D& T LTD marketing team needed more information about the effectiveness of their 3 main mode of advertising. To determine which type is the most effective, the manager collected one week’s data from 25 randomly selected stores. For each store, the following variables were recorded: Weekly gross sales Weekly expenditure on direct mailing (Direct) Weekly expenditure on newspaper advertising (Newspaper) Weekly expenditure on television commercials (Television) Following is the regression output based on the above-mentioned data. SUMMARY OUTPUT
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
D& T LTD marketing team needed more information about the effectiveness of their 3 main
Weekly gross sales
Weekly expenditure on direct mailing (Direct)
Weekly expenditure on newspaper advertising (Newspaper)
Weekly expenditure on television commercials (Television)
Following is the regression output based on the above-mentioned data.
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SUMMARY OUTPUT |
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Regression Statistics Multiple R |
0.442 |
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R Square |
A |
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Adjusted R Square |
0.080 |
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Standard Error |
2.587 |
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Observations |
25 |
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ANOVA |
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Df |
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SS |
MS |
F |
Significance F |
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Regression |
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B |
34.1036 |
E |
F |
0.1979 |
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Residual |
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21 |
D |
6.6933 |
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Total |
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C |
174.6631 |
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Coefficients |
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Standard Error |
t Stat |
Pvalue |
Lower 95% |
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Intercept 12.31 4.70 2.62 0.02 |
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2.54 |
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Direct 0.57 1.72 H 0.74 |
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-3.01 |
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Newspaper 3.32 1.54 2.16 0.04 |
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0.12 |
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Television G 1.96 0.37 0.71
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-3.34 |
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a. Complete the missing entries from A to H in this output |
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b. Assess the independent variables significance at 5% level |
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c. Does the model is significant at 5% level? |
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