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Variables Dependent Independent IV Variable Variable Axis Y X1 X2 Title Sell Price SqFt Age Condition 95000 1926 30 Good 119000 2069 40 + Excellent 124800 1720 30 Excellent 135000 1396 15 Good 142000 1706 32 Mint 145000 1847 38 Mint 159000 1950 27 Mint 165000 2323 30 Excellent 182000 2285 26 Mint 183000 3752 35 Good 200000 2300 18 Good 211000 2525 17 Good 215000 3800 40 Excellent 219000 1740 12 MintQuestions to interpret the results of the regression analysis Type answer in the box 1. Is the regression model statistically significant (Yes or No)? 2. Highlight the cell in green that corresponds to the statistic value you used to answer question #1. 3. For each unit increase in SqFt, how much does Sell Price change? 4. A variable should be added to the model if it causes the Adjusted R Square to decrease (True or False)? 5. For each unit increase in Age, how much does Sell Price change? 6. How much does Sell Price change if the condition is Excellent? 6. How much variability (in %) in Sell Price, can be explained by the regression model with SqFt, Age, and Condition as the independent variables? 7. Highlight the cell in blue that corresponds to the statistic value you used to answer question #6. 8. Highlight the cell in yellow that correponds to the Correlation Coefficient. 9. What is the expected Sell Price in $ if SqFt = 2285, Age = 26, and the Condition is Mint? 10. What is the expected Sell Price in $ if SqFt = 2300, Age = 18, and the Condition is Good?Vaiaes Dependent Variable Independent Variable IV IV IV IV Y X1 W ERA B40 X V A B C D E F G H K L M Questions to interpret the results of the regression analysis Type answer in 40 the box 1. Is the regression model statistically significant (Yes or No)? 41 42 2. Highlight the cell in green that corresponds to the statistic value you used to answer question #1. 3. For each unit increase in R (runs), how much does W (wins) change? 43 4. A variable should be added to the model if it causes the Adjusted R Square to increase (True or False)? 44 5. For each unit increase in batting average (AVG), how much does W (wins) change? 45 + 46 6. How much do W (wins) change if the field is astroturf? 6. How much variability (in %) in Wins, can be explained by the regression model with ERA, R, AVG, OBP, 47 Field, and Stadium as the independent variables? 7. Highlight the cell in blue that corresponds to the statistic value you used to answer question #6. 48 8. Highlight the cell in yellow that correponds to the Correlation Coefficient. 49 9. What are the expected number of Wins if ERA =4, R = 700, AVG =0.250, OBP =0.310, and the games are 50 played indoors on astroturf? (round to the nearest whole number) 10. What are the expected number of Wins if ERA =4, R = 700, AVG =0.250, OBP =0.310, and the games are 51 played outdoors on grass? (round to the nearest whole number) 11. Create an OBP Line Fit Plot with a regression line for the predicted W. Figure number is 3, title is OBP Fit11. Create an OBP Line Fit Plot with a regression line for the predicted W. Figure number is 3, title is OBP Fit + Plot by "your last name" Place the chart below the "OBP Line Fit Plot Here" cells highlighted in blue. 12. Create a Normal Probability Plot with a regression line. Figure number is 4, title is Normal Probability Plot by "your last name" Place the chart below the "Normal Probability Plot Here" cells highlighted in blue