tabcellsep <0.0001\tabcellsep <0.0001\end{longtable} \par
 
\caption{\label{tab_4}Table T 9}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.009725400457665904\textwidth}P{0.2324370709382151\textwidth}P{0.18672768878718535\textwidth}P{0.034038901601830665\textwidth}P{0.04279176201372997\textwidth}P{0.12448512585812356\textwidth}P{0.04668192219679634\textwidth}P{0.1731121281464531\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Table R 1 : Timing of Purchase.}\\
\tabcellsep \multicolumn{2}{l}{Timing}\tabcellsep of\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep \multicolumn{2}{l}{Purchase}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Need\tabcellsep \tabcellsep \tabcellsep 81\tabcellsep 79\tabcellsep 02\tabcellsep 0.6789\\
\tabcellsep \multicolumn{2}{l}{Festive season}\tabcellsep \tabcellsep 16\tabcellsep 10\tabcellsep 06\tabcellsep 0.0488\\
\tabcellsep \multicolumn{2}{l}{Special Occasion}\tabcellsep \tabcellsep 03\tabcellsep 11\tabcellsep -08\tabcellsep 0.0017\\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Table R 2 : Same Brand as that of Television.}\\
\tabcellsep \multicolumn{3}{l}{Same Brand as}\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep that\tabcellsep \tabcellsep of\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Refrigerator}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Yes\tabcellsep \tabcellsep \tabcellsep 69\tabcellsep 84\tabcellsep -15\tabcellsep 0.0006\\
\tabcellsep No\tabcellsep \tabcellsep \tabcellsep 31\tabcellsep 16\tabcellsep 15\tabcellsep 0.0006\\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Table R 3 : Number of Refrigerators.}\\
\tabcellsep \multicolumn{2}{l}{Number}\tabcellsep \tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep 1\tabcellsep \tabcellsep \tabcellsep 77\tabcellsep 86\tabcellsep -09\tabcellsep 0.0184\\
\tabcellsep \multicolumn{2}{l}{2 or more}\tabcellsep \tabcellsep 23\tabcellsep 14\tabcellsep 09\tabcellsep 0.0184\\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Table R 4 : Duration of Planning before Buying.}\\
\tabcellsep \multicolumn{3}{l}{Planning before}\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep \multicolumn{2}{l}{Buying}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Few Days}\tabcellsep \tabcellsep 35\tabcellsep 31\tabcellsep 04\tabcellsep 0.3457\\
\tabcellsep Weeks\tabcellsep \tabcellsep \tabcellsep 42\tabcellsep 43\tabcellsep -01\tabcellsep 0.8634\\
\tabcellsep \multicolumn{2}{l}{Months}\tabcellsep \tabcellsep 23\tabcellsep 22\tabcellsep 01\tabcellsep 0.4032\\
\tabcellsep \multicolumn{6}{l}{Table R 5 : Buying of Refrigerator (Discriminant Analysis).}\\
\tabcellsep \multicolumn{3}{l}{Variables Standardized}\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Structure Matrix\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Canonical}\tabcellsep \tabcellsep \multicolumn{2}{l}{Canonical}\\
S. No.\tabcellsep \tabcellsep \multicolumn{3}{l}{Discriminant Function}\tabcellsep \multicolumn{2}{l}{Discriminant Function}\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\\
1\tabcellsep X 1\tabcellsep -1.440\tabcellsep \tabcellsep \tabcellsep -2.427\tabcellsep X 2\tabcellsep 0.394\\
