There is a dataset named xi with two variables i, and x. For the below data step, what's the 2nd BY group? data x2; set xi; by i; run; 1 2 3 4 5 O 6 7 O obs 1-2 O obs 3-4 obs 6-7 obs 5-7 8 8 9 9 10 10 11 SSFFR22 10 10 11 11 12 12 12

Here is a set of data. 6 7 260 301 338 417 504 623 649 714 867 907 Identify the 5 number summary (min, Q1, median, Q3, max)  , , , ,

Assitance with the following please. Using the information given in the image. c.  Compare the variation in the number of driving citations and the annual income. Explain why you choose this method to compare the variations?

Typed please and asap

The basketball coach at a local college believes that his team scores more points at home games when more people show up. Below is a list of all home games last year with scores and corresponding attendance. Use Excel, SPSS, or work by hand to show your work finding r. Show your work on the attached pages. Score Attendance   Score Attendance 54 380   67 410 57 350   78 215 59 320   67 113 80 478   56 250 82 451   85 450 75 250   101 489 73 489   99 472 53 451       What is the obtained t-score for this correlation? What is the critical t-score for a two-tailed test with a = 0.05? Is this correlation significant based on the t-scores?

What percentage of data would you predict would be between 25 and 50 and what percentage would you predict would be more than 50 miles? Use the Week 4 spreadsheet again to find the percentage of the data set we expect to have values between 25 and 50 as well as for more than 50. Now determine the percentage of data points in the dataset that fall within each of these ranges, using same strategy as above for counting data points in the data set. How do each of these compare with your prediction and why is there a difference? Predicted percentage between 25 and 50: ________________________ Actual percentage: Predicted percentage more than 50 miles: Actual percentage: ___________________________________________ Comparison ____________________________________________________ Why? __________________________________________________________ Drive: 44 20 88 6 71 42 76 63 61 63 84 28 55 33 88 80 86 83 5 85 25 25 54 54 81 73 29 76 78…

Suppose you have access to a database with the variables listed below. Choose two variables you are interested in exploring that may be related (a predictor and outcome). Which variable is your y (outcome) and which is your x (predictor)?  *Hint: which one may predict the other? Example: hot weather may predict sunburns.

Here is a set of data. 27 231 276 283 299 308 321 407 567 584 587 625 737 830 966 974 Identify the 5 number summary (min, Q1, median, Q3, max)

Below is data for two variables, X and Y.  Answer the questions that follow.  X Y X-MX Y-MY (X-MX)2 (Y-MY)2 (X-MX)(Y-MY) 3 3 -2 -1 4 1 2 4 3 -1 -1 1 1 ___ 6 4 1 ___ 1 0 0 7 6 2 2 ___ 4 4   r = ____

−13,−13,−13,0,0,0,11−13,−13,−13,0,0,0,11 Determine if the data set is unimodal, bimodal, multimodal, or has no mode. Identify the mode(s), if any exist.