STAT_600_3T1_Group project Mengyao Wang(943288) Ruifeng Li(945750) Qinchuan Dai(895510 ) Kailai Sheng (943287)
1. Shown below is Minitab output for correlation and regression analysis for the education and sick day data. The correlation between hours of education and number of sick days is an electively strong negative correlation. This indicates that the more hours of education received the fewer the sick days. There may be several reasons for this. One explanation may be that as workers participate in an educational process about their work, they become more interested in what they are doing because they understand more about it and can see more potential. If the training is interesting, they may look forward to the time in the classroom. In addition, they may feel more a part of the team by having been included in training and feel more self-worth for being selected for the training.

Correlation of HrsEduc and SickDays = 0.773 The regression equation is SickDays = 7.75 0.0795 HrsEduc Predictor Constant HrsEduc s = 2.455 Coef 7.7455 0.07946 Stdev 0.7980 0.01536 t ratio 9.71 5.17 p 0.000 0.000

R sq = 59.8%

R sq(adj) = 57.5%

Analysis of Variance SOURCE Regression Error Total DF 1 18 19 SS 161.26 108.49 269.75 MS 161.26 6.03 F p 26.75 0.000

The F test for the overall model is significant at .001 as is the t value testing the slope. The r2 of .598 indicates that almost 60% of the variation of the sick days is accounted for by the hours of education. The standard error of the estimate is 2.455 days which is a modest error. The regression equation has an intercept of 7.75 which indicates that the model predicts that a worker will have an average of 7.75 sick days if the worker has participated in no hours of education. Notice the negative slope. As the number of hours of education increase, the regression model deducts days from the intercept resulting