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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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?
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
None
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
A regression model relating x, 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 = 40. ANOVA df SS MS F Significance F Regression 6,759.5 Residual Total 9,031.4 Coefficients Standard Error t Stat P-value Intercept 75.0 10.408 Number of 40.0 5.323 Salespersons a. Write the estimated regression equation (to whole number). ŷ = + b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) F-value p-value is - Select your answer - we - Select your answer - v Ho c. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) t Stat p-value is - Select your answer - we - Select your answer - v Ho: B, d. Predict the annual sales at the Memphis branch office. This branch employs 10 salespersons. $ thousand (to whole number) %24

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