MATH 533 Course Project Part C Regression Model Keller 2023

MATH 533 Course Project Part C Regression Model Keller 2023. The regression analysis is telling us that for each additional call sales will go up by 0.202. Minitab Result Regression Analysis: SALES versus CALLS (Appendix II) The regression equation is SALES = 9.64 + 0.202 CALLS Math 3 3. The correlation coefficient between sales and calls is calculated as 0.871. This positive correlation coefficient tells us that as the number of calls increase so does the number of sales. Correlation: Sales, Calls (Appendix III) Minitab Result Pearson correlation of Sales and Calls = 0.871 P-Value = 0.000 4. Minitab Result: (See above Fitted Line Plot for below result) S = 2.05708 R-Sq = 75.9% R-Sq(adj) = 75.7% The coefficient of determination, R-sq, is 75.9%. It gives us the proportion of the dependant variable. Sales can be explained by the number of calls. 5. H0: (Null Hypothesis) – There is not significant correlation H1: (Alternate Hypothesis)- There is correlation either negative or positive. Significance Level, α = 0.05 Decision Rule: This p-value is less than significant value (.05). Thus, the null hypothesis should be rejected. We accept the alternative hypothesis that sales are not equal to zero. It can be conclude that the regression model is valid due to overall test of significance. Math 4 Minitab Result General Regression Analysis: SALES versus CALLS (Appendix IV) Regression Equation SALES = 9.63795 + 0. CALLS Coefficients Term Coef SE Coef T P 95% CI Constant 9.63795 1.87156 5.1497 0.000 (5.92391, 13.3520) CALLS 0.20175 0.01148 17.5797 0.000 (0.17898, 0.2245) 6. There exist a strong positive relationship (R-sq=+0.871) between sales and calls. Calls are a good predictor for forecasting of sales. We can be nearly 100% sure that a relationship exist between calls and sales.