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EXERCISING ESSENTIAL STATISTICS 1. The department of transportation in a state government range for each office. Discuss the meaning of each of these performance measures for the offices. has four vehicle registration offices in a metropolitan area. The department recently conducted a series of studies to determine the number of mistakes made in issuing or renewing drivers licenses (an outcome measure). Twelve tests were conducted in each of these four offices, and the results are shown in Tablew9 Table w9.1 -Number of Errors in Vehicle License Offices (per 100 Licenses Issued) Office A Office B Office C Office D 3.00 7.00 8.00 5.00 5.00 5.00 1.00 9.00 10.00 5.00 3.00 4.00 9.00 Test I Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Test 9 Test 10 Test 11 Test 12 3.00 2.00 5.00 2.00 9.00 6.00 4.00 8.00 2.00 2.00 9.00 9.00 7.00 6.00 4.00 1.00 2.00 9.00 10.00 4.00 1.00 2.00 2.00 2.00 2.00 10.00 5.00 6.00 7.00 7.00 8.00 2 Which office has the best performance, based on these measures? Which has the worst? What makes you draw these conclusions? If the state decides to retrain offices one at a time, what would be your priority list for retraining? Explain your rationale for creating this list. Calculate the mean, the sample variance, the sample standard deviation, the maximum, the minimum, and the

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Answer #1

1) Answer:- using R-Software (with r-codes and conclusion) >n 12 > a-c(3,7,8,5,5,5,1,9,1 0,5,3,4) #no. of errors in vehicle license issued by office A > b-c(6,3,2,5,2,9,6,4,8,2,2,1 ) #no. of errors in vehicle license issued by office B > c-c(9,4,2,2,6,2,1 0,5,6,7,7,8) #no. of errors in vehicle license issued by office C > d-c(9,9,7,6,4,1,2,9,1 0,4,1,2) #no. of errors in vehicle license issued by office D #no. of tests mean c(mean(a),mean(b),mean(c),mean(d)) mean #mean [1] 5.416667 4.166667 5.666667 5.333333 >#conclusion :-average no. of errors in vehicle license issued by office A,B,C,D are 5.416667, 4.166667, 5.666667, 5.333333 respectively >v-c(var(a),var(b),var(c),var(d) > V 1] 6.992424 6.878788 7.515152 11.696970 >variance (n-1)/n)*v > variance #variance 1] 6.409722 6.305556 6.888889 10.722222 >#conclusion-variability between no. of errors in vehicle license issued by office A,B,C,D are 6.409722,6.305556, 6.888889, 10.722222 respectively >standarddeviation sqrt(variance) > standarddeviation #standard deviation 1 2.531743 2.511087 2.624669 3.274480 >#conclusion :-average distance between the no. of errors in vehicle license issued by office A,B,C,D and its corresponding mean are 2.531743, 2.511087, 2.624669, 3.274480 respectively minimum-c(min(a),min(b),min(c),min(d) > minimum [1] 1 121 >#conclusion :-minimum no. of errors in vehicle license issued by office A,B,C,D are 1,1,2,1 #mínimum respectively > maximum c(max(a),max(b),max(c),max(d)) > maximum 1] 10 9 10 10 >#conclusion-maximum no. of errors in vehicle license issued by office A,B,C,D 109,10,10 #maximumrespectively >range maximum-minimum > range [1]9 889 #range >#conclusion :-difference between maximum no. of errors in vehicle license issued by office A,B,C,D and minimum no. of errors in vehicle license issued by office A,B,C,D are 9,8,8,9 respectively. Q2) Answer- i) average no. of errors in vehicle license issued by office B is small as compare to all other offices, therefore office B has a best performance. ii) average no. of errors in vehicle license issued by office C is large as compare to all other offices, therefore office C is worst. ii) if the state decides to retain offices one at a time then we just combined the data of all offices and then we do the further analysis of combined data. >z-c(a,b,c,d)z (r-code for combined data).

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