Exhibit 15-6Below you are given a partial Excel output based on a sample of 16 observations.ANOVARegressionResidualInterceptX1X2000b. 5ofc..2O d..77Coefficients12.924-3.68245.216SS4,853MS2,426.5485.3Standard ErrorThe test statistic used to determine if there is a relationship among the variables equalsa. -1.44.4252.63012.560F

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Answered: Exhibit 15-6 Below you are given a…

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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7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the 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
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per month
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Q) Plot the relationship between total operating costs and machine hours. Draw the regression line.
he data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 51 inches. Is the result close to the actual weight of 4 punds? Use a significance level of 0.05. thest size (inches) Veight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. 45 43 43 52 52 352 374 275 314 440 367 Critical Values of the Pearson Correlation Coefficient r What is the regression equation? Critical Values of the Pearson Correlation Coefficient r a = 0.05 NOTE: To test Ho: p=0 against H, p#0, reject Ho if the absolute value of r is greater than the critical value in the table. y%3= x (Round to one decimal place as needed.) a = 0.01 4 0.950 0.990 0.878 0.959 0.811 0.917 0.754 0.875 8. 0.707 0.834 9. 0.666 0.798 10 0.632 0.765 11 0.602 0.735 12 0.576 0.708 13 0.553 0.684 14 0.532 0.661 15 0.514 0.641 16 0.497 0.623 17 0.606 0.590…
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When there are omitted variables in your regression, then... Group of answer choices This has no effect on the estimation of the explanatory variable because the variable is omitted This will always bias the OLS estimation of the explanatory variable the estimation of the explanatory variable(s) is unaffected The OLS estimation is biased if the omitted variables are correlated with the included variable
Using the following information: Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12,323.80 12,323.80 90.0481 Residual 8 1,454.602 136.8550 Total 9 13,778.4 What is the correlation coefficient? Multiple Choice 0.8944 0.9457 −0.9457 0.8878

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