2\tabcellsep X 2\tabcellsep 1.143\tabcellsep \tabcellsep \tabcellsep 2.725\tabcellsep X 3\tabcellsep 0.247\\
3\tabcellsep X 3\tabcellsep 0.858\tabcellsep \tabcellsep \tabcellsep 2.171\tabcellsep X 1\tabcellsep -0.189\\
4\tabcellsep X 4\tabcellsep -0.499\tabcellsep \tabcellsep \tabcellsep -0.611\tabcellsep X 4\tabcellsep -0.131\\
\tabcellsep Constant\tabcellsep \tabcellsep \tabcellsep \tabcellsep -1.653\end{longtable} \par
 
\caption{\label{tab_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{R} \par 
\begin{longtable}{P{0.2857531055900621\textwidth}P{0.2309782608695652\textwidth}P{0.12406832298136646\textwidth}P{0.15970496894409936\textwidth}P{0.04949534161490683\textwidth}}
\multicolumn{2}{l}{Income Groups Urban/Rural}\tabcellsep \tabcellsep \multicolumn{2}{l}{Planning before Buying}\\
\tabcellsep \tabcellsep Few Days\tabcellsep Weeks\tabcellsep Months/Years\\
\tabcellsep U (\%)\tabcellsep 38\tabcellsep 47\tabcellsep 15\\
Upto 1.5 lakh\tabcellsep R (\%)\tabcellsep 33\tabcellsep 48\tabcellsep 19\\
\tabcellsep U-R\tabcellsep 05\tabcellsep -01\tabcellsep -04\\
\tabcellsep p value (two tailed)\tabcellsep 0.6143\tabcellsep 0.9259\tabcellsep 0.6213\\
\tabcellsep U (\%)\tabcellsep 34\tabcellsep 37\tabcellsep 29\\
>1.5 to 2.5 lakh\tabcellsep R (\%)\tabcellsep 31\tabcellsep 38\tabcellsep 31\\
\tabcellsep U-R\tabcellsep 03\tabcellsep -01\tabcellsep -02\\
\tabcellsep p value (two tailed)\tabcellsep 0.755\tabcellsep 0.9029\tabcellsep 0.8493\\
\tabcellsep U (\%)\tabcellsep 30\tabcellsep 55\tabcellsep 15\\
>2.5 to 3.5 lakh\tabcellsep R (\%)\tabcellsep 24\tabcellsep 50\tabcellsep 26\\
\tabcellsep U-R\tabcellsep 06\tabcellsep 05\tabcellsep -11\\
\tabcellsep p value (two tailed)\tabcellsep 0.528\tabcellsep 0.6958\tabcellsep 0.2469\\
\tabcellsep U (\%)\tabcellsep 38\tabcellsep 39\tabcellsep 23\\
>3.5 lakh\tabcellsep R (\%)\tabcellsep 35\tabcellsep 26\tabcellsep 39\\
\tabcellsep U-R\tabcellsep 03\tabcellsep 13\tabcellsep -16\\
\tabcellsep p value (two tailed)\tabcellsep 0.8196\tabcellsep 0.1905\tabcellsep 0.7558\\
Chi Square (df=3)\tabcellsep \tabcellsep 17.03\tabcellsep 27.96\tabcellsep 11.02\\
p value (chi square)\tabcellsep \tabcellsep <0.0001\tabcellsep <0.0001\tabcellsep 0.0009\\
\multicolumn{4}{l}{Table R 6 : Timing of Purchase among Income Groups.}\tabcellsep \\
\multicolumn{2}{l}{Income Groups Urban/Rural}\tabcellsep \multicolumn{2}{l}{Timing of Purchase}\tabcellsep \\
\tabcellsep \tabcellsep Need\tabcellsep \multicolumn{2}{l}{Festival/Special}\\
\tabcellsep \tabcellsep \tabcellsep Occasion\tabcellsep \\
\tabcellsep U (\%)\tabcellsep 79\tabcellsep 21\tabcellsep \\
Upto 2.5 lakh\tabcellsep R (\%)\tabcellsep 72\tabcellsep 28\tabcellsep \\
\tabcellsep U-R\tabcellsep 07\tabcellsep -07\tabcellsep \\
\tabcellsep p value (two tailed)\tabcellsep 0.2236\tabcellsep 0.2236\tabcellsep \\
\tabcellsep U (\%)\tabcellsep 81\tabcellsep 19\tabcellsep \\
>2.5 lakh\tabcellsep R (\%)\tabcellsep 90\tabcellsep 10\tabcellsep \\
\tabcellsep U-R\tabcellsep -09\tabcellsep 09\tabcellsep \\
\tabcellsep p value (two tailed)\tabcellsep 0.0932\tabcellsep 0.0932\tabcellsep \\
