Spring 2021 Business Intelligence and Data Analysis Assignment-2 Answer the following questions by creating pivot tables based on the Workers data set ("Workers.csv" on Blackboard). You can refer to the DataSet Descriptions-1 document for summary attribute descriptions. You do not need to submit Excel spreadsheets; please just answer the following questions: a) Report the percent of union membership ('Union' attribute) for the four subgroups (2 x 2 table) based on the two attributes 'South' and 'Sex'. Are female workers more likely to be union members compared to male workers? Is the region where the worker lives important for predicting union membership? b) Consider the effect of the two attributes 'Experience' and 'Sex' for prediction union membership ('Union' attribute). Create a 6 by 2 table using Experience as the row identifier. Group experience entries in to six bins (10 years each). Can you identify a trend about union membership based on more years of experience (independent of the gender)? How does gender play a role with respect to experience for explaining the union membership likelihood? Generalize your findings and state your conclusions independently for increasing levels of experience and gender. If there are exceptions to the rule identify them as well. F L K Union Sex F F M OOOOO M M 1 M M M M M M M HOOOO 0 0 1 1 0 1 0 0 1 A B Education South 8 N 9 N 12 N 12 N 12 N 13 N 10 Y 12 N 16 N 12 N 12 N 12 N 8 Y 9 Y 9 Y 12 N 7 Y 12 N 11 N 12 N 12 N 7 N 12 N 11 Y 12 N 6 Y 12 N 10 N 12 N 12 N 14 N 12 Y 8 N 17 Y 12 N 12 N 12 Y 12 N 12 IN D E Experience Wage Age 21 5.1 42 4.95 1 6.67 4 4 17 7.5 9 13.07 27 4.45 9 19.47 11 13.28 9 8.75 17 11.35 19 11.5 27 6.5 30 6.25 29 19.98 37 7.3 44 8 26 22.2 16 3.65 33 20.55 16 5.71 42 7 9 3.75 14 4.5 23 9.56 45 5.75 8 9.36 30 6.5 8 3.35 8 4.75 13 8.9 46 4 19 4.7 1 5 19 9.25 36 10.67 20 7.61 35 10 G Race 35 H 57 W 19 W 22 W 35 W 28 W 43 W 27 w 33 W 27 w 35 W 37 W 41 W 45 W 44 W 55 W 57 w 44 W 33 W 51 W 34 W 55 O 27 W 31 0 41 W 57 W 26 W 46 W 26 W 26 W 33 W 64 W 33 W 24 W 37 W 54 0 38 0 53 0 M M M M F M M M M M M M F M M F M F M M M M H Occupation Sector Marr Other Manufacturii Married Other Manufacturii Married Other Manufacturit Single Other Other Single Other Other Married Other Other Single Other Other Single Other Other Single Other Manufacturit Married Other Other Single Other Other Married Other Manufacturis Single Other Other Married Other Other Single Other Other Married Other Construction Married Other Other Married Other Manufacturii Married Other Other Single Other Other Married Other Manufacturit Married Other Manufacturii Married Other Other Single Other Other Married Other Other Married Other Manufacturii Married Other Manufacturii Married Other Other Married Other Manufacturii Married Other Other Married Other Other Single Other Other Single Other Other Married Other Other Single Other Manufacturi Single Other Other Single Other Construction Married Other Construction Married Other Othor sinalo O O 1 1 0 0 0 0 0 0 0 0 0 0 0 OOOO 1 A B D F G K L F F F M F ONMON.NONMON.NO 12 N 14 Y 12 N 14 Y 12 N 9 N 13 Y 7Y 16 N 10 Y 8 N 12 N 12 N 16 N 12 N 12 N 13 N 12 N 13 N 10 N 12 N 12 N 11 N 12 N 3 Y 12 Y 12 N 10 N 12 Y 12 N 12 N 10 Y 11 Y 14 N 10 N 8 Y 8 Y 6 N 11 Y 12 IN 3 10 0 14 14 16 8 15 12 13 33 9 7 13 7 16 0 11 17 13 22 28 17 24 55 3 6 27 19 19 38 41 3 20 15 8 39 43 25 , E 7.5 12.2 3.35 11 12 4.85 4.3 6 15 4.85 9 6.36 9.15 11 4.5 4.8 4 5.5 8.4 6.75 10 5 6.5 10.75 7 11.43 4 9 13 12.22 6.28 6.75 3.35 16 5.25 3.5 4.22 3 4 21 W 30 w 18 W 34 W 32 w 31 W 27 W 28 W 34 W 29 W 47 w 27 W 25 W 35 W 25 W 34 W 19 W 29 W 36 W 29 W 40 O 46 W 34 W 42 W 64 H 21 W 24 0 43 W 37 0 37 W 56 W 57 O 20 0 40 W 31 W 22 H 53 W 55 H 42 W H Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other +bar Other Single Manufacturii Married Other Single Manufacturii Married Manufacturii Married Manufacturii Married Construction Single Manufacturi Married Manufacturii Married Other Single Other Married Manufacturii Married Other Married Manufacturii Married Manufacturii Married Manufacturii Married Other Single Manufacturi Single Manufacturi Single Manufacturi Married Manufacturi Single Manufacturii Married Other Single Construction Married Manufacturii Married Construction Single Manufacturi Single Construction Married Manufacturii Married Construction Married Manufacturii Married Manufacturi Married Manufacturi Single Other Married Other Married Manufacturii Married Manufacturit Married Manufacturit Married Manufacturii Married Married OOOOOOOOOOOOOOOOOOOOOOO F M M M M M 1 0 1 0 0 0 1 1 F F F NA +bar