Consider the following estimated regression model relating annual salary to years of education and work experience....

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Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 11,507.30 + 3258.45 (Education) +811.24 (Experience) Suppose an employee with 10 years of education has been with the company for 12 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary? Answer 2 Points $ Keypad Keyboard Shortcuts Consider the following monthly revenue data for an up-and-coming cyber security company. Sales Data Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 326 8 705 2 552 9 787 3 535 10 816 4 571 11 827 5 636 12 846 6 685 13 858 7 700 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square 0.950622806 0.903683719 Adjusted R Square 0.894927694 Standard Error 50.25041687 Observations 13 ANOVA SS MS F df Regression 1 260,608.620879 260,608.620879 103.20706793 Residual 11 27,776.148352 Total 2525.104396 12 288,384.769231 Coefficients Standard Error t Stat P-value Intercept 415.42307692 29.56475264 14.05129554 2.26252E-08 Month 37.84065934 3.724808717 10.159087948 6.31286E-07 Step 1 of 3: Write the estimated regression equation using the least squares estimates for by, and b. Round to four decimal places, if necessary. Answer 2 Points Revenue= Mo (Manth) Keypad Keyboard Shortcuts = ev Consider the following monthly revenue data for an up-and-coming cyber security company. Sales Data Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 326 8 705 2 552 9 787 3 535 10 816 4 571 11 827 5 636 12 846 6 685 13 858 7 700 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square 0.950622806 0.903683719 Adjusted R Square 0.894927694 Standard Error 50.25041687 Observations 13 ANOVA df SS MS F Regression 1 260,608.620879 260,608.620879 103.20706793 Residual 11 27,776.148352 Total 12 288,384.769231 2525.104396 Coefficients Standard Error Intercept 415.42307692 Month 37.84065934 29.56475264 3.724808717 10.159087948 t Stat 14.05129554 P-value 2.26252E-08 6.31286E-07 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 14th month. Round to four decimal places, if necessary. Answer 2 Points Keypad Keyboard Shortcuts N Consider the following monthly revenue data for an up-and-coming cyber security company. Sales Data Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 326 8 705 2 552 9 787 3 535 10 816 4 571 11 827 5 636 12 846 6 685 13 858 7 700 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square 0.950622806 0.903683719 Adjusted R Square 0.894927694 Standard Error 50.25041687 Observations 13 ANOVA df SS MS F Regression 1 260,608.620879 260,608.620879 103.20706793 Residual 11 27,776.148352 2525.104396 < Total 12 288,384.769231 Prev Coefficients Standard Error Intercept 415.42307692 Month 37.84065934 29.56475264 3.724808717 10.159087948 1 Stat 14.05129554 P-value 2.26252E-08 6.31286E-07 Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary. Answer 2 Points % Keypad Keyboard Shortcuts = Ne Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 11,507.30 + 3258.45 (Education) +811.24 (Experience) Suppose an employee with 10 years of education has been with the company for 12 years (note that education years are the number of years after 8th grade). According to this model, what is his estimated annual salary? Answer 2 Points $ Keypad Keyboard Shortcuts Consider the following monthly revenue data for an up-and-coming cyber security company. Sales Data Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 326 8 705 2 552 9 787 3 535 10 816 4 571 11 827 5 636 12 846 6 685 13 858 7 700 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square 0.950622806 0.903683719 Adjusted R Square 0.894927694 Standard Error 50.25041687 Observations 13 ANOVA SS MS F df Regression 1 260,608.620879 260,608.620879 103.20706793 Residual 11 27,776.148352 Total 2525.104396 12 288,384.769231 Coefficients Standard Error t Stat P-value Intercept 415.42307692 29.56475264 14.05129554 2.26252E-08 Month 37.84065934 3.724808717 10.159087948 6.31286E-07 Step 1 of 3: Write the estimated regression equation using the least squares estimates for by, and b. Round to four decimal places, if necessary. Answer 2 Points Revenue= Mo (Manth) Keypad Keyboard Shortcuts = ev Consider the following monthly revenue data for an up-and-coming cyber security company. Sales Data Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 326 8 705 2 552 9 787 3 535 10 816 4 571 11 827 5 636 12 846 6 685 13 858 7 700 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square 0.950622806 0.903683719 Adjusted R Square 0.894927694 Standard Error 50.25041687 Observations 13 ANOVA df SS MS F Regression 1 260,608.620879 260,608.620879 103.20706793 Residual 11 27,776.148352 Total 12 288,384.769231 2525.104396 Coefficients Standard Error Intercept 415.42307692 Month 37.84065934 29.56475264 3.724808717 10.159087948 t Stat 14.05129554 P-value 2.26252E-08 6.31286E-07 Step 2 of 3: Using the model from the previous step, predict the company's revenue for the 14th month. Round to four decimal places, if necessary. Answer 2 Points Keypad Keyboard Shortcuts N Consider the following monthly revenue data for an up-and-coming cyber security company. Sales Data Month Revenue (Thousands of Dollars) Month Revenue (Thousands of Dollars) 1 326 8 705 2 552 9 787 3 535 10 816 4 571 11 827 5 636 12 846 6 685 13 858 7 700 The summary output from a regression analysis of the data is also provided. Regression Statistics Multiple R R Square 0.950622806 0.903683719 Adjusted R Square 0.894927694 Standard Error 50.25041687 Observations 13 ANOVA df SS MS F Regression 1 260,608.620879 260,608.620879 103.20706793 Residual 11 27,776.148352 2525.104396 < Total 12 288,384.769231 Prev Coefficients Standard Error Intercept 415.42307692 Month 37.84065934 29.56475264 3.724808717 10.159087948 1 Stat 14.05129554 P-value 2.26252E-08 6.31286E-07 Step 3 of 3: What percent of the variation in revenue is explained by the linear time trend model? Round to two decimal places, if necessary. Answer 2 Points % Keypad Keyboard Shortcuts = Ne