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Sales in Medical Supply Industry Analysis Assignment
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Assignment Task
Meddicorp Company sells medical supplies to hospitals, clinics and doctors’ officers. The company currently markets in 25 sales territories. Meddicorp management is concerned with the effectiveness of a new bonus program. This program is overseen by regional sales managers and provides bonuses to salespeople based on performance. Management wants to know if the bonuses paid in 2020 were related to sales. In determining this relationship exists, they also want take into account the effects of advertising.
The variables to be used in the study include:
Y: Sales (in thousands of dollars) in each territory
X1:The amount spent on advertising in each territory (in hundreds of dollars) X2:The total amount of bonuses paid in each territory (in hundreds of dollars)
Correlations
| Sales | Adv | Bonus | ||
| Sales | Pearson Correlation | 1 | .900** | .568** |
| Sig. (2-tailed) | .000 | .003 | ||
| N | 25 | 25 | 25 | |
| Adv | Pearson Correlation | .900** | 1 | .419* |
| Sig. (2-tailed) | .000 | .037 | ||
| N | 25 | 25 | 25 | |
| Bonus | Pearson Correlation | .568** | .419* | 1 |
| Sig. (2-tailed) | .003 | .037 | ||
| N | 25 | 25 | 25 |
Model Summaryb
| Model | R | R Square | Adjusted RSquare | Std. Error of theEstimate |
| 1 | .925a | .855 | .842 | 90.74851 |
- Predictors: (Constant), bonus, Adv
- Dependent Variable: sales
ANOVAb
| Model | Sum ofSquares | Df | MeanSquare | F | Sig. | |
| 1 | Regression | 1067797.321 | 2 | 533898.660 | 64.831 | .000a |
| Residual | 181176.419 | 22 | 8235.292 | |||
| Total | 1248973.740 | 24 | ||||
- Predictors: (Constant), bonus, Adv
- Dependent Variable: sales
Coefficientsa
| Model | UnstandardizedCoefficients | StandardizedCoefficients | t | Sig. | CollinearityStattistics | |||
| B | Std. Error | Beta | Tolerance | VIF | ||||
| 1 | (Constant) | 516.444 | 189.876 | -2.270 | .013 | |||
| Adv | 2.743 | .275 | .803 | 8.983 | .000 | .825 | 1.213 | |
| Bonus | 1.858 | .716 | .232 | 2.593 | .017 | .825 | 1.213 | |
You are Required to:
- Develop a regression model to predict
- Interpret the overall utility of the (F test in ANOVA)
- Interpret whether there is a linear relationship between each independent variable and dependent (T tests for slope coefficients)
- Interpret the value of R2 (multiple coefficient of determination).
- Interpret residual analysis results and determine the adequacy of the
- State the multiple regression
- Interpret the regression
- Predict the mean sales when advertising is $50,000 and total bonus payment is $30,000.
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