Regression Statistics Multiple R 0.427544459 R Square 0.182794265 Adjusted R Square 0.129498238 Standard Error 12.45749356 Observations 50 ANOVA Regression Residual Total df SS MS F Significance F 3 1596.799298 532.2664326 3.429791628 0.024596922 46 7138.700702 155.1891457 49 8735.5 Coefficients Standard Error Intercept 104.2460353 P-value 0.00064554 Lower 95% 46.93486971 Gender Numeric Age GPA 3.357643288 -0.518174564 -14.35589635 t Stat Upper 95% 28.47200105 3.661352608 161.5572009 46.93486971 161.5572009 3.578337563 0.938324915 0.352978384 -3.845176644 10.56046322 -3.845176644 10.56046322 1.161691886 -0.446051634 0.657652545 -2.85653905 1.820189921 -2.85653905 1.820189921 Lower 95.0% Upper 95.0% 4.563887699 -3.145541103 0.002904394 -23.54252581 -5.169266883 -23.54252581 -5.169266883

Regression Statistics Multiple R 0.427544459 R Square 0.182794265 Adjusted R Square 0.129498238 Standard Error 12.45749356 Observations 50 ANOVA Regression Residual Total df SS MS F Significance F 3 1596.799298 532.2664326 3.429791628 0.024596922 46 7138.700702 155.1891457 49 8735.5 Coefficients Standard Error Intercept 104.2460353 P-value 0.00064554 Lower 95% 46.93486971 Gender Numeric Age GPA 3.357643288 -0.518174564 -14.35589635 t Stat Upper 95% 28.47200105 3.661352608 161.5572009 46.93486971 161.5572009 3.578337563 0.938324915 0.352978384 -3.845176644 10.56046322 -3.845176644 10.56046322 1.161691886 -0.446051634 0.657652545 -2.85653905 1.820189921 -2.85653905 1.820189921 Lower 95.0% Upper 95.0% 4.563887699 -3.145541103 0.002904394 -23.54252581 -5.169266883 -23.54252581 -5.169266883

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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It appears that there is a significant relationship between cyberbullying (X) and internet trolling (Y). Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .889a .790 .748 1.50555 a. Predictors: (Constant), CyberBullying ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 42.667 1 42.667 18.824 .007b Residual 11.333 5 2.267 Total 54.000 6 a. Dependent Variable: InternetTrolling b. Predictors: (Constant), CyberBullying Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .333 1.639 .203 .847 CyberBullying 1.333 .307 .889 4.339 .007 a. Dependent Variable: InternetTrolling State hypothesis for the correlation: Is the correlation significant using α = .05? State the conclusion in APA format.…
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7204030110.720403011 R Square 0.5189804980.518980498 Adjusted R Square 0.4986557300.498655730 Standard Error 8.5090076558.509007655 Observations 7575 ANOVA dfdf SSSS MSMS F� Significance F� Regression 33 5546.3155546.315 1848.7721848.772 25.534425.5344 2.5815E-112.5815E-11 Residual 7171 5140.6285140.628 72.40372.403 Total 7474 10,686.94310,686.943 Step 1 of 2 : How many independent variables are included in the regression model
Regression: Determine what percentage of variation in College GPA is NOT explained by High School GPA. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square ANOVA Standard Error 0.25 Observations 20 Residual 0.3895 Total 0.1517 Regression 1 Intercept 0.1398 High School GPA 18 19 Coefficients 0.79 0.002 Significance F 2.502556445 2.502556 37.77623 8.36481E-06 $5 MS 1.192443555 0.066247 3.695 Standard Error t Stat P-value 0.348877197 2.393669 0.027785 0.000344183 6.146237 8.36E-06
Which of the following is FALSE? * The regression model assumes the errors (residuals) are normally distributed. Data point below the regression line, the residual is negative. The errors (residuals) in a regression model are assumed to have a zero mean. The errors (residuals) in a regression model are assumed to have increasing mean. In regression analysis, the regression line Minimize the sum of distances between the actual value x and the predicted x values. Minimize the perpendicular distance between the regression line and each data point. Minimize the sum of the squared residuals between the actual x and the predicted x values. Minimize the sum of the squared residuals between the actual y and the predicted y values. The smaller the distance between observed data points and the regression line, the smaller the sum of squares regression (SSR). TRUE FALSE
Regression StatisticsMultiple R 0.890579188 Business Administration Program Final Assessment Semester Spring 2020Page 4R Square 0.793131289Adjusted R Square 0.7379663Standard Error 30.28395534Observations 20ANOVA df SS MS FRegression 4 52743.23074 13185.81 14.37743932Residual 15 13756.76926 917.1179509 Total 19 66500 Coefficients Standard Error t Stat P-valueIntercept 73.33291 62.25276 1.17799 0.25715X1 -0.13882 0.05353 -2.59326 0.02037X2 3.73984 0.95568 3.91328 0.00138X3 0.37644 0.16876 2.23061 0.04140X4 17.70752 15.57847 1.13667 0.27351 a. Write down the fitted multiple regression equation
Write the estimated regression equation from the coefficient output and interpret the results.
