Multiple Regression Analysis Of Consumer Behavior

The aim of marketing is to meet and satisfy target customer’s needs and wants better than competitors. Consumer behavior is the study of how individuals, groups and organizations select, buy, use and dispose of goods, services, ideas or experiences to satisfy their needs and wants. One can analyze the relationship between the variables (factors) of consumer behavior and willingness to purchase. Regression analysis is a statistical technique for estimating the relationship among variables which have reason and result relation. This is proposed to find the relationship between the consumer behavior factors to the consumer willingness to buy a product. The coefficient of determination (R2) is of the variation that occurs in the willingness to

The process generally includes the following components.
1. Inter disciplinary teams that combine substantial knowledge of the process with an understanding of analysis tools such as Multiple Regression that are needed for model development.
2. Ideal data for regression models have independent factors with a wide range and small correlations between independent factors.
3. Theory, experience, and historical knowledge are the foundation for analysis and problem solving.
4. An understanding based on experience that data contain both information and error. Analysis and modeling seek to enhance information and minimize error.
IV. Multiple Regression Model: Let us consider the willingness to buy (Y) as a dependent (regress) factor and factors like Amount of Purchase on product (X1), Discount rate (X2), Age (X3) and Promotion on the product (X4) are independent (predictor) factors.

Therefore the model can be defined as some functions of X’s,
i.e.,
More clearly, Multiple Regression is a procedure for determining the simultaneous linear effect of k-independent factors on a dependent factor. This is done by estimating the coefficients of the linear equations using the principle of least

ii) An estimate of error variance. iii) The components of variability explained by each of the independent factors give the variability by the other factor.

The statistic
Reject , if

Remark: The error mean square is an estimate of the variance , of the random error term , And is the standard error of estimate. This is the variance in Y after the effect of all the independent factors has been removed.

2. Tests on individual Regression coefficients: The‘t’ test is used to check the significance of individual regression coefficients in the multiple linear regression model. Adding a significant factor to a regression model makes the model more effective, while and adding an unimportant factor may take the model worse. The hypothesis statements to tests the significance of particular regression coefficients : V/s
The test statistic for this test is based on the t – distribution, Reject H0, if absolute value of is greater than , and we conclude that the factor has a significant effect and should be included in the multiple regression model.