Business Analytics
3rd Edition
ISBN: 9780135231678
Author: Evans, James R. (james Robert)
Publisher: PEARSON EDUCATION (COLLEGE)
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Chapter 2, Problem 37PE
Construct PivotTables showing the counts of gender versus carrier and type versus usage in the Excel file Cell Phone Survey. What might you conclude from this analysis?
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BUSINESS DISCUSS
A researcher wishes to estimate, with 90% confidence, the population proportion of adults who support labeling
legislation for genetically modified organisms (GMOs). Her estimate must be accurate within 4% of the true proportion.
(a) No preliminary estimate is available. Find the minimum sample size needed.
(b) Find the minimum sample size needed, using a prior study that found that 65% of the respondents said they support
labeling legislation for GMOs.
(c) Compare the results from parts (a) and (b).
...
(a) What is the minimum sample size needed assuming that no prior information is available?
n =
(Round up to the nearest whole number as needed.)
The table available below shows the costs per mile (in cents) for a sample of automobiles. At a = 0.05, can you conclude that at least one mean
cost per mile is different from the others?
Click on the icon to view the data table.
Let Hss, HMS, HLS, Hsuv and Hмy represent the mean costs per mile for small sedans, medium sedans, large sedans, SUV 4WDs, and minivans
respectively. What are the hypotheses for this test?
OA. Ho: Not all the means are equal.
Ha Hss HMS HLS HSUV HMV
B. Ho Hss HMS HLS HSUV = μMV
Ha: Hss *HMS *HLS*HSUV * HMV
C. Ho Hss HMS HLS HSUV =μMV
= =
H: Not all the means are equal.
D. Ho Hss HMS
HLS HSUV HMV
Ha Hss HMS
HLS =HSUV = HMV
Chapter 2 Solutions
Business Analytics
Ch. 2 - Explain the difference between a data set and a...Ch. 2 - For the Monthly Product Sales Excel file, use the...Ch. 2 - For the Monthly Product Sales Excel file, use the...Ch. 2 - What does the term filtering mean in the context...Ch. 2 - Convert the Credit Risk database to an Excel...Ch. 2 - Sort the data in the purchase orders database from...Ch. 2 - Prob. 2.4CYUCh. 2 - Prob. 3.1CYUCh. 2 - Prob. 3.2CYUCh. 2 - Prob. 3.3CYU
Ch. 2 - Prob. 4.1CYUCh. 2 - In the Purchase Orders database, what will =...Ch. 2 - Prob. 4.3CYUCh. 2 - Prob. 5.1CYUCh. 2 - Prob. 5.2CYUCh. 2 - Prob. 5.3CYUCh. 2 - Prob. 5.4CYUCh. 2 - Prob. 6.1CYUCh. 2 - Prob. 6.2CYUCh. 2 - Prob. 6.3CYUCh. 2 - The Excel file MBA Motivation and Salary...Ch. 2 - Prob. 2PECh. 2 - The Budget Forecasting database shows estimated...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 - The Excel file Corporate Default Database...Ch. 2 - Open the Excel file Store and Regional Sales...Ch. 2 - In the Excel file Automobile Quality, search for...Ch. 2 - In the Purchase Orders database, conduct a Pareto...Ch. 2 - Conduct a Pareto analysis for the number of...Ch. 2 - Prob. 12PECh. 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 - Prob. 18PECh. 2 - Modify the data in the Base Data worksheet in the...Ch. 2 - Prob. 20PECh. 2 - Figure 2.41 shows the 2016 U.S. federal income tax...Ch. 2 - For the Bicycle Inventory database, write a...Ch. 2 - Prob. 23PECh. 2 - A manager needs to identify some information from...Ch. 2 - The Excel file SP 500 provides open, high, low,...Ch. 2 - Enhance the Sales Transactions database file to...Ch. 2 - Prob. 27PECh. 2 - For this exercise, use the Purchase Orders...Ch. 2 - Use INDEX and MATCH functions to fill in a table...Ch. 2 - Prob. 30PECh. 2 - Prob. 31PECh. 2 - Prob. 32PECh. 2 - Prob. 33PECh. 2 - Prob. 34PECh. 2 - A college graduate who is living in Cincinnati...Ch. 2 - The Excel file Payroll Data provides hourly...Ch. 2 - Construct PivotTables showing the counts of gender...Ch. 2 - Prob. 38PECh. 2 - Prob. 39PECh. 2 - Prob. 40PECh. 2 - Prob. 2.1C
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Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
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- Find the minimum sample size n needed to estimate μ for the given values of c, σ, and E. C=0.98, σ 6.7, and E = 2 Assume that a preliminary sample has at least 30 members. n = (Round up to the nearest whole number.)arrow_forwardIn a survey of 2193 adults in a recent year, 1233 say they have made a New Year's resolution. Construct 90% and 95% confidence intervals for the population proportion. Interpret the results and compare the widths of the confidence intervals. The 90% confidence interval for the population proportion p is (Round to three decimal places as needed.) J.D) .arrow_forwardLet p be the population proportion for the following condition. Find the point estimates for p and q. In a survey of 1143 adults from country A, 317 said that they were not confident that the food they eat in country A is safe. The point estimate for p, p, is (Round to three decimal places as needed.) ...arrow_forward
- (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)arrow_forwardBusiness 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_forward
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