MyLab Statistics for Business Stats wit...13th EditionJames T. McClave, P. George Benson, Terry Sincich Publisher: PEARSONISBN: 9780134748610
MyLab Statistics for Business Stats wit...
Solutions for MyLab Statistics for Business Stats with Pearson eText -- Standalone Access Card -- for Statistics for Business and Economics
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
Book Details
Real Data. Real Decisions. Real Business. Now in its 13th Edition, Statistics for Business and Economics introduces statistics in the context of contemporary business. Emphasizing statistical literacy in thinking, the text applies its concepts with real data and uses technology to develop a deeper conceptual understanding. Examples, activities, and case studies foster active learning in the classroom while emphasizing intuitive concepts of probability and teaching students to make informed business decisions. The 13th Edition continues to highlight the importance of ethical behavior in collecting, interpreting, and reporting on data, while also providing a wealth of new and updated exercises and case studies.
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