Brennan Ross Problem Set 1

Employee Hours of Overtime Days Absent 1 243 3 2 126 2 3 86 0 4 424 6 5 236 3 6 128 0 7 0 0 8 126 2 9 324 3 10 118 0 11 62 0 12 128 3 13 460 6 14 135 1 15 118 1 16 260 2 17 0 1 18 126 1 19 234 2 20 246 3 21 120 1 22 80 0 Bin Widt 23 112 1 Number 24 237 3 25 129 2 26 24 1 27 36 0 28 128 2 29 246 3 30 326 6 a. Develop tw distributed? b. Develop a appear to be This chart Editing th permanen a. Deve data ap Based o skewed seeing i

0 0 1 2 3 4 5 6 7 Days Absent b. Deve hours a Based o correlat strong p Additio regress that as to incre

Summary Statistics Hours OT Mean 167.26666667 Standard Error 20.996328816 Median 128 Mode 126 Standard Deviation 115.00162917 Sample Variance 13225.374713 Kurtosis 0.4772358947 Skewness 0.8526325958 Range 460 Minimum 0 Maximum 460 Sum 5018 Count 30 th = Range / Number of Bins = 460/5 = 92 r of Bins = log (n) / log (2) = log (30) / log (2) = 5 wo separate histograms for hours of overtime and days absent. How do the data app a scatter plot of hours of overtime versus days absent. Do the data overtime hours and e correlated? t isn't available in your version of Excel. his shape or saving this workbook into a different file format will ntly break the chart. elop two separate histograms for hours of overtime and days absent. How do the ppear to be distributed? on the data shown in the histogram, it appears to be distributed with a right dness, also known as a positively skewed histogram. This can be further verified by if the Mean > Median > Mode, which it is 167 > 128 > 126.

50 100 150 200 250 300 350 400 450 500 f(x) = 0.013504690912871 x − 0.325551300026178 R² = 0.77835307944634 Days Absent Hours of Overtime elop a scatter plot of hours of overtime versus days absent. Do the data overtime and days absent appear to be correlated? on the scatter plot of hours of overtime versus days absent, the data appears to be ted. This is supported by seeing that the correlation coefficient is 0.88, indicating a positive relationship between the hours of overtime and days absent variables. onally, the R Squared valued of 0.78 indicates that the hours of overtime in the sion model explains about 78% of the variance for days absent. This data suggests the number of hours of overtime increases, the number of days absent also tends ease, and vice versa.