Chi Square (df=1)\tabcellsep \tabcellsep 5.18\tabcellsep 11.16\tabcellsep \\
p value (chi square)\tabcellsep \tabcellsep 0.0228\tabcellsep 0.0008\tabcellsep \\
\multicolumn{5}{l}{Table R 7 : Number of Refrigerators among Different Income Groups.}\\
\multicolumn{2}{l}{Income Groups Urban/Rural}\tabcellsep \tabcellsep \multicolumn{2}{l}{Number of Refrigerators}\\
\tabcellsep \tabcellsep One\tabcellsep \multicolumn{2}{l}{Two or more}\\
\tabcellsep U (\%)\tabcellsep 81\tabcellsep 19\tabcellsep \\
Upto 1.5 lakh\tabcellsep R (\%)\tabcellsep 93\tabcellsep 07\tabcellsep \\
\tabcellsep U-R\tabcellsep -12\tabcellsep 12\tabcellsep \\
\tabcellsep p value (two tailed)\tabcellsep 0.0439\tabcellsep 0.0439\tabcellsep \\
\tabcellsep U (\%)\tabcellsep 77\tabcellsep 23\tabcellsep \\
>1.5 to 2.5 lakh\tabcellsep R (\%)\tabcellsep 86\tabcellsep 14\tabcellsep \\
\tabcellsep U-R\tabcellsep -09\tabcellsep 09\tabcellsep \\
\tabcellsep p value (two tailed)\tabcellsep 0.2636\tabcellsep 0.2636\tabcellsep \\
\tabcellsep U (\%)\tabcellsep 82\tabcellsep 18\tabcellsep \\
>2.5 to 3.5 lakh\tabcellsep R (\%)\tabcellsep 81\tabcellsep 19\tabcellsep \\
\tabcellsep U-R\tabcellsep 01\tabcellsep -01\tabcellsep \\
\tabcellsep p value (two tailed)\tabcellsep 0.9235\tabcellsep 0.9235\tabcellsep \\
\tabcellsep U (\%)\tabcellsep 73\tabcellsep 27\tabcellsep \\
>3.5 lakh\tabcellsep R (\%)\tabcellsep 71\tabcellsep 29\tabcellsep \\
\tabcellsep U-R\tabcellsep 02\tabcellsep -02\tabcellsep \\
\tabcellsep p value (two tailed)\tabcellsep 0.8337\tabcellsep 0.8337\tabcellsep \\
Chi Square (df=3)\tabcellsep \tabcellsep 47.57\tabcellsep 4.29\tabcellsep \\
p value (chi square)\tabcellsep \tabcellsep <0.0001\tabcellsep 0.383\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table R 8}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{R} \par 
\begin{longtable}{P{0.24315403422982884\textwidth}P{0.23484107579462102\textwidth}P{0.02493887530562347\textwidth}P{0.10599022004889976\textwidth}P{0.020782396088019562\textwidth}P{0.2202933985330073\textwidth}}
Family Size\tabcellsep \multicolumn{2}{l}{Urban/Rural}\tabcellsep \tabcellsep \tabcellsep Number of Refrigerators\\
\tabcellsep \tabcellsep \tabcellsep One\tabcellsep \tabcellsep Two or more\\
\tabcellsep U (\%)\tabcellsep \tabcellsep 79\tabcellsep \tabcellsep 21\\
Upto 4\tabcellsep R (\%)\tabcellsep \tabcellsep 87\tabcellsep \tabcellsep 13\\
\tabcellsep U-R\tabcellsep \tabcellsep -08\tabcellsep \tabcellsep 08\\
\tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep \multicolumn{2}{l}{0.1855}\tabcellsep 0.1855\\
\tabcellsep U (\%)\tabcellsep \tabcellsep 78\tabcellsep \tabcellsep 22\\
>4\tabcellsep R (\%)\tabcellsep \tabcellsep 85\tabcellsep \tabcellsep 15\\
\tabcellsep U-R\tabcellsep \tabcellsep -07\tabcellsep \tabcellsep 07\\
\tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep \multicolumn{2}{l}{0.1204}\tabcellsep 0.1204\\
Chi Square (df=1)\tabcellsep \tabcellsep \tabcellsep 3.79\tabcellsep \tabcellsep 0.04\\
p value (chi square)\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{0.0516}\tabcellsep 0.8415\\
c) Automobile\tabcellsep \multicolumn{5}{l}{Table A 1: Timing of Purchase.}\\
