Business Analytics
3rd Edition
ISBN: 9780135231678
Author: Evans, James R. (james Robert)
Publisher: PEARSON EDUCATION (COLLEGE)
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Chapter 3, Problem 2.2PE
To determine
Construct a sunburst chart for the regions, items, and units sold in “Store and Regional Sales Database”.
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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 3 Solutions
Business Analytics
Ch. 3 - Explain the pros and cons of tabular versus visual...Ch. 3 - How is data visualization used in descriptive,...Ch. 3 - Summarize the most useful charts available in...Ch. 3 - Prob. 2.2CYUCh. 3 - Prob. 2.3CYUCh. 3 - Prob. 2.4CYUCh. 3 - Prob. 3.1CYUCh. 3 - What is a heatmap, and what are some typical...Ch. 3 - Prob. 3.3CYUCh. 3 - Prob. 4.1CYU
Ch. 3 - Prob. 4.2CYUCh. 3 - Prob. 4.3CYUCh. 3 - Create clustered column and stacked column charts...Ch. 3 - Prob. 1.2PECh. 3 - Prob. 1.3PECh. 3 - Prob. 1.4PECh. 3 - Prob. 1.5PECh. 3 - Prob. 1.6PECh. 3 - Prob. 1.7PECh. 3 - Prob. 1.8PECh. 3 - Construct a column chart for the data in the Excel...Ch. 3 - The Excel file Internet Usage provides data about...Ch. 3 - Construct an appropriate chart to show the...Ch. 3 - Construct an appropriate chart or charts to...Ch. 3 - Prob. 1.13PECh. 3 - In the Excel file Banking Data, apply the...Ch. 3 - Prob. 1.15PECh. 3 - Prob. 1.16PECh. 3 - Prob. 1.17PECh. 3 - Prob. 1.18PECh. 3 - Create a useful dashboard for the data in the...Ch. 3 - Prob. 1.20PECh. 3 - Prob. 1.21PECh. 3 - Prob. 2.1PECh. 3 - Prob. 2.2PECh. 3 - Prob. 2.3PECh. 3 - Prob. 2.4PECh. 3 - Use PivotCharts to create a useful dashboard for...Ch. 3 - Prob. 2.6PECh. 3 - Prob. 2.7PECh. 3 - Prob. 2.8PECh. 3 - Use PivotCharts to create a useful dashboard for...
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- The details of the clock sales at a supermarket for the past 6 weeks are shown in the table below. The time series appears to be relatively stable, without trend, seasonal, or cyclical effects. The simple moving average value of k is set at 2. If the smoothing constant is assumed to be 0.7, and setting F1 and F2=A1, what is the exponential smoothing sales forecast for week 7? Round to the nearest whole number. Week Units sold 1 88 2 44 3 54 4 65 5 72 6 85 Question content area bottom Part 1 A. 80 clocks B. 60 clocks C. 70 clocks D. 50 clocksarrow_forwardThe details of the clock sales at a supermarket for the past 6 weeks are shown in the table below. The time series appears to be relatively stable, without trend, seasonal, or cyclical effects. The simple moving average value of k is set at 2. Calculate the value of the simple moving average mean absolute percentage error. Round to two decimal places. Week Units sold 1 88 2 44 3 54 4 65 5 72 6 85 Part 1 A. 14.39 B. 25.56 C. 23.45 D. 20.90arrow_forwardThe accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Year Fossil Fuels Production Fossil Fuels Consumption Total Energy Consumption1949 28.748 29.002 31.9821950 32.563 31.632 34.6161951 35.792 34.008 36.9741952 34.977 33.800 36.7481953 35.349 34.826 37.6641954 33.764 33.877 36.6391955 37.364 37.410 40.2081956 39.771 38.888 41.7541957 40.133 38.926 41.7871958 37.216 38.717 41.6451959 39.045 40.550 43.4661960 39.869 42.137 45.0861961 40.307 42.758 45.7381962 41.732 44.681 47.8261963 44.037 46.509 49.6441964 45.789 48.543 51.8151965 47.235 50.577 54.0151966 50.035 53.514 57.0141967 52.597 55.127 58.9051968 54.306 58.502 62.4151969 56.286…arrow_forward
- The accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Year Fossil Fuels Production Fossil Fuels Consumption Total Energy Consumption1949 28.748 29.002 31.9821950 32.563 31.632 34.6161951 35.792 34.008 36.9741952 34.977 33.800 36.7481953 35.349 34.826 37.6641954 33.764 33.877 36.6391955 37.364 37.410 40.2081956 39.771 38.888 41.7541957 40.133 38.926 41.7871958 37.216 38.717 41.6451959 39.045 40.550 43.4661960 39.869 42.137 45.0861961 40.307 42.758 45.7381962 41.732 44.681 47.8261963 44.037 46.509 49.6441964 45.789 48.543 51.8151965 47.235 50.577 54.0151966 50.035 53.514 57.0141967 52.597 55.127 58.9051968 54.306 58.502 62.4151969 56.286…arrow_forwardThe accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Develop line charts for each variable and identify the characteristics of the time series (that is, random, stationary, trend, seasonal, or cyclical). What is the line chart for the variable Fossil Fuels Production?arrow_forwardThe accompanying data shows the fossil fuels production, fossil fuels consumption, and total energy consumption in quadrillions of BTUs of a certain region for the years 1986 to 2015. Complete parts a and b. Year Fossil Fuels Production Fossil Fuels Consumption Total Energy Consumption1949 28.748 29.002 31.9821950 32.563 31.632 34.6161951 35.792 34.008 36.9741952 34.977 33.800 36.7481953 35.349 34.826 37.6641954 33.764 33.877 36.6391955 37.364 37.410 40.2081956 39.771 38.888 41.7541957 40.133 38.926 41.7871958 37.216 38.717 41.6451959 39.045 40.550 43.4661960 39.869 42.137 45.0861961 40.307 42.758 45.7381962 41.732 44.681 47.8261963 44.037 46.509 49.6441964 45.789 48.543 51.8151965 47.235 50.577 54.0151966 50.035 53.514 57.0141967 52.597 55.127 58.9051968 54.306 58.502 62.4151969 56.286…arrow_forward
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