Consider the following summary output, which was generated from a sample of 8 employees relating age to annual salary.SUMMARY OUTPUTRegression StatisticsMultiple R0.138089232R Square0.019068636Adjusted R Square-0.144419925Standard Error1.46902996Observations8.ANOVAdfSSMSFRegression0.2517060.2520.116775Residual6.12.9482942.158Total13.2wkes Learning ANOVAdfSSMSFRegression10.2517060.2520.116775Residual6.12.9482942.158Total13.2CoefficientsStandard Errort StatP-valueIntercept3.1599591422.657367821.1891310.27931Age0.02267620.0663978940.341520.744355Step 2 of 2: What percentage of the variation in annual salary is explained by the model? Round your answer to two decimal places.

From the given data R^2 = ? R^2 = 0.019068636

Answered: Consider the following summary output,…

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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Last year, TimeWise conducted a study that aimed at predicting the waiting time (in minutes) at the checkout line of the Ranch 99 Supermarket in South Jakarta based on the number of customers in one particular month. Using the simple regression method on a dataset with a sample size of 30, the regression output tables are given below: SUMMARY OUTPUT Regression Statistics Multiple R 0.88728417 R Square 0.7872732 Adjusted R Square 0.77967582 Standard Error 0.59521532 Observations 30 ANOVA MS Significance F Regression 36.7121241 36.7121241 103.6242271 6.48782E-11 Residual 28 9.91987592 0.35428128 Total 29 46.632 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept -0.4479791 0.2782914 -1.6097484 0.118670876 -1.018033233 0.12207495 Customers 0.12847188 0.01262053 10.1795986 6.48782E-11 0.102619905 0.15432385 a. Determine the simple regression model to predict the waiting time b. Interpret the meaning of the regression coefficient of the independent variable c. What is…
**Please complete table**
The results of the regression analysis to estimate setup costs using the number of setup hours are as follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.965127 R Square 0.931469 Adjusted R Square 0.921679 Standard Error 285.6803 Observations 9 ANOVA df SS MS F Regression 1 7765004 7765004 95.14395 Residual 7 571292.5 81613.21 Total 8 8336296 Coefficients Std Error t Stat P-value Intercept 2498.644 680.6304 3.671073 0.007953 X Variable 1 2.506915 0.257009 9.754176 2.52E–05 What percentage change (rounded to one decimal place) in setup costs can be explained by changes in setup hours? a.95.1% b.96.5% c.25.7% d.none of these e.93.1%
A study was conducted to examine how fear of public speaking varies across year of tertiary education (1st, 2nd or 3rd). Subjects were interviewed at the end of each of year of their tertiary education for fear of public speaking (measured on a 10 point metric scale). Which of the following would be an appropriate statistical test to conduct, which addresses these hypotheses? Group of answer choices: Factorial ANOVA Mixed ANOVA Multiple Regression Within Subjects ANOVA Single Factor ANOVA
A business is evaluating their advertising budget, and wishes to determine the relationship between advertising dollars spent and changes in revenue. Below is the output from their regression. SUMMARY OUTPUT Regression Statistics Multiple R 0.95 R Square 0.90 Adjusted R Square 0.82 Standard Error 0.82 Observations 8 ANOVA df SS MS F Significance F Regression 3 23.188 7.729 11.505 0.020 Residual 4 2.687 0.672 Total 7 25.875 Coefficients Std Error t Stat P-value Lower 95% Upper 95% Intercept 83.91 2.03 41.36 0.00 78.28 89.54 TV ($k) 1.96 0.48 4.10 0.01…
Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F 492.357679 11.531217 42.697806 Significance F 0.006092 Copy Data Ĵ Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F Significance F 492.357679 11.531217 0.006092 42.697806 Copy Data
Given the following ANOVA table: Degrees Mean Source of of Variance Sum of Sum of F-stat Squares Freedom Squares | Regression 2 5.5 Residual 27 2.507 Total What is the value of SST? Please report your answer in 3 decimal places.
A car dealership would like to develop a regression model that would predict the number of cars sold per month by a dealership employee based on theemployee's number of years of sales experience. The accompanying regression output was developed based on a random sample of employees. ANOVA df SS Regression 1 79.909407 Residual 23 261.210593 Total 24 341.12 Coefficients Standard Error Intercept 7.271539 1.229763 Slope 0.539854 0.203521 The coefficient of determination is 0.234 Test statistic= 0.704 P-value= 0.014 Construct a 95% confidence interval around the sample slope and interpret its meaning. The confidence interval is (__________,_________). (Type an integer or decimal rounded to three decimal places as needed.)
Only round final answer to 2 decimal places.
An analysis is conducted to compare mean time to pain relief (measured in minutes) under four competing treatment regimens. Summary statistics on the four treatments are shown below. Complete the following ANOVA Table. Source of Variation SS df MS F Between Groups Within Groups 3719.48 Total
A multiple regression analysis produced the following tables. Summary Output Regression Statistics Multiple R 0.978724022 R Square 0.957900711 Adjusted R Square 0.952287472 Standard Error 67.67055418 Observations 18 ANOVA df SS MS F Significance F Regression 2 1562918.941 781459.5 170.6503 4.80907E-11 Residual 15 68689.55855 4579.304 Total 17 1631608.5 Coefficients Standard Error t Stat P-value Intercept 1959.709718 306.4905312 6.39403 1.21E-05 X1 -0.469657287 0.264557168 -1.77526 0.096144 X2 -2.163344882 0.278361425 -7.77171 1.23E-06 Using α = 0.01 to test the…

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