Predicting Cost from Mileage 80000 y = -0.1725x + 24765 70000 r2 = .0204 60000 50000 Cost of Car 40000 30000 20000 10000 O O 10000 20000 30000 40000 50000 60000 MileageRegina owns a small car dealership and she completed a series of studies to better understand what factors affect the price of cars. In her third study, she wants to know whether car price is related to mileage on the car (as measured by the odometer). So she measures the relationship between price of car and mileage. The results of he findings are shown here. What is the correlation coefficient for these data? (hint: does the regression line go up or down? What does that mean in terms of +/-?) O a) +0.0204 b) - 0.0204 O c) +0.1428 O d) -0.1428Regina owns a small car dealership and she completed a series of studies to better understand what factors affect the price of cars. In her third study, she wants to know whether car price is related to mileage on the car (as measured by the odometer). So she measures the relationship between price of car and mileage. The results of her findings are shown here. What is the coefficient of determination? O a) +0.0204 O b) -o.0204 O c) +0.1423 Q d) -0.1428 Regina owns a small car dealership and she completed a series of studies to better understand what factors affect the price of cars. In her third study, she wants to know whether car price is related to mileage on the car (as measured by the odometer). So she measures the relationship between price of car and mileage. The results of her findings are shown here. Which of the following would be the best interpretation of the regression equation? For each additional mile driven (as x goes Up by 1), O a) price goes down by 14.28 cents. O b) price goes down by 17.25 cents. O c) price goes up by 24,765.00 cents. Q d) price goes up by 17.25 cents. Regina owns a small car dealership and she completed a series of studies to better understand what factors affect the price of cars. In her third study, she wants to know whether car price is related to mileage on the car (as measured by the odometer). So she measures the relationship between price of car and mileage. The results of her findings are shown here. Which of the following would best describe this regression analysis? 0 a) The independent variable is mileage, which is a ratio level of measurement 0 b) The independent variable is mileage, which is an interval level of measurement 0 c) The dependent variable is mileage, which is a ratio level of measurement 0 d) The dependent variable is mileage, which is an interval level of measurement Salary Years Experience Years of Education Number supervised Salary 1 Years Experience 0.783143559 Years of Education 0.201119059 0.000501348 Number of employees supervised 0.236970798 -0.010210379 -0. 179306399 Zeana runs a human resources department and wants to know more about what factors affect salary. So, she surveyed 100 executives and asked them their salary, how many years of experience they have had, how many years of education they have had and the number of people they supervise. In her first analysis she simply created a correlation matrix, as shown in this figure. Each correlation in the matrix was based on 100 executives. Which of the following correlations will provide you with the strongest predictive results? O a) "years of experience" and "number of employees supervised O b) "salary" and "years of education" O c) "salary" and "number of employees supervised" O d) "salary" and "years of experience"SUMMARY OUTPUT Regression Statistics Multiple R 0857422237 R Square 0735172892 Adjusted R Square 0726897045 Standard Error 1317158115 Observations 100 ANOVA d 55 Ms F S' n'ioanceF Regression 3 46277477588 1542582553 8883355162 1.359646-27 Residual 96 16670269912 1736486449 Total 9 62947747500 Coo icients Standard Error tStat Pwaiuo Intercept 3034443116 10570.3962 0287070129 0774677329 Years Experience 2699224217 1803829507 1496385444 7.8072952? Years of Education 2765406523 5840578782 433481589 7.56282E-06 NumberofernEloEes supervised 4338973757 805% 5437686511 4.1028507 Zeana wants to know what factors predict salary, so she simply completed a multiple regression analysis. She surveyed 100 executives and asked them four questions: what their salary was (Salary), how many years of experience they have had (Years of Exp), how many years of education they have had (Years of Educ), and the number of people they supervise (Number Sup). Please assume an alpha of .05. In this regression analysis, there are ___ dependent variables and ___ independent variables in her regression analysis? Zeana wants to know what factors predict salary, so she simply completed a multiple regression analysis. She surveyed 100 executives and asked them four questions: what their salary was (Salary), how many years of experience they have had (Years of Exp), how many years of education they have had (Years of Educ), and the number of people they supervise (Number Sup). Please assume an alpha of .05. In this regression analysis, there are ___ dependent variables and ___ independent variables in her regression analysis? O a) 1; 3 O b) 3 ; 1 O c) 1;4 O d) 4; 1Please refer again to Zeana's multiple regression results presented here, and identify the correct regression equation: 0 a) Salary = (2765)(Years of Exp) + (43.79)(Years of Educ) +(3034)(Number Sup) + 2699 O b) Salary = (43.78)(Years of Exp) + (3034)(Years of Educ) +(2699)(Number Sup) + 2765 O c) Salary = (3034)(Years of Exp) + (2699)(Years of Educ) +(2765)(Number Sup) + 43.78 0 d) Salary = (2699)(Years of Exp) + (2765)(Years of Educ) +(43.79)(Number Sup) + 3034 Refer to Zeana's analysis to answer this question: As the years of experience increases, the predicted salary will increase. If we increase \"years of experience\" by 1 full point and hold the other two independent variables constant, we can estimate an increase of ________ in salary. O a) $2699 O b) $2765 O c) $43.79 0 d) $3034 When would you use a regression analysis over a correlation? O a) We use a regression to make predictions and correlations to observe trends 0 b) We use a regression to observe trends and correlations to make predictions O c) Both are used when we have two categorical variables Q d) Both are used when we have a categorical independent variable