Timing\tabcellsep of\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
Purchase\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Need\tabcellsep \tabcellsep 79\tabcellsep 90\tabcellsep -11\tabcellsep 0.0085\\
\multicolumn{2}{l}{Festive season}\tabcellsep 06\tabcellsep 02\tabcellsep 04\tabcellsep 0.1394\\
\multicolumn{2}{l}{Special Occasion}\tabcellsep 15\tabcellsep 08\tabcellsep 07\tabcellsep 0.0386\end{longtable} \par
 
\caption{\label{tab_7}Table R 9}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{A} \par 
\begin{longtable}{P{0.38818897637795274\textwidth}P{0.1438976377952756\textwidth}P{0.06358267716535433\textwidth}P{0.06023622047244094\textwidth}P{0.19409448818897637\textwidth}}
\multicolumn{5}{l}{A Comparison Of Rural And Urban Buying Of Consumer Durables}\\
\tabcellsep \multicolumn{4}{l}{2 : Types of Automobiles}\\
Vehicles\tabcellsep U (\%)\tabcellsep R (\%)\tabcellsep U-R\tabcellsep p value (two tailed)\\
S only\tabcellsep 27\tabcellsep 27\tabcellsep 00\tabcellsep 0.9832\\
M only\tabcellsep 08\tabcellsep 26\tabcellsep -18\tabcellsep <0.0002\\
C only\tabcellsep *\tabcellsep *\tabcellsep *\tabcellsep *\\
S+M\tabcellsep 11\tabcellsep 09\tabcellsep 02\tabcellsep 0.4752\\
S+C\tabcellsep 23\tabcellsep 14\tabcellsep 09\tabcellsep 0.0184\\
M+C\tabcellsep 08\tabcellsep 12\tabcellsep -04\tabcellsep 0.1521\\
S+M+C\tabcellsep 22\tabcellsep 12\tabcellsep 10\tabcellsep 0.0101\\
*negligible value\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table A}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{A} \par 
\begin{longtable}{P{0.008453038674033148\textwidth}P{0.29491712707182316\textwidth}P{0.09016574585635359\textwidth}P{0.11364640883977901\textwidth}P{0.024419889502762433\textwidth}P{0.09486187845303867\textwidth}P{0.05353591160220994\textwidth}P{0.14182320441988952\textwidth}P{0.028176795580110495\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{2.1: Types of Automobiles}\\
\tabcellsep \multicolumn{2}{l}{Vehicles}\tabcellsep U (\%)\tabcellsep \multicolumn{2}{l}{R (\%)}\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep S\tabcellsep \tabcellsep 82\tabcellsep 62\tabcellsep \tabcellsep 20\tabcellsep <0.0002\\
\tabcellsep M\tabcellsep \tabcellsep 48\tabcellsep 58\tabcellsep \tabcellsep -10\tabcellsep 0.0343\\
\tabcellsep C\tabcellsep \tabcellsep 54\tabcellsep 39\tabcellsep \tabcellsep 15\tabcellsep 0.0014\\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Table A 3 : Duration of Planning before Buying.}\\
\tabcellsep \multicolumn{2}{l}{Duration of}\tabcellsep U (\%)\tabcellsep \multicolumn{2}{l}{R (\%)}\tabcellsep U-R\tabcellsep p value (two tailed)\\
\tabcellsep \multicolumn{3}{l}{Planning before}\tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Buying}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Few Days}\tabcellsep 21\tabcellsep 22\tabcellsep \tabcellsep -01\tabcellsep 0.8705\\
\tabcellsep Weeks\tabcellsep \tabcellsep 15\tabcellsep 17\tabcellsep \tabcellsep -02\tabcellsep 0.5405\\
\tabcellsep Months\tabcellsep \tabcellsep 49\tabcellsep 49\tabcellsep \tabcellsep 00\tabcellsep 0.9585\\
\tabcellsep Years\tabcellsep \tabcellsep 15\tabcellsep 12\tabcellsep \tabcellsep 03\tabcellsep 0.3565\\
