- Windingspeeds Num Breaks Winding Speed 1 1 1 1 1 1 1 1 1 4 3 2 3 4 4 3 6 10 5 4 2 4 4 2 32 4 7 6 4 6 7 2 9 5 5 9 1 1 1 1 (14.04 KiB) Viewed 8 times
- Windingspeeds Num Breaks Winding Speed 1 1 1 1 1 1 1 1 1 4 3 2 3 4 4 3 6 10 5 4 2 4 4 2 32 4 7 6 4 6 7 2 9 5 5 9 1 1 1 2 (70.02 KiB) Viewed 8 times
WindingSpeeds num breaks winding speed 1 1 1 1 1 1 1 1 1 4 3 2 3₂ 4 4 3 6 10 5 4 2 4 4 2 32 4 7 6 4 6 7 2 9 5 5 9 1 1 1 1 1 1 1 2 2 2 2 19 2 2 2 2 19 2 19 2
3 8 6 4 7 6 12 6 14 12 10 9 12 17 7 6 12 11 6 13 10 14 17 15 7 20 13 11 16 25 11 24 18 21 16 19 2 2 2 19 2 2 2 3 3 3 3 3 3 3 3 3 3₂ 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4
9 23 4 4
4. In a completely randomized design to study the effect of the speed of winding thread (1:slow, 2:normal, 3:fast, 4:maximum) onto 75-yard spools, 16 runs of 10,000 spools each were made at each of the four winding speeds. The response variable is the number of thread breaks during the production run. The data is in the file WindingSpeeds.csv. (a) Import the data and display the structure of the dataframe. Use the function recode from the car package to recode the values in column i, which is the explanatory variable. Here is a sample command. I am assuming that the name of the dataframe is data. library (car) data$i<-factor (recode (data$i, "1='Slow'; 2= 'Normal'; 3= 'Fast'; 4= 'Maximum'")) Verify that i is now a factor and display its levels, which should be "Slow", "Normal", "Fast", "Maximum". (b) Produce comparative boxplots to describe the number of thread breaks according to the winding speed. Based on the plots, is it reasonable to assume that the variance of the random error is constant? (c) Perform the modified Levene test at a = 5. Does it support your findings from part (b). (d) Fit a log-log model to describe the association between the group standard deviation and the group mean. Give a 95% confidence interval for the slope. Does it suggest that a log transformation of the response might be useful for stabilizing the variance.