Statistics for Business and Economics, ...13th EditionJames T. McClave, P. George Benson, Terry Sincich Publisher: PEARSONISBN: 9780134456997
Statistics for Business and Economics, ...
Solutions for Statistics for Business and Economics, Student Value Edition (13th Edition)
Problem 1.1LM:
What is statistics?Problem 1.7LM:
Explain how populations and variables differ.Problem 1.8LM:
Explain how populations and samples differ.Problem 1.9LM:
What is a representative sample? What is its value?Problem 1.10LM:
Why would a statistician consider an inference incomplete without an accompanying measure of its...Problem 1.11LM:
Explain the difference between a population and a process.Problem 1.12LM:
Define statistical thinking.Problem 1.13LM:
Suppose youre given a data set that classifies each sample unit into one of four categories: A, B,...Problem 1.14LM:
Suppose that a population contains 200,000 experimental units. Use a random number generator to...Problem 1.1AE:
The Random Numbers applet generates a list of n random numbers from 1 to N, where n is the size of...Problem 1.2AE:
The Random Numbers applet can be used to select a random sample from a population, but can it be...Problem 1.15ACB:
Applying the ConceptsBasic 1.15 Performance-based logistics. In industry, performance-based...Problem 1.16ACB:
Jamming attacks on wireless networks. Terrorists often use wireless networks to communicate. To...Problem 1.17ACB:
Disaggregation of annually reported accounting data. Researchers have constructed a new measure of...Problem 1.18ACB:
College application data. Colleges and universities are requiring an increasing amount of...Problem 1.19ACB:
Opinion polls. Pollsters regularly conduct opinion polls to determine the popularity rating of the...Problem 1.20ACB:
Cybersecurity survey. The information systems organization, ISACA, conducts an annual survey of...Problem 1.21ACB:
Treasury deficit prior to the Civil War. In Civil War History (June 2009), historian Jane Flaherty...Problem 1.22ACB:
The lucky store effect in lottery ticket sales. In the American Economic Review (Vol. 98, 2008),...Problem 1.23ACI:
Consumer recycling behavior. Under what conditions will consumers dispose of recyclable paper in the...Problem 1.24ACI:
Who is better at multi-tasking? In business, employees are often asked to perform a complex task...Problem 1.25ACI:
Zillow.com estimates of home values. Zillow.com is a real estate Web site that provides free...Problem 1.26ACI:
Drafting NFL quarterbacks. The National Football League (NFL) is a lucrative business, generating an...Problem 1.27ACI:
The economic return to earning an MBA. What are the economic rewards (e.g., higher salary) to...Problem 1.28ACI:
Corporate sustainability and firm characteristics. Corporate sustainability refers to business...Problem 1.29ACI:
Inspection of highway bridges. All highway bridges in the United States are inspected periodically...Problem 1.30ACI:
Structurally deficient highway bridges. Refer to Exercise 1.29. The NBI data were analyzed and the...Problem 1.31ACI:
Monitoring product quality. The Wallace Company of Houston is a distributor of pipes, valves, and...Problem 1.32ACI:
Guilt in decision making. The effect of guilt emotion on how a decision maker focuses on the problem...Problem 1.33ACI:
Accounting and Machiavellianism. Behavioral Research in Accounting (January 2008) published a study...Problem 1.34ACI:
Can money spent on gifts buy love? Is the gift you purchased for that special someone really...Problem 1.36ACA:
Random-digit dialing. To ascertain the effectiveness of their advertising campaigns, firms...Problem 1.37ACA:
Current population survey. The employment status (employed or unemployed) of each individual in the...Problem 1.38ACA:
Monitoring the production of soft-drink cans. The Wakefield plant of Coca-Cola and Schweppes...Browse All Chapters of This Textbook
Chapter 1 - Statistics, Data, And Statistical ThinkingChapter 2 - Methods For Describing Sets Of DataChapter 2.1 - Describing Qualitative DataChapter 2.2 - Graphical Methods For Describing Quantitative DataChapter 2.3 - Numerical Measures Of Central TendencyChapter 2.4 - Numerical Measures Of VariabilityChapter 2.5 - Using The Mean And Standard Deviation To Describe DataChapter 2.6 - Numerical Measures Of Relative StandingChapter 2.7 - Methods For Detecting Outliers: Box Plots And z-ScoresChapter 2.8 - Graphing Bivariate Relationships (Optional)