\tabcellsep \multicolumn{6}{l}{Table A 4 : Buying of Automobile (Discriminant Analysis).}\\
\tabcellsep \multicolumn{4}{l}{Variables Standardized Canonical}\tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Canonical\tabcellsep Structure\\
\tabcellsep \tabcellsep \multicolumn{3}{l}{Discriminant Function}\tabcellsep \multicolumn{2}{l}{Discriminant}\tabcellsep Function\tabcellsep Matrix\\
S. No.\tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep \tabcellsep \multicolumn{2}{l}{Coefficients}\\
1\tabcellsep X 1\tabcellsep 0.386\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{0.594}\tabcellsep X 3\tabcellsep 0.562\\
2\tabcellsep X 3\tabcellsep -1.732\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{-1.776}\tabcellsep X 1\tabcellsep 0.490\\
3\tabcellsep X 4\tabcellsep 1.862\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{0.828}\tabcellsep X 4\tabcellsep 0.136\\
\tabcellsep Constant\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{0.685}\\
\tabcellsep \multicolumn{6}{l}{Table A 5 : Purchase Timing among Different Income Groups}\\
\tabcellsep \multicolumn{3}{l}{Income Groups Urban/Rural}\tabcellsep \tabcellsep \tabcellsep Timing of Purchase\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Need}\tabcellsep Festival/Special\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Occasion\\
\tabcellsep \tabcellsep \tabcellsep U (\%)\tabcellsep \tabcellsep 68\tabcellsep 32\\
\tabcellsep \multicolumn{2}{l}{Upto 2.5 lakh}\tabcellsep R (\%)\tabcellsep \tabcellsep 88\tabcellsep 12\\
\tabcellsep \tabcellsep \tabcellsep U-R\tabcellsep \tabcellsep \multicolumn{2}{l}{-20}\tabcellsep 20\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep \multicolumn{2}{l}{0.0002}\tabcellsep 0.0002\\
\tabcellsep \tabcellsep \tabcellsep U (\%)\tabcellsep \tabcellsep 90\tabcellsep 10\\
\tabcellsep >2.5 lakh\tabcellsep \tabcellsep R (\%)\tabcellsep \tabcellsep 91\tabcellsep 09\\
\tabcellsep \tabcellsep \tabcellsep U-R\tabcellsep \tabcellsep \multicolumn{2}{l}{-01}\tabcellsep 01\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep \multicolumn{2}{l}{0.8792}\tabcellsep 0.8792\\
\tabcellsep \multicolumn{2}{l}{Chi Square (df=1)}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{18.93}\tabcellsep 0.13\\
\tabcellsep \multicolumn{2}{l}{p value (chi square)}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{<0.0001}\tabcellsep 0.7184\end{longtable} \par
 
\caption{\label{tab_9}Table A}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{A} \par 
\begin{longtable}{P{0.22972972972972974\textwidth}P{0.1576781326781327\textwidth}P{0.053255528255528255\textwidth}P{0.1367936117936118\textwidth}P{0.06891891891891892\textwidth}P{0.0877149877149877\textwidth}P{0.07518427518427517\textwidth}P{0.040724815724815726\textwidth}}
\multicolumn{2}{l}{Income Groups Urban/Rural}\tabcellsep \tabcellsep \multicolumn{4}{l}{Duration of Planning before Buying}\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Few Days Weeks}\tabcellsep \multicolumn{2}{l}{Months Years}\tabcellsep \\
\tabcellsep U (\%)\tabcellsep \tabcellsep 19\tabcellsep 20\tabcellsep 42\tabcellsep 19\tabcellsep \\
