4. Interpret the excel regression results below.SUMMARY OUTPUTRegression StatisticsMultiple RR Square0.9161126540.839262395Adjusted R SquareStandard E ror0.8337197180.193622646Observations31ANOVASignificance F4.91029E-13dfMSFRegression15.6766297345.676629734 151.4182656Residual291.0872021390.037489729Total306.763831874CoefficientsStandard Erort StatP-valueLower 95%Upper 95%InterceptIn P8.440.4761369217.72316294 4.22539E -177.464842337 9.412462084-1.580.128360421 -12.30521294 4.91029E-13-1.842028993 -1.316975632The regression is on daily ticket sales data, with varying prices. Natural logarithms were taken of boththe number of tickets sold each day (dependent variable) and the price (independent variable) beforerunning the regression. Give specific responses to the following: What is the effect of a 1% increase inprice on ticket sales? Is the estimated effect statistically significant? How much of the total variation inInQ is explained by InP?

4. The regression output is given.

Answered: 4. Interpret the excel regression…

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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Similar questions

Perform a regression on two different variables within the attached data, and exaplin what the findings determine. What decision can be made?
The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 33 18.154037618.1540376 6.051345876.05134587 17.6080562317.60805623 4.3383E-034.3383E-03 Residual 55 1.7183457921.718345792 0.343669160.34366916 Total 88 19.8723833919.87238339 Step 2 of 3 : What is the adjusted coefficient of determination for this model, R2a��2? Round your answer to four decimal places
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73650.7365 R Square 0.54240.5424 Adjusted R Square 0.52250.5225 Standard Error 2124.60962124.6096 Observations 4949 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 246,127,958.1791246,127,958.1791 123,063,979.0896123,063,979.0896 27.262927.2629 1.6E-081.6E-08 Residual 4646 207,642,442.8821207,642,442.8821 4,513,966.14964,513,966.1496 Total 4848 453,770,401.0612453,770,401.0612 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14256.268814256.2688 2,513.30952,513.3095 5.67235.6723 0.0000008950.000000895 9197.23929197.2392 19,315.298419,315.2984 Education (Years) 2353.85412353.8541 336.0719336.0719 7.00407.0040 0.0000000090.000000009 1677.37651677.3765 3030.33173030.3317 Experience (Years) 832.8371832.8371…
The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 20.2147598620.21475986 20.2147598620.21475986 20.9811354520.98113545 0.0013268390.001326839 Residual 99 8.6712580048.671258004 0.963473110.96347311 Total 1010 28.8860178628.88601786 Step 1 of 3: What is the coefficient of determination for this model, R2�2? Round your answer to four decimal places.
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 2020 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 1 of 2: How many independent variables are included in the regression model
Using the Excel output reported above, if we were to test to see whether "Attendance" is statistically significantly associated with "Score received on the exam," we would conclude that we should Regression Statistics Multiple R R Square Standard Error Observations Intercept Attendance 0.142620229 0.02034053 20.25979924 22 Coefficients Standard Error 39.39027309 37.24347659 0.340583573 0.52852452 T Stat 1.057642216 0.644404489 P-value 0.302826622 0.526635689 O Reject the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Reject the null hypothesis and conclude that Attendance is is NOT statistically significantly associated with "Score received on the exam" Accept the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Fail to reject the null hypothesis and conclude that we cannot say that Attendance is statistically significantly associated with…
The owner of a new pizzeria in town wants to study the relationship between weekly revenue and advertising expenditures. All measures are recorded in thousands of dollars. The summary output for the regression model is given below.ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 20.2147598620.21475986 20.2147598620.21475986 20.9811354520.98113545 0.0013268390.001326839 Residual 99 8.6712580048.671258004 0.963473110.96347311 Total 1010 28.8860178628.88601786 Step 3 of 3: Which statistic is most appropriate for the pizzeria owner to determine the usefulness of the regression model and why?
B. Would the Floor Area be a significant predictor of Assessed Value?
The following regression output can be used to predict the weight of a Shetland pony based on the animal's age: Regression Statistics Multiple R 0.97224608 R Square 0.94526245 Adjusted R Square 0.9270166 Standard Error 14.0700917 Observations Intercept Age Coefficients Standard Error 55.7 tStat 12.08558531 P-value 4.61142 0.01918 7.19771 0.00553 5.9 0.818914857 True or false? The pony's weight is related to its age at the .01 level of significance. A True B) False
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7357 R Square 0.5412 Adjusted R Square 0.5213 Standard Error 2128.8575 Observations 49 ANOVA df SS MS F Significance F 122,948,379.2778 27.1287 1.7E-08 Regression 2 245,896,758.5555 Total Residual 46 46 208,473,570.9955 48 454,370,329.5510 4,532,034.1521 Coefficients Standard Error t Stat P-value Lower 95 % Upper 95 % Intercept Education (Years) 14265.71682 2352.8476 2,518.3346 Experience (Years) 832.0973 336.7438 390.9718 5.6647 0.000000918 6.9871 0.00000001 2.1283 0.038700983 9196.5722 19,334.8615 1675.0175 3030.6777 45.1119 1619.0827 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
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.
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Multiple R Regression Statistics 0.7357 R Square 0.5412 Adjusted R Square 0.5213 Standard Error 2128.8575 Observations 49 ANOVA df SS Regression 2 245,896,758.5555 MS 122,948,379.2778 27.1287 F Significance F 1.7E-08 Total Residual 46 208,473,570.9955 48 454,370,329.5510 4,532,034.1521 Coefficients Standard Error t Stat Intercept Education (Years) 14265.71682 2352.8476 Experience (Years) 832.0973 2,518.3346 336.7438 390.9718 P-value 5.6647 0.000000918 6.9871 0.00000001 2.1283 0.038700983 Lower 95% Upper 95 % 9196.5722 19,334.8615 1675.0175 3030.6777 45.1119 1619.0827 Step 1 of 2: What would be your expected salary with no education and no experience?

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