Chapter 2.10 - Distorting The Truth With Descriptive TechniquesChapter 3 - ProbabilityChapter 3.1 - Events, Sample Spaces, And ProbabilityChapter 3.4 - The Additive Rule And Mutually Exclusive EventsChapter 3.6 - The Multiplicative Rule And Independent EventsChapter 3.7 - Bayes's RuleChapter 4 - Random Variables And Probability DistributionsChapter 4.1 - Two Types Of Random VariablesChapter 4.2 - Probability Distributions For Discrete Random VariablesChapter 4.3 - The Binomial DistributionChapter 4.4 - Other Discrete Distributions: Poisson And HypergeometricChapter 4.6 - The Normal DistributionChapter 4.7 - Descriptive Methods For Assessing NormalityChapter 4.8 - Other Continuous Distributions: Uniform And ExponentialChapter 5 - Sampling DistributionsChapter 5.1 - The Concept Of A Sampling DistributionChapter 5.2 - Properties Of Sampling Distributions: Unbiasedness And Minimum VarianceChapter 5.3 - The Sampling Distribution Of The Sample Mean And The Central Limit TheoremChapter 5.4 - The Sampling Distribution Of The Sample ProportionChapter 6 - Inferences Based On A Single Sample: Estimation With Confidence IntervalsChapter 6.2 - Confidence Interval For A Population Mean: Normal (z) StatisticChapter 6.3 - Confidence Interval For A Population Mean: Student's T-statisticChapter 6.4 - Large-Sample Confidence Interval For A Population ProportionChapter 6.5 - Determining The Sample SizeChapter 6.6 - Finite Population Correction For Simple Random Sampling (Optional)Chapter 6.7 - Confidence Interval For A Population VarianceChapter 7 - Inferences Based On A Single Sample: Tests Of HypothesesChapter 7.2 - Formulating Hypotheses And Setting Up The Rejection RegionChapter 7.3 - Observed Significance Levels: P-valuesChapter 7.4 - Test Of Hypothesis About A Population Mean: Normal (z) StatisticChapter 7.5 - Test Of Hypothesis About A Population Mean: Student's T-statisticChapter 7.6 - Large-Sample Test Of Hypothesis About A Population ProportionChapter 7.7 - Test Of Hypothesis About A Population VarianceChapter 7.8 - Calculating Type Ii Error Probabilities: More About 13 (Optional)Chapter 8 - Inferences Based On Two Samples: Confidence Intervals And Tests Of HypothesesChapter 8.2 - Comparing Two Population Means: Independent SamplingChapter 8.3 - Comparing Two Population Means: Paired Difference ExperimentsChapter 8.4 - Comparing Two Population Proportions: Independent SamplingChapter 8.5 - Determining The Required Sample SizeChapter 8.6 - Comparing Two Population Variances: Independent SamplingChapter 9 - Design Of Experiments And Analysis Of VarianceChapter 9.1 - Elements Of A Designed ExperimentChapter 9.2 - The Completely Randomized Design: Single FactorChapter 9.3 - Multiple Comparisons Of MeansChapter 9.4 - The Randomized Block DesignChapter 9.5 - Factorial Experiments: Two FactorsChapter 10 - Categorical Data Analysis 57 5 o&~Chapter 10.2 - Testing Category Probabilities: One-way TableChapter 10.3 - Testing Category Probabilities: Two-way (Contingency) TableChapter 11 - Simple Linear Regression 616"'Chapter 11.1 - Probabilistic ModelsChapter 11.2 - Fitting The Model: The Least Squares ApproachChapter 11.3 - Model AssumptionsChapter 11.4 - Assessing The Utility Of The Model: Making Inferences About The SlopeChapter 11.5 - The Coefficients Of Correlation And DeterminationChapter 11.6 - Using The Model For Estimation And PredictionChapter 11.7 - A Complete ExampleChapter 12 - Multiple Regression And Model BuildingChapter 12.3 - Evaluating Overall Model UtilityChapter 12.4 - Using The Model For Estimation And PredictionChapter 12.5 - Interaction ModelsChapter 12.6 - Quadratic And Other Higher-Order ModelsChapter 12.7 - Qualitative (Dummy) Variable ModelsChapter 12.8 - Models With Both Quantitative And Qualitative VariablesChapter 12.9 - Comparing Nested ModelsChapter 12.10 - Stepwise RegressionChapter 12.12 - Some Pitfalls: Estimability, Multicollinearity, And ExtrapolationChapter 13 - Methods For Quality Improvement: Statistical Process Control (Available Online)Chapter 13.4 - A Control Chart For Monitoring The Mean Of A Process: Ths~x -ChartChapter 13.5 - A Control Chart For Monitoring The Variation Of A Process: The R-ChartChapter 13.6 - A Control Chart For Monitoring The Proportion Of Defectives Generated By A Process: The p-ChartChapter 13.7 - Diagnosing The Causes Of VariationChapter 14 - Time Series: Descriptive Analyses, Models, And Forecasting (Available Online)Chapter 14.1 - Descriptive Analysis: Index NumbersChapter 14.2 - Descriptive Analysis: Exponential SmoothingChapter 14.5 - Forecasting Trends: Holt's MethodChapter 14.6 - Measuring Forecast Accuracy: Mad And RmseChapter 14.8 - Seasonal Regression ModelsChapter 14.9 - Autocorrelation And The Durbin-Watson TestChapter 15 - Nonparametric Statistics (Available Online)Chapter 15.2 - Single Population InferencesChapter 15.3 - Comparing Two Populations: Independent SamplesChapter 15.4 - Comparing Two Populations: Paired Difference ExperimentChapter 15.5 - Comparing Three Or More Populations: Completely Randomized DesignChapter 15.6 - Comparing Three Or More Populations: Randomized Block DesignChapter 15.7 - Rank Correlation
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