Spring 2021 Business Intelligence and Data Analysis Assignment-2 Answer the following questions by creating pivot tables based on the Workers data set ("Workers.csv" on Blackboard). You can refer to the DataSet Descriptions-1 document for summary attribute descriptions. You do not need to submit Excel spreadsheets; please just answer the following questions: a) Report the percent of union membership ('Union' attribute) for the four subgroups (2 x 2 table) based on the two attributes 'South' and 'Sex'. Are female workers more likely to be union members compared to male workers? Is the region where the worker lives important for predicting union membership? b) Consider the effect of the two attributes 'Experience' and 'Sex' for prediction union membership ('Union' attribute). Create a 6 by 2 table using Experience as the row identifier. Group experience entries in to six bins (10 years each). Can you identify a trend about union membership based on more years of experience (independent of the gender)? How does gender play a role with respect to experience for explaining the union membership likelihood? Generalize your findings and state your conclusions independently for increasing levels of experience and gender. If there are exceptions to the rule identify them as well. F L K Union Sex F F M OOOOO M M 1 M M M M M M M HOOOO 0 0 1 1 0 1 0 0 1 A B Education South 8 N 9 N 12 N 12 N 12 N 13 N 10 Y 12 N 16 N 12 N 12 N 12 N 8 Y 9 Y 9 Y 12 N 7 Y 12 N 11 N 12 N 12 N 7 N 12 N 11 Y 12 N 6 Y 12 N 10 N 12 N 12 N 14 N 12 Y 8 N 17 Y 12 N 12 N 12 Y 12 N 12 IN D E Experience Wage Age 21 5.1 42 4.95 1 6.67 4 4 17 7.5 9 13.07 27 4.45 9 19.47 11 13.28 9 8.75 17 11.35 19 11.5 27 6.5 30 6.25 29 19.98 37 7.3 44 8 26 22.2 16 3.65 33 20.55 16 5.71 42 7 9 3.75 14 4.5 23 9.56 45 5.75 8 9.36 30 6.5 8 3.35 8 4.75 13 8.9 46 4 19 4.7 1 5 19 9.25 36 10.67 20 7.61 35 10 G Race 35 H 57 W 19 W 22 W 35 W 28 W 43 W 27 w 33 W 27 w 35 W 37 W 41 W 45 W 44 W 55 W 57 w 44 W 33 W 51 W 34 W 55 O 27 W 31 0 41 W 57 W 26 W 46 W 26 W 26 W 33 W 64 W 33 W 24 W 37 W 54 0 38 0 53 0 M M M M F M M M M M M M F M M F M F M M M M H Occupation Sector Marr Other Manufacturii Married Other Manufacturii Married Other Manufacturit Single Other Other Single Other Other Married Other Other Single Other Other Single Other Other Single Other Manufacturit Married Other Other Single Other Other Married Other Manufacturis Single Other Other Married Other Other Single Other Other Married Other Construction Married Other Other Married Other Manufacturii Married Other Other Single Other Other Married Other Manufacturit Married Other Manufacturii Married Other Other Single Other Other Married Other Other Married Other Manufacturii Married Other Manufacturii Married Other Other Married Other Manufacturii Married Other Other Married Other Other Single Other Other Single Other Other Married Other Other Single Other Manufacturi Single Other Other Single Other Construction Married Other Construction Married Other Othor sinalo O O 1 1 0 0 0 0 0 0 0 0 0 0 0 OOOO 1 A B D F G K L F F F M F ONMON.NONMON.NO 12 N 14 Y 12 N 14 Y 12 N 9 N 13 Y 7Y 16 N 10 Y 8 N 12 N 12 N 16 N 12 N 12 N 13 N 12 N 13 N 10 N 12 N 12 N 11 N 12 N 3 Y 12 Y 12 N 10 N 12 Y 12 N 12 N 10 Y 11 Y 14 N 10 N 8 Y 8 Y 6 N 11 Y 12 IN 3 10 0 14 14 16 8 15 12 13 33 9 7 13 7 16 0 11 17 13 22 28 17 24 55 3 6 27 19 19 38 41 3 20 15 8 39 43 25 , E 7.5 12.2 3.35 11 12 4.85 4.3 6 15 4.85 9 6.36 9.15 11 4.5 4.8 4 5.5 8.4 6.75 10 5 6.5 10.75 7 11.43 4 9 13 12.22 6.28 6.75 3.35 16 5.25 3.5 4.22 3 4 21 W 30 w 18 W 34 W 32 w 31 W 27 W 28 W 34 W 29 W 47 w 27 W 25 W 35 W 25 W 34 W 19 W 29 W 36 W 29 W 40 O 46 W 34 W 42 W 64 H 21 W 24 0 43 W 37 0 37 W 56 W 57 O 20 0 40 W 31 W 22 H 53 W 55 H 42 W H Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other Other +bar Other Single Manufacturii Married Other Single Manufacturii Married Manufacturii Married Manufacturii Married Construction Single Manufacturi Married Manufacturii Married Other Single Other Married Manufacturii Married Other Married Manufacturii Married Manufacturii Married Manufacturii Married Other Single Manufacturi Single Manufacturi Single Manufacturi Married Manufacturi Single Manufacturii Married Other Single Construction Married Manufacturii Married Construction Single Manufacturi Single Construction Married Manufacturii Married Construction Married Manufacturii Married Manufacturi Married Manufacturi Single Other Married Other Married Manufacturii Married Manufacturit Married Manufacturit Married Manufacturii Married Married OOOOOOOOOOOOOOOOOOOOOOO F M M M M M 1 0 1 0 0 0 1 1 F F F NA +bar