Why this type of plot is used in analysing regression models
Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 Question What is the meant by the standard error of the estimate? Which number measures the scatter of points about the regression line?
Interpreting Simple Linear Regression 1. Linear correlation coefficient r = 0.794556 2. Coefficient of Determination ( r square) 0.631319 3. Standard Error of the estimate = 12.9668 4. SSR (Explained variation) = 5182.41 5. SSE ( Unexplained variation) = 3026.49 6. SST = 8208.90 7. Predicted equation or equation of the regression line (Y predicted or hat) = 0.725983X + 16.5523 8. Level of Significance = 0.1 9. Critical Value= 0.378419 Question What is meant by SSR? Which number measures the variation explained by the regression line? Sum of the Squares Regression (SSR); amount of variation in Y explained by the variation in X explanatory variable Sum of the Squares Regression (SSR); amount of variation in X explained by the variation in Y SSR is the percent variation in total variation SST that is explained by the variation in X or SSR/SST = 5182.41/8208.90 = 0.631319 or 63% Both A and C
A corporation owns several companies. The strategic planner for the corporations believes dollars spend on advertising can to some extend be a predictor of total sales dollars. As an aid in long-term planning, she gathers the following sales and advertising information from several of the companies for 2017 ($ millions). Advertising Sales 12.5 148 3.7 55 21.6 338 60.0 994 37.6 541 6.1 89 16.8 126 41.2 379 i) Based on the output given, develop the equation of the simple linear regression line to predict sales from advertising expenditures using this data. ii) Explain the values of r and r. iii) Predict the sales if the advertising expenditures is 50 ($ millions). iv) Do the data support the existence of a linear relationship between advertising expenditures and sales? Test using a = 0.05.
Simple linear regression results: Dependent Variable: Grade (Marks)Independent Variable: AbsencesGrade (Marks) = 89.100971 - 3.1906331 AbsencesSample size: 30R (correlation coefficient) = -0.93164951R-sq = 0.86797081Estimate of error standard deviation: 3.2600128 Parameter estimates: Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 89.100971 1.1464745 ≠ 0 28 77.717357 <0.0001 Slope -3.1906331 0.23516911 ≠ 0 28 -13.567399 <0.0001 Analysis of variance table for regression model: Source DF SS MS F-stat P-value Model 1 1956.2835 1956.2835 184.07431 <0.0001 Error 28 297.57514 10.627684 Total 29 2253.8587 a) Interpret the slope in terms of abscences and grade. b) Does y-intercept have meaning? If so, what is it?
ANOVAS Model Sum of Squares df Mean Square F Sig. .000 Regression 3.999E12 2 1.999E12 2.538E3 Residual 1.920E12 2437 7.877E8 Total 5.919E12 2439 Coefficients" Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta Sig. (Constant) 767.408 1290.261 .595 552 Appraised Land Value 3.192 .053 724 59.777 .000 Appraised Value of Improvements a. Dependent Variable: Sale Price 478 .026 227 18.728 .000 a) Identify the dependent and independent variables. b) State the estimated regression equation of the model. State and interpret the coefficient of correlation. Calculate the coefficient of determination and explain its meaning. e) Identify, with a reason, if there is serial correlation problem in the model. Evaluate the model using a 5% level of significance.