5. Referring to Scenario 13-11, what is the standard deviation around the regression line? SCENARIO 13-11A computer software developer would like to use the number of downloads (in thousands) forthe trial version of his new shareware to predict the amount of revenue (in thousands of dollars)he can make on the full version of the new shareware. Following is the output from a simplelinear regression along with the residual plot and normal probability plot obtained from a data setof 30 different sharewares that he has developed:Regression StatisticsMultiple RR SquareAdjusted R SquareStandard Error0.86910.75540.746744.4765Observations30.0000ANOVAdfMSSignificance F0.0000FRegression171062.9193 171062.919386.4759Residual2855388.43091978. 1582Total29226451.3503Standard Error26.9183t Stat-3.5315Lower 95%Upper 95%CoefficientsP-valueIntercept-95.06140.0015-150.2009-39.9218Download3.72970.40119.29920.00002.90824.5513Download Residual PlotNormal Probability Plot1201201001008080606040402020-20-2040-40-60-60-80-80-100-1000.0020.0040.0060.0080.00100.00120.00-3-1DownloadZ Value2.2.sjerpisayResiduals

Answered: Referring to Scenario 13-11, what is…

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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1. Describe the key features of the normal curve and explain why the normal curve in real-life distributions nevermatches the model perfectly.
The standard error of the estimate in regression is: a) similar to the standard deviation, but that it takes deviations from the regression line instead of the horizontal one b) the sum of squared deviations from the mean of the data c) the sum of absolute deviations from predicted values d) none of the above
Explain the concept of Asymptotic Distribution of the OLS Estimatorand t-Statistic in the multi regression model?
11. What is the standard error of the slope of the regression line (SEb)? Please put the answer in bold.
3) For a sample of 306 students in a basic business communications course, the sample regression line y = 58.813 + 0.2875x was obtained. Here y = final student score at the end of the course x = score on a diagnostic writing skills test given at the beginning of the course The coefficient of determination was 0.1158 and the estimated standard deviation of the estimator of the slope of population regression line was 0.04566. a) Define and interpret the estimates of the regression equation. b) Interpret the coefficient of determination. c) What will happen to the final student score if his diagnostic writing test score increases by 2? d) How can you inmprove this model?
24. A bivariate regression was conducted to determine the time to failure (seconds) based on the number of push-ups conducted, what term describes when the line of best fit crosses the vertical axis? Outlier Y-intercept Intercept
Suppose we have fit a multiple linear regression with 8 explanatory variables and an intercept with 85 observations. We want to test the joint significance of the first 5 explanatory variables using an F test. Please fill in the blanks for the numerator and denominator degrees of freedom of the F statistic of the test: "The F statistic is F(
4. A simple linear regression is fit to a dataset. Unfortunately, the corresponding ANOVA table is not complete because some quantities in the table are missing due to unknown digital errors. Calculate the missing values, denoted by "?", in the ANOVA table based on the other available values. Is this regression model significant (α = 0.05)? Source Regression Residual Total df ? ? 21 SS ? ? ? MS = SS/df ? 1.13 F-Ratio 430.65 p-value ?
12. Use a = 0.10. Is the coefficient of the independent variable significantly different from zero? No a. b. Yes с. Cannot conclude 13. In multiple regression analysis, high correlation among the independent variables is termed linearity b. a. multicollinearity homoscedasticity endogeneity с. d.
П. 2. What is the degrees of freedom in a multiple regression model( with n values in each variable) with 14 independent variables when doing a t-test for the individual regression coefficients determined?
In regression, what do residuals tell us? The amount of error between the predicted y-values and the actual y-values. Residuals tell us how close the predicted values are to the average of the y variable at each value of the x-variable. How much error there was in predicting the x-variable from the actual value for the x-variable. How much the predicted values for the y- variable are different from the pointof-averages.
5.

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