Please use the below table to solve question with program R. I have included both stacked and unstacked datasets, please use whichever is easier.

\fRefer to the data set on steel ball bearings. Hand et al. describe the data set as follows: \"Production lines in a large industrial corporation are set to produce a specic type of ball bearing with a diameter of 1 micron. At the end of a day's production, ten ball bearings were randomly picked om the production line and their diameters measured. In a second experiment ten ball hearings were selected from a different production line.\" (a) (b) (C) Test the hypothesis that the (population) means for the ball bearing diameters differ between the two production lines, using a 5% level of signicance. Be sure to include all steps of the hypothesis test, including stating your null and alternative hypotheses precisely in terms of population parameters. Also show the substitutions into the appropriate test statistic as found on \"Hypothesis Tests and C13\" (posted on CourseLink). Assume equal population variances; show how the pooled sample variance is calculated. Verify your results in (a) by using the t.test() function in R. [Quick-R provides a good summary on the use of this function] Remember that equal population variances were assumed in (a); this is not the default in R, but you can specify equal variances as an option. Report the p-value for this test; is the p-value consistent with your conclusions in (a)? Conduct the test of (b) but without assuming equal population variances. This \"approximate\" t-test is the default option in R. How do the values of the test statistics, degrees of freedom and p-values compare between (b) and (c)? Do any conclusions change