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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 x2 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. 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 x2 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