Run Linear-Linear and Log-Log Regression

Run linear-linear and log-Log regression models using the data. Determine which regression model you would use. Why?

The most suitable regression model to use is the liner-liner regression model. This is because it is the most suitable model for the relationships between the variables and it is the model that yields an almost straight-line relationship on the graph plots. The reason as to why the log-log regression cannot even be used in this work is because some independent variables like Display and Feature have zero (0) values and it is not possible to compute the log value of 0 (zero).It therefore becomes automatic that the moist suitable model to use is the linear-linear regression model which yield the following graph and correlation coefficients.

Model Summary

Model

R

R Square

Adjusted R. Square

Std. Error of the Estimate

1

.490a

.240

.239

5.4473518E3

a. Predictors: (Constant), Feature, Price, Display

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

Regression

2.721E10

3

9.069E9

.000a

Residual

8.629E10

2.967E7

Total

1.135E11

a. Predictors: (Constant), Feature, Price, Display

b. Dependent Variable: Sales

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

1.923

.055

Price

.041

2.417

.016

Display

.253

13.072

.000

Feature

.315

16.321

.000

a. Dependent Variable: Sales

The graph of the variables and sales levels

(b)

Interpret the regression results from the model you chose. Specifically, discuss the effectiveness of using price promotion, feature and display based on your results. It would be useful to provide some numerical examples.

Price promotion

The effectiveness of using price promotion is noted to be the least since it has the least coefficient (0.041). In regard to distribution, the graph indicates that the price brings in the highest level of sale but for a small/percentage of sales. This means that the use of price promotion as a marketing mix variable does not contribute much sales for of the brands.

Display

The effectiveness of using display as a marketing mix variable is average. It has a coefficient of 0.253. This is in regard to all of the brands.

Feature

The effectiveness of using feature as a marketing mix variable is noted to be the most efficient for all of the brands. This is due to its high level of significance (0.315).This means that it is the variable which should be perfected by all of the brands

(c) Store 4 is the largest store in the market with more than 40% of market share (see "Statistics"). As a brand manager for Coca-Cola, you are not happy about the fact that sales of Coca-Cola lag behind Pepsi, your major competitor, in the store. Can you explain why this happens? Also, can you recommend how to stimulate sales in store 4 by marketing more effectively? It would be useful to provide some numerical examples.

Why Coca-Cola lags behind Pepsi in sales

A review of the statistical variable outcomes reveals that the most important marketing variables in order of important are feature, display and then price. A review of the feature variable of Coca-Cola reveals that it lies below the one for Pepsi by about 0.049. This means that Pepsi has a more significant feature variable than Coca-Cola. Cola-Cola however scores better than Pepsi in the other two variables; display and price.