Question

The reasons given by workers for quitting their jobs generally fall into one of two categories: (1) Worker quits to seek or take a different job, or (2) worker quits to withdraw from the labor force. Economic theory suggests that wages and quit rates are related. The table below lists quit rates (quits per 100 cm- ployees) and the average hourly wage in a sample of 15 manufacturing in dustries. A Wor, rates? What does your model suggest about the relationship between quit rates and wages? b) Predict the Quit rate for an average wage of S7.50. c) Make a scatterplot to represent the data. Comment on the relation- ship between Quit Rate, y and Average Wage, x. d) Using excel, find the correlation coefficient, and coefficient of de- termination R2 and interpret cach. e) Construct a 95% confidence interval for the population mean quit rate for the industries. Interpret your result. 1) Write a paragraph stating what the results can be used in the industry. Quay 1 155 153 82 10 64 587 1044 961 11.00 1179 3. 604 3.63 21 699 10 11 12 13 14 15 1235 14 105 1141 93 Use Excel to find the regression equation and the cocfficient of determina- tion: a) Do the data present sufficient evidence to conclude that the average hourly wage contributes useful information for the prediction of quit

Answer 1

SUMMARY OUTPUT Quit Rate Average Wage 1.225 8.7 0.525 10.85 6.68 5.87 2.425 3.225 1.525 1.525 Regression Statistics Multiple R 0.807054633 R Square 0.651337181 Adjusted R Square 0.624516964 Standard Error 0.556267725 Observations 15 10.44 9.61 0.825 11.09 0.325 13.79 1825 8.49 ANOVA 3.625 6.04 df SS MS F Significance F 0.000276001 2.125 8 Regression Residual 7.514694169 24.28530629 0.309433782 1.225 6.93 7.514694169 4.022639164 11.53733333 13 1.225 9.33 Total 14 1.625 11.43 1.825 9.3 Standard Error t Stat P-value * Coefficients 4.682439182 -0.330732975 Intercept Average Wage 0.627606284 7.460790783 4.75961E-06 -4.928012408 0.000276001 Lower 95% Upper 95% 3.326578237 6.038300128 -0.47572148 -0.18574447 0.067112854

a. The p value for significance of regression tedt is less than 0.05. Thus it mau concluded that the dat fitted regression equation is significant.

The equation can be interpreted as for unit increase in average wage the quit rate decreases by 0.3307 units. The maximum possible quit rate for lowest salary is 4.682.

Regression 'Equation Quit Rate 0.3307 Average Wage

b.

Prediction for Quit Rate Regression Equation Quit Rate = 4.682 - 0.3307 Average Wage Variable Average Wage Setting 7.5 Fit SE Fit 95% CI 2.20194 0.179465 (1.81423, 2.58965) 95% PI (0.939204, 3.46468)

c.

scatter plot shows that there is negative relationship between average wages and quit rate. As wages increase quit rate decreases.

16 14 6 12 6 10 3 סחוב עaAE מודה ב0 4 2 0 0 0.5 15 2 2.5 3 2.5 4 Average Wages

d. Coefficient of determination = 0.6513 which can he interpreted as the fitted regression equation explain 65.13% of the variation in the quit rate.

e. 95% CI is provided it excel output above.