In determining the best companies to work for, a number of variables are considered, including size,average annual pay, and turnover rate. Moreover, employee surveys are conducted to assess aspects ofthe organization's culture, such as trust and openness to change. In an attempt to determine what affectsturnover rate, a sample of 33 companies was randomly selected, and data were collected on the averageannual bonus and turnover rate (%) for 2008. In addition, a questionnaire was administered to theemployees of each company to arrive at a trust index (measured on a scale of 0-100). Below are themultiple regression results.Dependent Variable is Turnover RatePredictorCoefSE CoefConstant12.10050.782615.460.000Trust Index-0.071490.01966-3.640.001Average Bonus -0.00072160.0001481-4.870.000S= 1.49746 R-Sq = 79.6% R-Sq (adj) = 78.3% S = 1.49746 R-Sq = 79.6% R-Sq(adj)= 78.3%Analysis of VarianceSourceDFMSRegression262.73131.36Residual Error3067.272.24Total32330.00How much of the variability in Turnover Rate is explained by the estimated multiple regression model?Select one:Oa. 79.6%O b. 2.24%O c. None of the aboved. 12.1%e. 95.4%2.

Answered: In determining the best companies to…

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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The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.987 2.967 2.690 0.010 x1 0.122 0.031 3.920 0.000 x2 − 1.120 0.053 − 2.270 0.028 x3 − 0.063 0.039 − 1.610 0.114 x4 0.523 0.142 3.690 0.001 x5 − 0.065 0.040 − 1.620 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 371000 742 12.89 0.000 Residual Error 46 2647.38 57.55 Total 51 6357.38 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health care–related. Write out the…
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
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per month
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 7.987 2.967 2.690 0.010 x1 0.122 0.031 3.920 0.000 x2 − 1.120 0.053 − 2.270 0.028 x3 − 0.063 0.039 − 1.610 0.114 x4 0.523 0.142 3.690 0.001 x5 − 0.065 0.040 − 1.620 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 371000 742 12.89 0.000 Residual Error 46 2647.38 57.55 Total 51 6357.38 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health care−related. Write out the…
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A researcher's results are shown below using Femlab(labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 US states. What is the R2 for this regression? Source of variation df ss ms f Regression 1 5377.836 5377.836 5.228879 residual 48 49367.389 1028.487 total 49 54745.225
Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…
In the below residual plot, which assumption(s) of regression is/are violated? -4 Residuals -2 0 2 Residuals vs Fitted 00 8080 00 08 810 300 049 -8 -6 -4 -2 -2 0 2 4 6 Fitted values Equal variance across levels of X Normality Residuals are independent Linear relationship between X and Y
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Rewrite the regression model to include coefficients from your regression analysis output and then answer the following question What would be the company's loss if the significant variable(s) change per unit? SUMMARY OUTPUT Regression Statistics Multiple R 0.93082 R Square 0.866425 Adjusted R Square 0.85833 Standard Error 4108.993 Observations 36 ANOVA df SS MS F Significance F Regression 2 3.61E+09 1.81E+09 107.0261 3.75E-15 Residual 33 5.57E+08 16883824 Total 35 4.17E+09 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 3996.678 6603.651 0.605223 0.549171 -9438.55 17431.91 -9438.55 17431.91 X Variable 1 43.5364 3.589484 12.12887 1.05E-13 36.23354 50.83926 36.23354 50.83926 X Variable 2…

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