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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A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7193267530.719326753 R Square 0.5174309770.517430977 Adjusted R Square 0.4991055710.499105571 Standard Error 8.6995238488.699523848 Observations 165165 ANOVA dfdf SSSS MSMS F� Significance F� Regression 66 12,821.56512,821.565 2136.9282136.928 28.235728.2357 8.9037E-238.9037E-23 Residual 158158 11,957.71111,957.711 75.68275.682 Total 164164 24,779.27624,779.276 Step 1 of 2 : How many independent variables are included in the regression model
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.…
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
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
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?
I have no idea how to fill in the blank in this regression output... please help
Average Salaries (Y)| Standard Deviation (sy)| Sample Size Non-college grads College grads 2890 520 127 5877 720 123 (a) Write down the estimated regression line using the non-college grads as the benchmark group. Make sure you calculate the estimated coefficients and standard error. (b) Write down the estimated regression line using the college grads as the benchmark group. Make sure you calculate the estimated coefficients and standard error.
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.