Research Objective
The current task seeks to examine the factors that influence the overall brand equity using multiple linear regression.
Hypothesis
To address the above research objective, the following hypothesis will be tested.
Null Hypothesis
There is no relationship between the chosen independent variables and the overall brand equity.
Alternative Hypothesis
At least one of the independent variables is statistically significant in predicting the overall brand equity.
Model Summary
Table 1
| Model Summary | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .610a | .372 | .287 | 14.23227 |
| a. Predictors: (Constant), “I rarely purchase the latest products until I am sure a wide number of people approve of them”, “I don’t believe buying environmentally friendly products will help the environment”, Please indicate which category best describes your total household income before tax, “I often consult other people to help choose the best alternative available product”, How old are you? We would like this information for classification purposes. Please tell us more about yourself! A reminder that all responses are entirely anonymous and de-identified during the analysis process. What is your gender?, “My attitude towards myself changes when I use beauty products” |
ANOVA Output of the Model
Table 1 below includes the ANOVA output of the regression model.
Table 2: ANOVA
| ANOVA | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 6227.587 | 7 | 889.655 | 4.392 | .001b |
| Residual | 10532.996 | 52 | 202.558 | |||
| Total | 16760.583 | 59 |
Coefficient Estimates
Table 3: Coefficient estimates
| Coefficients | ||||||
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 58.227 | 11.798 | 4.935 | .000 | |
| How old are you? | -1.144 | 3.187 | -.042 | -.359 | .721 | |
| Please indicate which category best describes your total household income before tax: | 1.165 | 1.207 | .111 | .966 | .339 | |
| “I often consult other people to help choose the best alternative available product” | 5.254 | 1.522 | .411 | 3.453 | .001 | |
| “My attitude towards myself changes when I use beauty products” | .924 | 1.789 | .071 | .517 | .608 | |
| We would like this information for classification purposes. Please tell us more about yourself! A reminder that all responses are entirely anonymous and de-identified during the analysis process. What is your gender? | 3.663 | 3.924 | .121 | .934 | .355 | |
| “I don’t believe buying environmentally friendly products will help the environment” | -3.436 | 1.728 | -.230 | -1.988 | .052 | |
| “I rarely purchase the latest products until I am sure a wide number of people approve of them” | 2.245 | 1.874 | .149 | 1.198 | .237 | |
| a. Dependent Variable: Includes the sum of all the brand equity scores |
Interpretation
From table 1 above, we note that the regression model when using age group, total household income, gender, and four different segmentation variables has an R-Squared score of .372 indicating that the model accounts for up to 37.2% of the variation in the data. Hence, it can be argued that the model has a relatively good fit. In addition, with F(7, 52) = 4.392 and p(0.001) (see table 1), we reject the null hypothesis that the chosen independent variables are not significant in predicting the overall brand equity of the products.
Examining table 3, we note that at 0.05, only the “I often consult other people to help choose the best alternative available product” attribute is significant in predicting the overall brand equity.
How Output Can Be Used to Make Predictions
Using the coefficient estimates provided in table 3 above, the overall brand equity can be predicted as:
(Overall Brand Equity) = 58.227 -1.144(age) + 1.165 (total household income) + 5.254 (I often consult other people to help choose the best alternative available product) + 0.924 (My attitude towards myself changes when I use beauty products) + 3.663 (Gender) – 3.436 (I don’t believe buying environmentally friendly products will help the environment) + 2.245 (I rarely purchase the latest products until I am sure a wide number of people approve of them)
Conclusion for Management
Based on our analysis, we have noted that whether a customer consults other people to help choose the best alternative available product is a significant attribute in predicting the overall brand equity. As such, management should adopt measures to help improve customer experiences which will lead to better recommendations by other customers.
