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
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
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
Are the heights of individuals affected by the heights of their parents. Regression Statistics Multiple R R Square Adjusted R Squar 0.631071992 0.398251859 0.365724932 Standard Error 2.914527039 Observations 40 ANOVA df MS F Significance F Regression Residual 2 208.0084392 104.0042 12.24376 8.30181E-05 37 314.2953108 8.494468 Total 39 522.30375 Coefficients Standard Error t Stat P-value Intercept Mother's Height Father's Height 9.804326378 12.39987353 0.79068 0.43417 0.657952815 0.147476295 4.461414 7.34E-05 0.200358437 0.138223638 1.449524 0.155615 1. Write the regression equation that represents the above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? 3. Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.
Multiple regression analysis was used to study the relationship between a dependent variable, y, and four independent variables; x1, x2, x3, and x4. The following is a partial result of the regression analysis involving 31 observations. Coefficients Standard Error Intercept 18.00 6.00 x1 12.00 8.00 x2 24.00 48.00 x3 -36.00 36.00 x4 16.00 2.00 ANOVA df SS MS F Regression 125 Error Total 760 Compute the multiple coefficient of determination. Perform a t test and determine whether or not β1 is significantly different from zero (α = .05). Perform a t test and determine whether or not β4 is significantly different from zero (α = .05). At α = .05, perform an F test and determine whether or not the regression model is significant.
Waterbury Insurance Company wants to study the relationship between the amount of fire damage and the distance between the burning house and the neorest fire station. This information will be used in setting rates for insurance coverage. For o somple of 30 claims for the last year, the director of the actuarial department determined the distance from the fire station (x) and the amount of fire domage, in thousands of dollars (y). ANOVA table Source Regression Residual SS df MS F 1,870.5782 1,870.5782 41.39 1,265.4934 3,136.0716 28 45.1962 Total 29 Regression output Standard Variables Coefficients Error t(df-28) Intercept Distance-X 13.76815 3.106 2.914 3.77es e. 5861 6.43 Click here for the Excel Data File a-1. Determine the regression equation. (Round your answers to 3 decimal places.) y3D X. a-2 Is there a direct or indirect relotionship between the distance from the fire station and the amount of fire damage? The relationship between distance and damage is b. How much domage would…
ANOVA df SS MS F Significance F Regression 1 7195512474 7195512474 6.504373807 0.012309437 Residual 98 1.08413E+11 1106257526 Total 99 1.15609E+11 Please explain this...
A regression model relating , number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following regression output. Where ntotal = 34. ANOVA df SS MS F Significance F Regression 6,971.9 Residual Total 9.108.7 Coefficients Standard Error t Stat P-value Intercept 82.0 10.894 Number of 45.0 5.730 Salespersons a. Write the estimated regression equation (to whole number). b. Compute the F statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) F-value D-value is - Select your answer - we Select your answer - V Ho c. Compute the t statistic and test the significance of the relationship at a 0.05 level of significance. (to 2 decimals) t Stat p-value is Select your answer - , we v Ho : B1 = Select your answer- d. Predict the annual sales at the Memphis branch office. This branch employs 10 salespersons. thousand (to whole number)
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salespers 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. The 95% prediction interval for a particular person making 30 calls is