An organization develops a linear regression model to forecast sales. Below are

Question

An organization develops a linear regression model to forecast sales. Below are the results of the regression analysis using 12 periods in the model.
What is the null hypothesis?
Is the model significant? What data demonstrate the significance of the model?
What does the R Square signify?
What is the forecast for period 13? SUMMARY OUTPUT Regression Statistics Multiple R 0868519059 Sce075482535 Adjusted R Square Standard Error Observations 0.729757892 ? 71684689 12 ANOVA Significance MS 1 15,77797 15 77797203 30 70424132 0000247139 Regression Residual Total 10 S 138695 0 513869464 11 2091667 Standard P-volue lower 95% Up er 95% Lower 95 0% upper 950% Intercept 12 42424242 044189 28 1608315S7 408 111 4412126 13 40727225 114412126 1340727225 x Vanable 0 332167832 0059946 541140796 000247139 01986038 46535283 0198600381 0 455735283 Coefficients Error Stat

Explanation / Answer

Null hypothesis : Overall regression is not significant. Yes, the model is significant. Since the P- value (significance F) = 0.000247139 < 0.01 (level of significance) , we reject Ho and conclude that the model is significant. R-square basically measures the total variability explained in the model by the independent variables. Here, R-square = 0.7543 which means 75.43 % variability is being explained by the independent variable x in the model. Forecast for period 13 can be obtained by the equation : y = 12.4242 + 0.33216 * x

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