A regression model relating , number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output.Where ntotal = 34.ANOVAdfSSMSFSignificance FRegression6,971.9ResidualTotal9.108.7CoefficientsStandard Errort StatP-valueIntercept82.010.894Number of45.05.730Salespersonsa. Write the estimated regression equation (to whole number).b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals)F-valueD-value is - Select your answer -weSelect your answer - V Hoc. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals)t Statp-value isSelect your answer -, wev Ho : B1 =Select your answer-d. Predict the annual sales at the Memphis branch office. This branch employs 10 salespersons.thousand (to whole number)

Regression analysis is used to study the relationship between two or more variables. Moreover, the…

Answered: A regression model relating x, number…

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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Which of the following is the regression equation at the 1% meaning level according to the Minitab output given below for regression analysis?
The regression equation is Health Index= y + a Age + ß Blood sugar + 8 Blood Pressu SE 20986 Coef 2912 339.28 71.95 Age Blood sugar 209.2 179.3 Blood pressure 207.2 225.4 S = 962.233 R-Sq = 86.6% R-Sq (adj) = 76.5% Coef Constant Analysis of Variance Source DF Regression 3 Residual Error 4 Total 7 SS 23863180 3703570 27566750 MS T 7.21 4.72 * 0.92 7954393 925892 F *** P 0.002 0.009 0.308 до 0.032 a) What is dependent and independent variables? b) Fully write out the regression equation. c) Fill in the missing values **, ***', and *****. d) Hence test whether & is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis.
Using the following information. Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12,323.56 12,323.56 90.0481 Residual 8. 1,094.842 136.8553 Total 9. 13,418.4 Estimate the value of Ý when X= 4.
5. You estimated the following regression. Which of the following is the estimated regression line? Source | SS df MS Number of obs = 379 -------------+---------------------------------- F(1, 377) = 24045.16 Model | 401185353 1 401185353 Prob > F = 0.0000 Residual | 6290117.58 377 16684.662 R-squared = 0.9846 -------------+---------------------------------- Adj R-squared = 0.9845 Total | 407475471 378 1077977.44 Root MSE = 129.17 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.08326 .065026 155.07 0.000 9.955396 10.21111 _cons | 87.07298 7.855763 11.08 0.000 71.62637 102.5196…
Explain Which of the following statements are true about studentized residuals? They are the deviations of the observed values from the fitted line. They're closely related to the errors of prediction of the observation. Large studentized residuals will lead to large influence on the regression model in all circumstances
A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 4.3 14.9x Coefficients (Intercept) X Estimate St 4.3 Ho: B₁ = 0 Ha: B₁ > 0 B1 O Ho: B₁ Ha: B₁ <0 = 0 14.9 B1 O Ho: B₁ = 0 0 Ha: B1 Std. Error Test statistic P-value 2.9 5.1 1.48 Specify the null and the alternative hypotheses that you would use in order to test whether a negative linear relationship exists between x and y. 2.92 0.08 0.01
In a multiple regression analysis involving an intercept, 15 independent variables and 200 observations, Total SS = 800 and Residual SS = 240. Adjusted R-squared is
In the above regression of air quality on other variables in question 2, R-squared is 0.3829. How do you interpret it?
The regression equation for predicting a woman's muscle mass (a quantitative metric, Y) from age (in years, X) is given as ý = 156.35 - 1.19. The ANOVA table for the linear regression of Y vs X is given below. Source Df SS MS p-value Age 11627.5 11627,5 174.06 <,0001 Error 58 3874.4 66.8 Total 59 15501.9 Fill in the blanks below that demonstrate how to construct an interval estimate for the muscle mass of an individual 60-year-old woman. Use a confidence level of 95%; note that the multiplier is t = 2.002 and the standard error of the mean response is s(i,) = 1.055. 95% Interval: ( Select) + ( Select ) [ Select]
Calculate the estimated R2 and state why we should consider using adjusted R2 instead of R2 in the setting of multiple linear regression?
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
A study was conducted to determine the relationship between starting salaries (RM thousands) for recent statistics graduates and their grade point averages in the major course. A linear regression model was fitted to the data and the estimates regression function was obtained. Part of the computer output for the above analysis is given below: ANOVA Model Sum of df Mean F Sig. Squares Square Regression 147.28 .000 Error 734.9 40.828 Total 6748.2 Coefficients Unstandardized Coefficients Model Sig. Std. B Error Constant GPA -8.42 3.007 3.395 0.2477 -2.48 12.14 0.011 0.000 (a) Complete the ANOVA table (blue boxes). (b) Write down the estimated regression function. Interpret the estimated parameters. (c) Test whether there is a linear association between salaries and grade point average. Use a = 0.05. (d) Determine the coefficient of determination for the model and interpret its meaning.

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