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Business
Operations Management
New Accounts Period New Accounts Period 200 6 232 11 214 248 12 3 211 250 13 4 228 253 14 235 10 267 15 Using linear regression, what is your forecast for period 16? O Less than 300 Between 301 and 308 Between 309 and 350 Period 1 2 O Higher than 350 New Accounts 281 275 280 288 310

New Accounts Period New Accounts Period 200 6 232 11 214 248 12 3 211 250 13 4 228 253 14 235 10 267 15 Using linear regression, what is your forecast for period 16? O Less than 300 Between 301 and 308 Between 309 and 350 Period 1 2 O Higher than 350 New Accounts 281 275 280 288 310

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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**Data for New Accounts Over Different Periods:** | Period | New Accounts | Period | New Accounts | Period | New Accounts | Period | New Accounts | |--------|--------------|--------|--------------|--------|--------------|--------|--------------| | 1 | 200 | 6 | 232 | 11 | 281 | | | 2 | 214 | 7 | 248 | 12 | 275 | | | 3 | 211 | 8 | 250 | 13 | 280 | | | 4 | 228 | 9 | 253 | 14 | 288 | | | 5 | 235 | 10 | 267 | 15 | 310 | | This table illustrates the number of new accounts opened over different periods. Each period shows incremental growth. ### Analysis and Forecast **Question:** Using linear regression, what is your forecast for period 16? **Options:** - Less than 300 - Between 301 and 308 - Between 309 and 350 - Higher than 350 To forecast future values, linear regression modeling can be employed. This statistical method fits a line to the given data points to predict future data points based on historical trends. **Details to Note:** - Observations over 15 periods show a progressive increase in new accounts. - Linear regression involves calculating the best fit line, which minimizes the sum of squared differences between the observed values and the values predicted by the line. In this context, based on the trend observed in the data presented above, practitioners have an opportunity to forecast future periods using linear regression techniques.
Transcribed Image Text:**Data for New Accounts Over Different Periods:** | Period | New Accounts | Period | New Accounts | Period | New Accounts | Period | New Accounts | |--------|--------------|--------|--------------|--------|--------------|--------|--------------| | 1 | 200 | 6 | 232 | 11 | 281 | | | 2 | 214 | 7 | 248 | 12 | 275 | | | 3 | 211 | 8 | 250 | 13 | 280 | | | 4 | 228 | 9 | 253 | 14 | 288 | | | 5 | 235 | 10 | 267 | 15 | 310 | | This table illustrates the number of new accounts opened over different periods. Each period shows incremental growth. ### Analysis and Forecast **Question:** Using linear regression, what is your forecast for period 16? **Options:** - Less than 300 - Between 301 and 308 - Between 309 and 350 - Higher than 350 To forecast future values, linear regression modeling can be employed. This statistical method fits a line to the given data points to predict future data points based on historical trends. **Details to Note:** - Observations over 15 periods show a progressive increase in new accounts. - Linear regression involves calculating the best fit line, which minimizes the sum of squared differences between the observed values and the values predicted by the line. In this context, based on the trend observed in the data presented above, practitioners have an opportunity to forecast future periods using linear regression techniques.
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