Hamilton County judges try thousands of cases per year. In an overwhelming majority of the cases disposed, the verdict stands as rendered. However, some cases are appealed, and of those appealed, some of the cases are reversed. Kristen DelGuzzi of the Cincinati Enquirer conducted a study of the cases handled by Hamilton County judges over a three year period. See Excel Data file below. The purpose of the newspaper's study was to evaluate the performance of the judges. Appeals are often the result of mistakes made by the judges and the newspaper wanted to know which judges were doing a good job and which were making too many mistakes. You are called in to assist in the data analysis. Use your knowledge of probability and conditional probability to help with the ranking of the judges. You are also to analyze the likelihood of appeal and reversal for cases handled by different courts. You are responsible to hand in a report analyzing the data in the analysis, you should make some conclusions regarding each judge such as: Who is doing a good job and who is not? Why do you think so Remember you have been hired to analyze the data, and you must report to the person who hired you. You may use graphs, statistical information such as: mean, median mode, standard deviation coefficient of variation, correlation coefficient, and probability to convey your findings Your Managerial Report must include . Probability of cases appealed AND reversed in each court Probability of a case being appealed for EACH judge Probability of a cose being reversed for EACH judge Probability of reversal given an appeal for EACH judge Ranking of each judge within each court. State the criteria you are using for your ranking AND provide the rationale for your choice Analysis: Who is doing a good job AND who is not? Why do you think so? Use your created ranking criteria to analyze the data 1 Judge Disposed Appealed Reversed Court 2 Fred Cartolano 3037 137 12 Common 3 Thomas Crush 3372 119 10 Common 4 Patrick Dinkelacker 1258 44 8 Common 5 Timothy Hogan 1954 60 7 Common 6 Robert Kraft 3138 127 7 Common 7 William Mathews 2264 91 18 Common 8 William Morrissey 3032 121 22 Common 9 Norbert Nadel 2959 131 20 Common 10 Arthur Ney Jr. 3219 125 14 Common 11 Richard Niehaus 3353 137 16 Common 12 Thomas Nurre 3000 121 6 Common 13 John O'Connor 2969 129 12 Common 14 Robert Ruehlman 3205 145 18 Common 15 J. Howard Sundermann Jr. 955 60 10 Common 16 Ann Marie Tracey 3141 127 13 Common 17 Ralph Winkler 3089 88 6 Common 18 Penelope Cunningham 2729 7 1 Domestic 19 Patrick Dinkelacker 6001 19 4 Domestic 20 Deborah Gaines 8799 48 9 Domestic 21 Ronald Panioto 12970 32 3 Domestic 22 Mike Allen 6149 43 4 Muni 23 Nadine Allen 7812 34 6 Muni 24 Timothy Black 7954 41 6 Muni 25 David Davis 7736 43 S Muni 26 Leslie Isaiah Gaines 5282 35 13 Muni 27 Karla Grady 5253 6 O Muni 28 Deidra Hair 2532 5 O Muni 29 Dennis Helmick 7900 29 5 Muni 30 Timothy Hogan 2308 13 2 Muni 31 James Patrick Kenney 2798 6 1 Muni 32 Joseph Luebbers 4698 25 8 Muni 33 Williarn Mallory 8277 38 9 Muni 34 Melba Marsh 8219 34 7 Muni 35 Beth Mattingly 2971 13 1 Muni 36 Albert Mestemaker 4975 28 9 Muni 37 Mark Painter 2239 7 3 Muni 38 Jack Rosen 7790 41 13 Muni 39 Mark Schweikert 5403 33 6 Muni 40 David Stockdale 5371 22 4 Muni 41 John A. West 2797 4 2 Muni 42