Upto 2.5 lakh\tabcellsep R (\%)\tabcellsep \tabcellsep 19\tabcellsep 22\tabcellsep 46\tabcellsep 13\tabcellsep \\
\tabcellsep U-R\tabcellsep \tabcellsep 00\tabcellsep -02\tabcellsep -04\tabcellsep 06\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep 0.9848\tabcellsep 0.7039\tabcellsep 0.5458\tabcellsep 0.208\tabcellsep \\
\tabcellsep U (\%)\tabcellsep \tabcellsep 23\tabcellsep 10\tabcellsep 56\tabcellsep 11\tabcellsep \\
> 2.5 lakh\tabcellsep R (\%)\tabcellsep \tabcellsep 27\tabcellsep 08\tabcellsep 55\tabcellsep 10\tabcellsep \\
\tabcellsep U-R\tabcellsep \tabcellsep -04\tabcellsep 02\tabcellsep 01\tabcellsep 01\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{p value (two tailed)}\tabcellsep 0.5392\tabcellsep 0.6426\tabcellsep 0.9633\tabcellsep 0.7271\tabcellsep \\
\multicolumn{2}{l}{Chi Square (df=1)}\tabcellsep \tabcellsep 1.65\tabcellsep 3.19\tabcellsep 7.91\tabcellsep 0.71\tabcellsep \\
\multicolumn{2}{l}{p value (chi square)}\tabcellsep \tabcellsep 0.199\tabcellsep 0.0741\tabcellsep 0.0049\tabcellsep 0.3994\tabcellsep \\
\multicolumn{7}{l}{Table A 7 : Types of Automobiles among Different Income Groups.}\tabcellsep \\
Income\tabcellsep Urban/Rural\tabcellsep \multicolumn{2}{l}{Automobiles}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Groups\tabcellsep \tabcellsep S only\tabcellsep M only\tabcellsep S+M\tabcellsep S+C\tabcellsep M+C\tabcellsep S+M+\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep C\\
\tabcellsep U (\%)\tabcellsep 39\tabcellsep 11\tabcellsep 14\tabcellsep 19\tabcellsep 05\tabcellsep 12\\
Upto 2.5 lakh\tabcellsep R (\%)\tabcellsep 38\tabcellsep 34\tabcellsep 08\tabcellsep 08\tabcellsep 08\tabcellsep 04\\
\tabcellsep U-R\tabcellsep 01\tabcellsep -20\tabcellsep 06\tabcellsep 11\tabcellsep -03\tabcellsep 08\\
\tabcellsep p value (two tailed)\tabcellsep 0.8981\tabcellsep \multicolumn{2}{l}{<0.0002 0.0903}\tabcellsep 0.0203\tabcellsep 0.4734\tabcellsep 0.0506\\
\tabcellsep U (\%)\tabcellsep 17\tabcellsep 05\tabcellsep 06\tabcellsep 30\tabcellsep 11\tabcellsep 31\\
> 2.5 lakh\tabcellsep R (\%)\tabcellsep 07\tabcellsep 11\tabcellsep 11\tabcellsep 25\tabcellsep 21\tabcellsep 25\\
\tabcellsep U-R\tabcellsep 10\tabcellsep -06\tabcellsep -05\tabcellsep 05\tabcellsep -10\tabcellsep 06\\
\tabcellsep p value (two tailed)\tabcellsep 0.0564\tabcellsep 0.127\tabcellsep 0.215\tabcellsep 0.4278\tabcellsep 0.0691\tabcellsep 0.3892\\
Chi Square (df=1)\tabcellsep \tabcellsep 8.44\tabcellsep 2.10\tabcellsep 0.85\tabcellsep 0.01\tabcellsep 0.32\tabcellsep 0.01\\
\multicolumn{2}{l}{p value (chi square)}\tabcellsep 0.0037\tabcellsep 0.1473\tabcellsep 0.3566\tabcellsep 0.9203\tabcellsep 0.5716\tabcellsep 0.9203\end{longtable} \par
  {\small\itshape [Note: S= Scooter, M= Motorcycle, and C= Car.A Comparison Of Rural And Urban Buying Of Consumer Durables ©2011 Global Journals Inc. (US) ©2011 Global Journals Inc. (US)]} 
\caption{\label{tab_10}Table A 6}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Global Journal of Management and Business Research Volume XI Issue V Version I\end{longtable} \par
  {\small\itshape [Note: ©2011 Global Journals Inc. (US)]} 
