Case Four – Diligent Consulting Group. Business & Marketing Essay
I already have the Excel regression model data available. I just need the paper. Here are the instructions:
Using Excel, generate regression estimates for the following model:
Annual Amount Spent on Organic Food = α + b1Age + b2AnnualIncome
+ b3Number of People in Household + b4Gender
After you have reviewed the results from the estimation, write a report to your boss that interprets the results that you obtained. Please include the following in your report:
The regression output you generated in Excel.
Your interpretation of the coefficient of determination (r-squared).
Your interpretation of the global test for statistical significance (the F-test).
Your interpretation of the coefficient estimates for all the independent variables.
Your interpretation of the statistical significance of the coefficient estimates for all the independent variables.
The regression equation with estimates substituted into the equation. (Note: Once the estimates are substituted into the regression equation, it should take a form similar to this: y = 10 +2x1 +1x2 +4x3 +0.9x4)
An estimate of “Annual Amount Spent on Organic Food” for the average consumer. (Note: You will need to substitute the averages for all the independent variables into the regression equation for x, the intercept for α, and solve for y.)
A discussion of whether or not the coefficient estimate on the Age variable in this estimation is different than it was in the simple linear regression model from Module 3 Case. Be sure to explain why it did/did not change.
You decide you want to generate an elasticity coefficient, so you log the following variables in Excel: Annual Amount Spent on Organic Food, Annual Income.
Using Excel, generate regression estimates for the following model:
Log(Annual Amount Spent on Organic Food) = α +b1Age + b2Log(AnnualIncome)
+ b3Number of People in Household + b4Gender
Your interpretation of the coefficient estimate for Log(AnnualIncome).
Your interpretation of the coefficient of determination (r-squared) for this new model.
You are a consultant who works for the Diligent Consulting Group. In this Case, you are engaged on a consulting basis by Loving Organic Foods. In order to get a better idea of what might have motivated customers’ buying habits you are asked to analyze the factors that impact organic food expenditures. You performed a simple linear regression analysis in the Module 3 Case. Now, you are adding a layer of complexity to that analysis and including more independent variables in your model.
Case Four – Diligent Consulting Group
Your Name
Subject and Section
Professor’s Name
April 14, 2020
Introduction
This report summarizes some of the socio-demographic factors that could affect the consumer’s behavior when it comes to buying organic foods. Particularly, the factors that would be discussed includes (1) age, (2) annual income, (3) Number of people in the household, and (4) gender. In order to provide an analysis of the relationship between variables, the subsequent sections would discuss the regression statistics that are relevant to the goal.
Regression Output
Figure 1 - Regression Summary
Interpretation of Data
R-Squared
The regression model (Figure 1) acquired during this study, showed an r-square value of 0.689 or 69%. This indicates that there is a significant possibility (69%) that the model used is a good fit to explain the variability of the data around the mean scores obtained for each of the variable. In other words, this means that this regression analysis, can be a good representation of how the Independent Variables (IVs) – such as age, annual income, number of persons in household, and gender – affect the consumers’ purchasing behaviors (Dependent Variables - DVs).
F-Test
The F-Test provides an insight about the equality of the population variances that are used in this model. Specifically, the regression statistics show an F-value of 66.11. This is higher than the F-value needed based on a 95% Confidence Interval, which means that the variance between the different IVs are not equal.
Coefficient Estimates for Independent Variables
Since the coefficient estimates of each of the four independent variables are positive, then it could be seen that each of the variables have a positive relationship with how the affect organic food expenditures. This stems from the fact that a both the means of the IV and the DV tends to increase in a positive coeffi...
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