1. What purpose does regression analysis serve? Give an example of how it might be used in marketing research. How is the strength of regression measures of association determined?

Regression analysis is used as a procedure for predicting the level of magnitude of a (metric) dependent variable based on the levels of multiple independent variables. In terms of regression analysis usage in market research, it can help the researchers to make the market predictions. For example, if evaluated a regression model utilizing information on deals, costs, and limited time exercises, the outcomes from this regression investigation could give an exact response to what might happen to deals if costs somehow managed to increment by 5% and special exercises were to increment by 10%. Such exact answers can help (advertising) supervisors settle on steady choices. Moreover, by giving different situations, for example, figuring the business impacts of cost increments of 5%, 10%, and 15%, supervisors can assess advertising arranges and make showcasing procedures (Smith, 2014).

  1. What is a dummy variable? Give an example using a dummy variable.

A dummy variable is used in regression analysis where a way of representing two groups or dichotomous is used, and nominally scale independent variables by coding one group as 0 and other as 1. As an example of using dummy variable, we can state that if we are using the regression analysis to evaluate the predictors of blood pressure and our unit of analysis the person. There the dependent variable would be the person’s diastolic blood pressure.

  1. Describe the potential problem of collinearity in multiple regression. How might a researcher test for collinearity? If collinearity is a problem, what should the researcher do?

In order to test collinearity, the correlation of independent variables with each other, which can bias estimates of regression coefficients is used. In order to test the collinearity, the simplest way is to examine the matrix showing the correlations between each variable in the analysis. If the collinearity has a problem, the researcher can drop one of the variables from the analysis if two are heavily correlated with each other. In second case, the researcher can combine the correlated variables in some fashion to form a new composite independent variable (McDaniel & Gates, 2013).

  1. A sales manager examined age data, education level, a personality factor that indicated level of introversion/extroversion, and level of sales attained by the company’s 120-person salesforce. The technique used was regression analysis. After analyzing the data, the sales manager said, “It is apparent to me that the higher the level of education and the greater the degree of extroversion a salesperson has, the higher will be an individual’s level of sales. In other words, a good education and being extroverted cause a person to sell more.” Would you agree or disagree with the sales manager’s conclusions? Why?

The statement of the sales manager is quite valid and it is agreeable. Reason behind his thing is that in marketing and to be a good sales person, one needs to be outspoken and expressive rather than being introvert and shy. A person who has the above mentioned skills would undoubtedly be a good sales person.

  1. The factors produced and the results of the factor loadings from factor analysis are mathematical constructs. It is the task of the researcher to make sense out of these factors. The following table lists four factors produced from a study of cable TV viewers. What label would you put on each of these four factors? Why?

These four factors can be labeled as:

  • For factor 1: Cable TV movie repetition
  • For factor 2: Emotions associated with cable TV movies
  • For Factor 3: Religious program on Cable TV
  • For Factor 4: Watching cable TV at home.
  1. The following table shows regression coefficients for two dependent variables. The first dependent variable is willingness to spend money for cable TV. The independent variables are responses to attitudinal statements. The second dependent variable is stated desire never to allow cable TV in their homes. By examining the regression coefficients, what can you say about persons willing to spend money for cable TV and those who will not allow cable TV in their homes?

This table shows the regression coefficients which state that the first person who is willing to spend money for cable TV is a habitual of watching television and he likes to watch movies and other program. For this reason, he can pay more money for cable TV. On the other hand, the second person who would never allow cable TV in home is very extrovert and antisocial. He doesn’t like to go for games and he does not have many choices.

 

 

 

References

McDaniel, C., & Gates, R. (2013). More powerful statistical methods. In Marketing Research Essentials (8th Edition ed., pp. 364-391). Hoboken, New Jersey: John Wiley and Sons, Inc.

Smith, M. (2014). Chapter 7 – Regression Analysis. A Conscise way to market research.