The partially completed regression output to predict the selling price of a house based onthe age of the house in years (Age), the living area of the house in square feet (Living Area)and the number of bedrooms (Bedrooms) is given belowRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotal0.848636,009.01dfSS36,709,265,905.7014 50,972,400,000.00Coefficients Standard Error108,597.3721-580.687086.828231,261.9127MS101,922.33332,092.498127.699411,006.8696t StatInterceptAgeLiving AreaBedrooms[NOTE: Do not include a comma in your response]1)The predicted selling price for a home that is 10 years of age, has 2,000 square feet ofliving area and has three bedrooms is $(to 2 d.p.) 6) Which one of the following statements describes the results of the hypothesis test for theoverall regression model using alpha = 0.05?O Because the test statistic is greater than the critical value, we reject the null hypothesisand conclude that there is a relationship between the dependent variable and theindependent variables.O Because the test statistic is greater than the critical value, we fail to reject the nullhypothesis and conclude that there is a relationship between the dependent variable andthe independent variables.O Because the test statistic is less than the critical value, we fail to reject the null hypothesisand conclude that there is no relationship between the dependent variable and theindependent variables.Because the test statistic is less than the critical value, we reject the null hypothesis andconclude that there is no relationship between the dependent variable and theindependent variables.

It is required to predict the selling price for a 10 years-old home that has a living area of 2,000…

Answered: The partially completed regression…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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What is a residual? Explain when a residual is positive, negative, and zero. OA. A residual is the sum of the observed y-value of a data point and the predicted y-value on a regression line for the x-coordinate of the data point. A residual is positive when the point is below the line, negative when it is above the line, and zero when the observed y-value equals the predicted y-value. OB. A residual is the difference between the observed y-value of a data point and the predicted y-value on a regression line for the x-coordinate of the data point. A residual is positive when the point is above the line, negative when it is below the line, and zero when the observed y-value equals the predicted y-value. OC. A residual is the difference between the observed y-value of a data point and the predicted y-value on a regression line for the x-coordinate of the data point. A residual is positive when the point is below the line, negative when it is above the line, and zero when the observed…
Write the formula for the estimated regression line and interpret the slope of the estimated regression line, the intercept of the estimated regression line- Is it meaningful?, and the estimated R2. Based on the fitted regression model, what is the predicted ATST for a child who is 7 years old? What is the correlation between AGE and ATST? Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Please fill in the blanks and select the correct answers over the image provided. Thank you
Interpret the estimated R2 Does the residual plot suggest that the fitted regression line is inappropriate for these data? Explain why or why not. Suppose that a new subject is added to the study data and that subject is 12.5 years old with an ATST of 580 minutes. If the regression model were to be refit with this additional data point, would the new slope be greater than or less than -14.041? Justify your response.
Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. Does “Promotion” have a significant effect on “Attendance”? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500
Please do only the last three
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
Which of the following is FALSE? * The residuals in a regression model are assumed to have a zero mean. Data point below the regression line, the residual is negative. The residuals in a regression model are assumed to have increasing mean. The regression model assumes the residuals are normally distributed.
Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…
please answer for what is the p value

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