Below is the output of a regression model where Standby hours is a dependent variable with...
Below is the output of a regression model where Standby hours is a dependent variable with 0.05 alpha.
Coefficients: Estimate Std. Error t value Pr(>|t|)
(Intercept) -364.37136 129.08862 -2.823 0.0113
Total.Staff 1.33524 0.47955 2.784 0.0122
Remote -0.11447 0.06024 -1.900 0.0235
Total.Labor 0.13480 0.07041 1.914 0.0716
Overtime 0.59979 1.21246 0.495 0.6268
The coefficient of Remote is -0.114. Which one is the correct interpretation?
| a. |
If Standby hour is up by 1 hour, Remote hours is down by 0.114 hours. |
|
| b. |
If Remote hour is up by 1 hour, mean Standby hours is down by 0.114 hours. |
|
| c. |
If Standby hour is up by 1 hour, mean Remote hours is down by 0.114 hours. |
|
| d. |
If Remote hour is up by 1 hour, mean Standby hours is up by 0.114 hours. |
|
| e. |
If Remote hour is up by 1 hour, Standby hours is down by 0.114 hours. |
Homework Answers
The correct interpretation is:
If Remote hour is up by 1 hour, Standby hours is down by 0.114 hours.
Option E is correct.
Add Answer to:
Below is the output of a regression model where Standby hours is
a dependent variable with...
Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Warranty_Yearsb . Enter a. Dependent Variable:...
Regression Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Warranty_Yearsb . Enter a. Dependent Variable: Number_of_people_mentioned b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .503a .253 .251 .95930 a. Predictors: (Constant), Warranty_Years ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 80.590 1 80.590 87.574 .000b Residual 237.425 258 .920 Total 318.015 259 a. Dependent Variable: Number_of_people_mentioned b. Predictors: (Constant), Warranty_Years Coefficientsa Model Unstandardized...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept)...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept) -5.128e+03 1.103e+02 46.49 2e-16** Estimate std. Brror t value Pr(lt|) TEMP PERT TEM: FERT 1.45se-01 9.692e-03 -15.01 1.06e-12 3.110e+01 1.344e+00 23.13 2e-16* 1.397e+02 3.140e+00 44.51 < 2e-16** TEMPSQ FERTSO -1.334e-01 6.853e-03 19.46 6.46e-15 -1.144e+00 2.741e-02 41.74 <2e-16 signif. codes: 00.001 0.01 0.05 011 Residual standard error: 1.679 on 21 degrees of freedom Multiple R-squared: 0.993, F-statistic: 596.3 on 5 and 21 DF, p-value: 2.2e-16...
R is a little difficult for me, please answer if you can interpret the R code, I want to learn better how to interpret the R code 4. each 2 pts] Below is the R output for a simple linear regression m...
R is a little difficult for me, please answer if you can
interpret the R code, I want to learn better how to interpret the R
code
4. each 2 pts] Below is the R output for a simple linear regression model Coefficients: Estimate Std. Error t value Pr(>t) (Intercept) 77.863 4.199 18.544 3.54e-13 3.485 3.386 0.00329* 11.801 Signif. codes: 0 0.0010.010.05 0.11 Residual standard error: 3.597 on 18 degrees of freedom Multiple R-squared: 0.3891, Adjusted R-squared: 0.3552 F-statistic: 11.47...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept) -5.128e+03 1.103e+02 46.49 2e-16** Estimate std. Brror t value Pr(lt|) TEMP PERT TEM: FERT 1.45se-01 9.692e-03 -15.01 1.06e-12 3.110e+01 1.344e+00 23.13 2e-16* 1.397e+02 3.140e+00 44.51 < 2e-16** TEMPSQ FERTSO -1.334e-01 6.853e-03 19.46 6.46e-15 -1.144e+00 2.741e-02 41.74 <2e-16 signif. codes: 00.001 0.01 0.05 011 Residual standard error: 1.679 on 21 degrees of freedom Multiple R-squared: 0.993, F-statistic: 596.3 on 5 and 21 DF, p-value: 2.2e-16...
R is a little difficult for me, please answer if you can
interpret the R code, I want to learn better how to interpret the R
code
4. each 2 pts] Below is the R output for a simple linear regression model Coefficients: Estimate Std. Error t value Pr(>t) (Intercept) 77.863 4.199 18.544 3.54e-13 3.485 3.386 0.00329* 11.801 Signif. codes: 0 0.0010.010.05 0.11 Residual standard error: 3.597 on 18 degrees of freedom Multiple R-squared: 0.3891, Adjusted R-squared: 0.3552 F-statistic: 11.47...
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