APPLIED STAT.IN BUS.+ECONOMICS
6th Edition
ISBN: 9781259957598
Author: DOANE
Publisher: RENT MCG
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Chapter 12.2, Problem 10SE
(a) Interpret the slope of the fitted regression Computer power dissipation = 15.73 + 0.032 Microprocessor speed, where Computer power dissipation is measured in watts and Microprocessor speed is measured in MHz. (b) What is the prediction for Computer power dissipation if Microprocessor speed is 3,000 MHz? (c) Is this intercept meaningful?
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(c) Because logistic regression predicts probabilities of outcomes, observations used to build a logistic regression model need not be independent.
A. false: all observations must be independent
B. true
C. false: only observations with the same outcome need to be independent
I ANSWERED: A. false: all observations must be independent.
(This was marked wrong but I have no idea why. Isn't this a basic assumption of logistic regression)
Business discuss
Spam 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?…
Chapter 12 Solutions
APPLIED STAT.IN BUS.+ECONOMICS
Ch. 12.1 - For each sample, do a test for zero correlation....Ch. 12.1 - Instructions for Exercises 12.2 and 12.3: (a) Make...Ch. 12.1 - Prob. 3SECh. 12.1 - Prob. 4SECh. 12.1 - Instructions for exercises 12.412.6: (a) Make a...Ch. 12.1 - Prob. 6SECh. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.2 - Prob. 9SECh. 12.2 - (a) Interpret the slope of the fitted regression...
Ch. 12.2 - (a) Interpret the slope of the fitted regression...Ch. 12.3 - Prob. 12SECh. 12.3 - Prob. 13SECh. 12.3 - The regression equation Credits = 15.4 .07 Work...Ch. 12.3 - Below are fitted regressions for Y = asking price...Ch. 12.3 - Refer back to the regression equation in exercise...Ch. 12.3 - Refer back to the regression equation in exercise...Ch. 12.4 - Instructions for exercises 12.18 and 12.19: (a)...Ch. 12.4 - Instructions for exercises 12.18 and 12.19: (a)...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.4 - Instructions for exercises 12.2012.22: (a) Use...Ch. 12.5 - Instructions for exercises 12.23 and 12.24: (a)...Ch. 12.5 - Instructions for exercises 12.23 and 12.24: (a)...Ch. 12.5 - A regression was performed using data on 32 NFL...Ch. 12.5 - A regression was performed using data on 16...Ch. 12.6 - Below is a regression using X = home price (000),...Ch. 12.6 - Below is a regression using X = average price, Y =...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.6 - Instructions for exercises 12.2912.31: (a) Use...Ch. 12.7 - Refer to the Weekly Earnings data set below. (a)...Ch. 12.7 - Prob. 33SECh. 12.8 - Prob. 34SECh. 12.8 - Prob. 35SECh. 12.9 - Calculate the standardized residual ei and...Ch. 12.9 - Prob. 37SECh. 12.9 - An estimated regression for a random sample of...Ch. 12.9 - An estimated regression for a random sample of...Ch. 12.9 - Prob. 40SECh. 12.9 - Prob. 41SECh. 12.9 - Prob. 42SECh. 12.9 - Prob. 43SECh. 12.11 - Prob. 44SECh. 12.11 - Prob. 45SECh. 12 - (a) How does correlation analysis differ from...Ch. 12 - (a) What is a simple regression model? (b) State...Ch. 12 - (a) Explain how you fit a regression to an Excel...Ch. 12 - (a) Explain the logic of the ordinary least...Ch. 12 - (a) Why cant we use the sum of the residuals to...Ch. 12 - Prob. 6CRCh. 12 - Prob. 7CRCh. 12 - Prob. 8CRCh. 12 - Prob. 9CRCh. 12 - Prob. 10CRCh. 12 - Prob. 11CRCh. 12 - Prob. 12CRCh. 12 - (a) What is heteroscedasticity? Identify its two...Ch. 12 - (a) What is autocorrelation? Identify two main...Ch. 12 - Prob. 15CRCh. 12 - Prob. 16CRCh. 12 - (a) What is a log transform? (b) What are its...Ch. 12 - (a) When is logistic regression needed? (b) Why...Ch. 12 - Prob. 46CECh. 12 - Prob. 47CECh. 12 - Prob. 48CECh. 12 - Instructions: Choose one or more of the data sets...Ch. 12 - Prob. 50CECh. 12 - Prob. 51CECh. 12 - Prob. 52CECh. 12 - Prob. 53CECh. 12 - Instructions: Choose one or more of the data sets...Ch. 12 - Instructions: Choose one or more of the data sets...Ch. 12 - Instructions: Choose one or more of the data sets...Ch. 12 - Prob. 57CECh. 12 - Prob. 58CECh. 12 - Prob. 59CECh. 12 - Prob. 60CECh. 12 - Prob. 61CECh. 12 - Prob. 62CECh. 12 - Prob. 63CECh. 12 - Prob. 64CECh. 12 - Prob. 65CECh. 12 - In the following regression, X = weekly pay, Y =...Ch. 12 - Prob. 67CECh. 12 - In the following regression, X = total assets (...Ch. 12 - Prob. 69CECh. 12 - Below are percentages for annual sales growth and...Ch. 12 - Prob. 71CECh. 12 - Prob. 72CECh. 12 - Prob. 73CECh. 12 - Simple regression was employed to establish the...Ch. 12 - Prob. 75CECh. 12 - Prob. 76CECh. 12 - Prob. 77CECh. 12 - Below are revenue and profit (both in billions)...Ch. 12 - Below are fitted regressions based on used vehicle...Ch. 12 - Below are results of a regression of Y = average...Ch. 12 - Prob. 81CE
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