DATA: day count2011 count2012 1 29 17 2 40 23 3 28 16 4 17 20 5 15 13 6 21 13 7 24 9 8 15 10 9 32 27 10 22 12 11 24 7 12 11 11 13 27 15 14 10 19 15 14 19 16 18 9 17 26 21 18 38 22 19 31 23 20 12 14 21 8 19 22 22 15 23 17 13 24 31 22 25 49 42 26 23 29 27 14 21 28 25 12 29 24 16 30 37 29 31 32 17 32 25 17 33 20 15 34 40 25 35 21 9 36 18 16 37 35 25 38 21 25 39 25 16 40 34 22 41 42 21 42 27 17 43 34 26 44 47 41 45 36 25 46 15 12 47 26 17 48 21 24 49 39 26 50 39 16 51 21 15 52 15 12 53 17 22 54 20 24 55 24 16 56 30 25 57 25 14 58 8 15 59 21 9 Researchers investigated the effect of enforcing speed limits on reducing the number of car accidents. The researchers monitored multiple randomly selected roads in suburban cities in California and counted the total number of car accidents per day. The researchers randomly selected 59 days in 2011 and selected 59 days in 2012 which are similar to the days selected in 2011 (in terms of days, months, and weather). During 2011, the speed limits were not enforced on the selected roads. During 2012, the speed limits were enforced on the selected roads. Read the data slim.csv and name the dataframe as "slim" as follows. The first row should be interpreted as follows. On the first selected day in 2011, there were a total of 29 accidents in the selected roads. In the next year (2012) of the similar day, there were a total of 17 accidents on the same roads. > slim = read.csv( "slim.csv" ) ### read the dataz > head(slim) ### view the first six lines day count2011 count2012 1 1 29 17 2 2 40 23 3 3 28 16 4 4 17 20 5 5 15 13 6 6 21 13 > ... ### to be completed in Question 1 > diff [1] -12 -17 -12 3 -2 -8 -15 -5 -5 -10 -17 0 -12 9 5 -9 -5 -16 -8 [20] 2 11 -7 -4 -9 -7 6 7 -13 -8 -8 -15 -8 -5 -15 -12 -2 -10 4 [39] -9 -12 -21 -10 -8 -6 -11 -3 -9 3 -13 -23 -6 -3 5 4 -8 -5 -11 [58] 7 -12 Write a single line of code which creates a new vector diff (the difference in the total number of accidents per day; the count in 2012 minus the count in 2011). State the null hypothesis in words. 3. Perform the hypothesis testing, and fill in the blanks below. ... t= , df= , p-value = e ... Calculate the 99% confidence interval (CI), and fill in the blanks of the R output below. [1] attr(,"conf.level") [1] Interpret the 99% CI in the context of the problem Do you think the normality assumption (or at least a symmetric distribution) is satisfied? Justify. Specify the conditions and explain whether each condition is met. At the significance level 0.01, state the general conclusion including (1) one sentence about the statistical significance, (2) one sentence for another piece of supporting evidence (e.g., other descriptive statistics, graphics), and (3) one sentence about the overall conclusion (including the practical significance and limitations). Your writing must have this format: 1) One sentence .... 2) One sentence .... 3) One sentence