EBK BUSINESS ANALYTICS
3rd Edition
ISBN: 9780136880844
Author: Evans
Publisher: PEARSON
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Chapter 2, Problem 18PE
For the Bicycle Inventory database, use database
- The total number of leisure bicycles on hand.
- The average purchase cost and selling price of road bicycles.
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can you explain why my answer to Q2 was wrong and, how to get the correct answer
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Chapter 2 Solutions
EBK BUSINESS ANALYTICS
Ch. 2 - The Excel file Syringe Samples provides data for...Ch. 2 - Convert the Store and Regional Sales database to...Ch. 2 - Convert the Purchase Orders database to an Excel...Ch. 2 - Prob. 6PECh. 2 - Prob. 8PECh. 2 - In the Excel file Automobile Quality, search for...Ch. 2 - Prob. 10PECh. 2 - Conduct a Pareto analysis for the number of...Ch. 2 - Use Excels filtering capability in the Purchase...Ch. 2 - In the Sales Transactions database, use Excels...
Ch. 2 - Filter the data in the Bicycle Inventory database...Ch. 2 - Use the Excel file Credit Risk Data that was...Ch. 2 - Apply the Advanced Filter to the Credit Risk Data...Ch. 2 - Prob. 17PECh. 2 - For the Bicycle Inventory database, use database...Ch. 2 - For the Bicycle Inventory database, write a...Ch. 2 - The Excel file SP500 provides open, high, low, and...Ch. 2 - In the Purchase Orders database, we need to find...Ch. 2 - An auditor found an expense receipt for 179.24...Ch. 2 - Use PivotTables to find the number of sales...
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- Business discussarrow_forwardSpam filters are built on principles similar to those used in logistic regression. We fit a probability that each message is spam or not spam. We have several variables for each email. Here are a few: to_multiple=1 if there are multiple recipients, winner=1 if the word 'winner' appears in the subject line, format=1 if the email is poorly formatted, re_subj=1 if "re" appears in the subject line. A logistic model was fit to a dataset with the following output: Estimate SE Z Pr(>|Z|) (Intercept) -0.8161 0.086 -9.4895 0 to_multiple -2.5651 0.3052 -8.4047 0 winner 1.5801 0.3156 5.0067 0 format -0.1528 0.1136 -1.3451 0.1786 re_subj -2.8401 0.363 -7.824 0 (a) Write down the model using the coefficients from the model fit.log_odds(spam) = -0.8161 + -2.5651 + to_multiple + 1.5801 winner + -0.1528 format + -2.8401 re_subj(b) Suppose we have an observation where to_multiple=0, winner=1, format=0, and re_subj=0. What is the predicted probability that this message is spam?…arrow_forwardConsider an event X comprised of three outcomes whose probabilities are 9/18, 1/18,and 6/18. Compute the probability of the complement of the event. Question content area bottom Part 1 A.1/2 B.2/18 C.16/18 D.16/3arrow_forward
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