\caption{\label{tab_12}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.15393700787401576\textwidth}P{0.15616797900262466\textwidth}P{0.10931758530183727\textwidth}P{0.0937007874015748\textwidth}P{0.08254593175853019\textwidth}P{0.08254593175853019\textwidth}P{0.0847769028871391\textwidth}P{0.08700787401574803\textwidth}}
\multicolumn{2}{l}{Family Size Urban/Rural}\tabcellsep Vehicles\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep S only\tabcellsep M only\tabcellsep S+M\tabcellsep S+C\tabcellsep M+C\tabcellsep S+M+\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep C\\
\tabcellsep U (\%)\tabcellsep 20\tabcellsep 11\tabcellsep 09\tabcellsep 24\tabcellsep 11\tabcellsep 25\\
Upto 4\tabcellsep R (\%)\tabcellsep 32\tabcellsep 30\tabcellsep 08\tabcellsep 08\tabcellsep 14\tabcellsep 08\\
\tabcellsep U-R\tabcellsep -12\tabcellsep -19\tabcellsep 01\tabcellsep 16\tabcellsep -03\tabcellsep 17\\
\tabcellsep p value (two tailed)\tabcellsep 0.0625\tabcellsep 0.0014\tabcellsep 0.6973\tabcellsep 0.0042\tabcellsep 0.4539\tabcellsep 0.0027\\
\tabcellsep U (\%)\tabcellsep 34\tabcellsep 06\tabcellsep 12\tabcellsep 23\tabcellsep 06\tabcellsep 19\\
> 4\tabcellsep R (\%)\tabcellsep 24\tabcellsep 23\tabcellsep 09\tabcellsep 18\tabcellsep 11\tabcellsep 15\\
\tabcellsep U-R\tabcellsep 10\tabcellsep -17\tabcellsep 03\tabcellsep 05\tabcellsep -05\tabcellsep 04\\
\tabcellsep p value (two tailed)\tabcellsep 0.0843\tabcellsep 0.0002\tabcellsep 0.4485\tabcellsep 0.3396\tabcellsep 0.1621\tabcellsep 0.3778\\
\multicolumn{2}{l}{Chi Square (df=1)}\tabcellsep 1.18\tabcellsep 1.80\tabcellsep 0.24\tabcellsep 6.06\tabcellsep 1.34\tabcellsep 6.05\\
\multicolumn{2}{l}{p value (chi square)}\tabcellsep 0.2774\tabcellsep 0.1797\tabcellsep 0.6242\tabcellsep 0.0138\tabcellsep 0.247\tabcellsep 0.0139\end{longtable} \par
 
\caption{\label{tab_13}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{A} \par 
\begin{longtable}{P{0.1938\textwidth}P{0.2754\textwidth}P{0.2074\textwidth}P{0.17339999999999997\textwidth}}
Family Size\tabcellsep Urban/Rural\tabcellsep Vehicles\tabcellsep \\
\tabcellsep \tabcellsep Two-wheeler only\tabcellsep Car +Two-wheeler\\
\tabcellsep U (\%)\tabcellsep 39\tabcellsep 61\\
Upto 4\tabcellsep R (\%)\tabcellsep 70\tabcellsep 30\\
\tabcellsep U-R\tabcellsep -31\tabcellsep 31\\
\tabcellsep p value (two tailed)\tabcellsep <0.0002\tabcellsep <0.0002\\
\tabcellsep U (\%)\tabcellsep 51\tabcellsep 49\\
> 4\tabcellsep R (\%)\tabcellsep 56\tabcellsep 44\\
\tabcellsep U-R\tabcellsep -05\tabcellsep 05\\
\tabcellsep p value (two tailed)\tabcellsep 0.4654\tabcellsep 0.4654\\
Chi Square (df=1)\tabcellsep \tabcellsep 0.06\tabcellsep 11.3\\
p value (chi square)\tabcellsep \tabcellsep 0.8065\tabcellsep 0.0008\end{longtable} \par
 
\caption{\label{tab_14}Table A 8}\end{figure}
 			\footnote{Global Journal of Management and Business Research Volume XI Issue V Version I ©2011 Global Journals Inc. (US) April} 			\footnote{©2011 Global Journals Inc. (US)} 			\footnote{©2011 Global Journals Inc. (US)April 2011} 		 		\backmatter  			 			 			  				\begin{bibitemlist}{1}
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\end{document}
