Consider the following regression estimates (FN8)SourceSSdfMSModel71007499.52 35503749.8Residual174967111497 352046.502Total245974611499 492935.092incometfemale-0.70 0.48313.97 0.000hours_cons6.360.000What is the R-squared of the regression above?Please state the answer as a value between 0 and 1.Coef. Std. Err.-37.5719 53.5540318.79374 1.345669302.975 47.65295Number of obs500F(2, 497)100.85Prob > F=0.0000P>|t| [95% Conf. Interval]-142.792167.648316.1498421.43765209.349396.6011||||

Answered: Image /qna-images/answer/5ebcf304-c0aa-4f78-936c-3e72b9fb4699.jpg

Answered: Consider the following regression…

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015

1st Edition

ISBN:9781680331141

Author:HOUGHTON MIFFLIN HARCOURT

Publisher:HOUGHTON MIFFLIN HARCOURT

Chapter4: Writing Linear Equations

Section: Chapter Questions

Problem 12CR

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Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + B₂hwrte + Â¸priGPA+ Â¹ ACT reg final atndrte hwrte priGPA ACT Source Model 3094.70776 Residual 11929.9465 SS Total 15024.6543 final atndrte hwrte priGPA ACT _cons df MS 773.676939 4 669 17.832506 673 22.324895 Coef. Std. Err. Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 1.46863 9.225908 Construct a 95% confidence interval about 33. The lower bound is |||||||||||| 674 [95% Conf. Interval]
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final = 30 +3₁atndrte + B₂hwrte +33priGPA + B ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 Residual 11929.9465 Total final . reg atndrte hwrte priGPA ACT _cons Total .reg final priGPA ACT final priGPA ACT _cons Source Model 2950.00785 Residual 12074.6465 Source ss 15024.6543 Total atndrte hwrte priGPA ACT _cons Coef. Std. Err. SS atndrte hwrte priGPA ACT .0138725 .0183476 .010863 1.906347 -3750236 .3990516 .0535332 9.225908 1.46863 15024.6543 Calculate the se(3₁). Model 89295.5136 Residual 93922.8539 df 4 669 SS 183218.367 673 22.324895 Coef. Std. Err. df 2.390227 .3233916 .3534674 .0505019 11.74854 1.147688 673 MS 773.676939 17.832506 2 1475.00393 671 17.9950022 df MS 22.324895 t P>|t| [95% Conf. Interval] MS Number of obs F(4, 669) Prob > F R-squared 3 29765.1712 670 140.183364 Adj R-squared Root MSE 673 272.241259 Number of obs F(2, 671)…
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + ß₂hwrte + Â¸priGPA+ Â¹ ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 11929.9465 Residual Total final SS atndrte hwrte priGPA ACT _cons 15024.6543 df 773.676939 4 669 17.832506 MS 673 22.324895 Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 9.225908 1.46863 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| ||||||||||||| 674 [95% Conf. Interval] Approximate the p-value from the null hypothesis that all of the slope parameters are equal to zero.
A statistics student wanted to determine if the distance to a destination was related to the price an airline would charge to fly to that destination. The student did a regression of airfare, y, on distance, x, and obtained the printout shown below from a statistical software package. Predictor Coef Constant 83.02 Distance 0.117 Oŷ= 0.117+83.02x Oy=83.02 +0.117x Oy= 49.29 + 83.02x Oŷ= 0.028 + 0.117x Oy=83.02 + 25.25x 1 SE Coef 49.29 0.028 S = 25.25 R-Sq = 63.2 % Which of the following is the correct equation of the least-squares line? Mark this and return t-ratio 1.689 4.179 R-Sq (Adj) = 64.7% Save and Exit р 0.046 0.000 di I tv Next AM Submit Aa
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right am blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 100 mm Hg Use a significance level of 0.05. Right Arm 103 102 95 78 Left Arm 175 169 147 146 144 m Click the icon to view the critical values of the Pearson correlation coetticient r The regression oquation is y =+O (Round to one decimal place as needed) Given that the systolic blood pressure in the right arm is 100 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg (Round to one decimal place as needed.)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y-ax+b a=-1.108 b-34.092 r2-0.736164 r=-0.858 Assume the correlation is significant, and use this to predict the number of situps a person who watches 4 区 hours q TV can do (to one decimal place) 72222392 口 D
Use the two regression lines to estimate when sales in grocery stores are likely to equal sales in warehouse clubs. What type of prediction did you just make? Explain.
is a scatterplot with RSquared equal to 0.022418 and Root Mean Square error equal to 2.422091 a good model and what do those two valus tell you about the graph. Picture of scatter plot attached.
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm 102 101 95 75 75 Left Arm 174 169 147 144 144 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = X. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 85 mm Hg, the best predicted systolic blood pressure in the left arm is (Round to one decimal place as needed.) mm Hg. Enter your answer in each of the answer boxes. F12 F9 F10 F11 F8 888 F4 F6 F7 DOO F5 esc F2 F3 F1 & @ 2$ delete 6. 7 8. 9. 2 4 Y U Q E tab enter K retur F A caps lock N P 工 LU # 3
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + B₂hwrte + Â¸priGPA+ ¹ ACT reg final atndrte hwrte priGPA ACT Source Model Residual Total final atndrte hwrte priGPA ACT _cons SS 3094.70776 11929.9465 15024.6543 df 4 669 673 Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 1.46863 9.225908 MS 773.676939 17.832506 22.324895 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| |||||||||||| 674 [95% Conf. Intervall Construct a 95% confidence interval about 33. The critical value for the confidence interval is
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 102 101 93 78 77 Left Arm 176 170 144 144 145 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y =+ x. (Round to one decimal place as needed.)
A simple linear regression of Y on X reveals that for n=30 observations, r²=0.80, the standard deviation of X is 3. The regression sum of squares is 1400. What is the value of the standard deviation of Y? What is the value of slope b? Show steps.

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