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.…
Model Summary Adjusted R Square Std. Error of Model R R Square the Estimate 1 .772 .596 .516 215.1509 a. Predictors: (Constant), Porosity ANOVA Sum of Model Squares df Mean Square Sig. Regression 341943.282 .042 341943.282 7.387 Residual 231449.575 46289.915 Total 573392.857 a. Dependent Variable: Runoff b. Predictors: (Constant), Porosity Coefficients Standardized Unstandardized Coefficients Coefficients В Sig. Model Std. Error Beta t 1 (Constant) 1863.761 237.546 7.846 .001 Porosity -80.729 29.703 -.772 -2.718 .042 a. Dependent Variable: Runoff A geomorphologist is building a model to understand variation in the amount of runoff observed in various streams within a large drainage basin. Linear regression is used to determine whether runoff is inversely related to the porosity of the soil. The variables here are thus runoff and porosity. The SPSS output from the analysis is shown above. Use the output to identify what percentage in of the variation in runoff can be explained by…
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
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
Proposed regression line: y ̂=5.4+(2.6)x n=4 Sum of squared deviations: ∑(??−?ℎ???)2∑(yi−yhati)2 = 25.2 Sums of squares: ∑(??−????????)2∑(yi−averagey)2 = 59 ∑?2?−?(????????)2∑xi2−n(averagex)2 = 5 Questions: (Enter answers rounded to one decimal place. If appropriate, include a zero before the decimal point) 1. What is ???Syx? 2. What is ??1Sb1? 3. What is SST? 4. What is SSE? 5. What is ?2R2?
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.
Regression Statistics Multiple R0.9101558 R Square0.8283837 Adjusted R Square0.7971807 Standard Error1.6929902 Observations14 ANOVA dfSSMS F Significance F Regression2152.185976.0929626.548236.167E-05 Residual1131.528372.866216 Total13183.7143 Coefficients Standard Error t Stat P-value Lower 95%Upper 95%Intercept87.8823673.06924128.633261.11E-1181.127012794.63772X10.37987660.0901314.214702 0.0014490.181498950.578254X20.22160510.0384425.764660.0001260.136994830.306215 Based on the F test, the model is good? Use α = .05. Yes No
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