Predictive Analytics Instructions: Put your name without fail on each page. Do t
ID: 3048037 • Letter: P
Question
Predictive Analytics Instructions: Put your name without fail on each page. Do the exam yourself, and sign a statement stating that you did not receive help from other persons besides the instructor. You may use a use the end of the excam for more space. Show your work. And, Remember, the Fore will be with you. Always Data Y hat 23 16 25 28 51 14 SUMMARY OUTPUT A Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.97688814 0.939080583 3.277208147 ANOVA Regression Residual Total 1 672.9797 3 32.22028 4 Standard Error Coefficients t Stat P-value 2.531468531 3.604616 0.702285 0.533087 3.43006993 0.433317 7.915839 0.004203 InterceptExplanation / Answer
9) Model B predictors are not significant for predictions.
The coefficients p-value must be less than 0.05. But both predictor(explanatory variables) p-values values are higher than the significant level.
10) Model A is better-compared than Model B. Because explanatory variables are significant to an output variable.
R-squared value for Model A= SSRegression/ (SSregression+SSResidual) =672.9797 / 705.2 = 0.95431
R-sqaured for Model B= 0.956648
Even though the R-sqaured value is high for Model B but Model A has significant inpt variables.
11) Correlation coefficients:
Model A: r= 0.976888 and R-squared = 0.95431
The correlation coefficient r = 0.976888
The percentage of estimated variance covered by Model A is 95.431%
Model B: r= 0.978084; R-sqaured= 0.956648
The correlation coefficient r = 0.978084
The percentage of estimated variance covered by Model B is 95.66%
12) To interpret coefficients of Model B:
For every additional unit of X output variable increases by 1.886228 units.
For every additional unit of X^2, output variable increases by 0.080838 units.