Instant Download Solution Manual For Predictive Analytics For Business Strategy, 1st Edition, Jeff Prince PDF All Chapter
Instant Download Solution Manual For Predictive Analytics For Business Strategy, 1st Edition, Jeff Prince PDF All Chapter
Instant Download Solution Manual For Predictive Analytics For Business Strategy, 1st Edition, Jeff Prince PDF All Chapter
com
https://testbankmall.com/product/solution-manual-for-
predictive-analytics-for-business-strategy-1st-edition-jeff-
prince/
OR CLICK BUTTON
DOWLOAD EBOOK
https://testbankmall.com/product/solution-manual-for-managerial-
economics-business-strategy-9th-edition-michael-baye-jeff-prince/
https://testbankmall.com/product/solution-manual-for-managerial-
economics-business-strategy-10th-edition-michael-baye-jeff-
prince/
https://testbankmall.com/product/test-bank-for-managerial-
economics-business-strategy-9th-edition-michael-baye-jeff-prince/
https://testbankmall.com/product/test-bank-for-managerial-
economics-business-strategy-10th-edition-michael-baye-jeff-
prince/
Solution manual for Managerial Economics & Business
Strategy Baye Prince 8th Edition
https://testbankmall.com/product/solution-manual-for-managerial-
economics-business-strategy-baye-prince-8th-edition/
https://testbankmall.com/product/solution-manual-for-essentials-
of-business-analytics-1st-edition/
https://testbankmall.com/product/solution-manual-for-forecasting-
and-predictive-analytics-with-forecast-x-7th-edition-by-keating/
https://testbankmall.com/product/test-bank-for-business-
analytics-1st-edition-evans/
https://testbankmall.com/product/test-bank-for-essentials-of-
business-analytics-1st-edition/
3. a. Query. This may seem like pattern discovery, but there needs to be some
threshold that qualifies this as a pattern.
5. Passive prediction involves predicting outcomes while observing, but not altering,
their determining factors. Active prediction involves predicting outcomes after
altering at least one of their determining factors.
b. Passive prediction – Ann does not directly alter the number of visits to her site.
d. Passive prediction – Alex does not directly alter people’s credit card purchasing.
e. Passive prediction – John does not directly alter the voter’s answers.
ii. You collect data on varying levels of ad expenditure along with profits across
locations and/or time. Then, using techniques described in later chapters, you
analyze how profits respond to changes in ad expenditure in the data. If the
analysis shows profits declining with increases in ad expenditure, this would
constitute a refutation to the claim.
2
© 2019 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.
b. Data consist of what actually occurred, allowing for evidence-based decision-
making, rather than “gut”-based decision-making.
9. Here, we need three factors that we believe have a causal effect on the number of
years an employee stays with a firm. Three such factors might be:
10. Following the example in the text, we can formally express the data generating
process for weekly soda sales as: 𝑆𝑎𝑙𝑒𝑠𝑡 = 𝑓(𝑃𝑟𝑖𝑐𝑒𝑡 , 𝑃𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑡 , 𝐻𝑜𝑙𝑖𝑑𝑎𝑦𝑡 ) + 𝑈𝑡 .
11. a. This does not require active prediction. Rather, it is a good example of an
application of passive prediction. We want to predict how purchases relate to age,
and we are not making changes to our customers’ ages.
b. This does require active prediction. We are considering making an active change
in strategy – in the form of a new celebrity endorsement – and we want to predict
how sales will respond.
c. This does require active prediction. We are actively changing product placement
(a strategic move), and want to know the impact on profits.
12. Amanda is making the active prediction. She is determining what will happen with a
change in strategy (i.e., a price cut). In comparison, Darryl is making a passive
prediction. He is using demographics – which Meredith is not considering, or
capable of, changing – to predict the likelihood of an accident.
13. See DataLoad.xlsx for the data loading, or the table below provides an example.
3
© 2019 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.
The unit of observation is a person-year. The data are panel data.
15. a. i. $1,468,424.42
ii. 11,526,750.78
iii. 3,579,884.506
iv. $51,439.46
v. $353,890,286.00
16. a. $1,481,100.02
b. $504,655
c. Region 166
4
© 2019 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.
d. Region 223
e. $2,397,435 - $500,776 = $1,896,659
17. a. There is a strong positive correlation between a customer being active and their
age level. Hence, it appears younger customers are most likely to drop. This is
lead information since it is designed to look ahead and assess where the greatest
risks of customer loss will be in the future.
b. The North Region had the most customers (84). This is lag information, since it
is simply reporting what happened.
5
© 2019 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.
Another random document with
no related content on Scribd:
Zwanzigstes Kapitel.