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Statistics for Business: Decision Making and Analysis, 3rd Edition

Name: Statistics for Business: Decision Making and Analysis, 3rd Edition
ISBN: 9780136759102

by Robert Stine, Dean Foster

January 2017

Beginner

882 pages

203h 41m

English

Pearson

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Statistics for Business Decision Making and Analysis
About the Authors
Contents
Preface
What’s New in This EditionCoverage and OrganizationFeatures
Student Resources
Instructor ResourcesTechnology Resources
Index of Applications
Accounting
Advertising
Agriculture
Automotive

Banking
Business (General)
Company Names
Construction
Consumers
Demographics
Distribution and Operations Management
E-Commerce
Economics
Education
Energy
Environment
Finance and Investments
Food/Drink
Games
Government
Human Resource Management/Personnel
Insurance
Labor
Law
Management
Manufacturing
Marketing
Media and Entertainment
Pharmaceuticals, Medicine, and Health
Quality Control
Real Estate
Salary and Benefits
Sales and Retail
Science
Service Industries
Sports
Surveys and Opinion Polls
Technology
Transportation
Part I Variation
Chapter 1 Introduction
1.1 What Is Statistics?
VariationPatterns and Models
1.2 Previews
Predicting EmploymentPricing a Car
Chapter 2 Data
Running a Business is Hard Work.
2.1 Data Tables
Rows and Columns
2.2 Categorical and Numerical Data
Measurement ScalesWhat Do You Think?
2.3 Recoding and Aggregation
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
2.4 Time Series
2.5 Further Attributes of Data
Sources of DataMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesAbout the Data
Exercises
Mix and MatchTrue/FalseThink About It
Chapter 3 Describing Categorical Data
Consumers Spend Billions of Dollars Online.
3.1 Looking at Data
VariationFrequency Table
3.2 Charts of Categorical Data
Bar ChartPie ChartWhat Do You Think?
3.3 The Area Principle
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
3.4 Mode and Median
Chapter Summary
Key TermsObjectivesAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 4 Describing Numerical Data
A Standard iPhone 6 Comes with 16 Gigabytes (GB) of Memory.
4.1 Summaries of Numerical Variables
PercentilesWhat Do You Think?AveragesMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsWhat Do You Think?
4.2 Histograms
Histograms versus Bar ChartsThe White Space RuleWidth of Histogram Intervals
4.3 Boxplots
Combining Boxplots with HistogramsWhat Do You Think?
4.4 Shape of a Distribution
ModesSymmetry and SkewnessMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsWhat Do You Think?Bell-Shaped Distributions and the Empirical RuleStandardizingWhat Do You Think?
4.5 Epilog
Chapter Summary
Key TermsObjectivesFormulasMean (Average)VarianceStandard Deviation (SD)Z-ScoreCoefficient of VariationAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 5 Association between Categorical Variables
Busy Web Sites Charge for Running Ads on Their Pages.
5.1 Contingency Tables
Marginal and Conditional DistributionsWhat Do You Think?Stacked Bar ChartsMosaic PlotsMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsWhat Do You Think?
5.2 Lurking Variables and Simpson’s Paradox
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
5.3 Strength of Association
Chi-SquaredWhat Do You Think?Cramer’s VMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasChi-SquaredCramer’s VAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 6 Association between Quantitative Variables
What Does It Cost To Heat a Home for the Winter?
6.1 Scatterplots
6.2 Association in Scatterplots
Visual Test for AssociationWhat Do You Think?Describing Association in a ScatterplotWhat Do You Think?
6.3 Measuring Association
CovarianceCorrelationWhat Do You Think?
6.4 Summarizing Association with a Line
Slope-Intercept FormLines and PredictionNonlinear Patterns
6.5 Spurious Correlation
What Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
6.6 Correlation Matrix
Chapter Summary
Key TermsObjectivesFormulasCovarianceCorrelationExpressions Relating Covariance and CorrelationCorrelation LineAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Statistics in Action Case: Financial Time Series
Stock Prices
Stock PricesSome Details: Stock Splits
Stock Returns
Simple Time SeriesHistograms
Value at Risk
Case Summary
Key Terms
Formula
Return on an Investment
About the Data
Questions for Thought
Statistics in Action Case: Executive Compensation
Income and Skewness
Log Transformation
Utility for WealthLogs and Histograms
Association and Transformations
Case Summary
Key Terms
About the Data
Questions for Thought
Part II Probability
Chapter 7 ProbabilityThousands of Customers Dial Toll-Free Telephone Numbers to Get Help.
7.1 From Data to Probability
The Law of Large NumbersWhat Do You Think?
7.2 Rules for Probability
Three Essential RulesThe Complement and Addition RulesAn ExampleWhat Do You Think?
7.3 Independent Events
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsBoole’s Inequality
Chapter Summary
Key TermsObjectivesFormulas and NotationComplement of an EventUnion of EventsIntersection of EventsBoole’s Inequality
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 8 Conditional Probability
Education Affects the Income That You Can Expect to Earn.
8.1 From Tables to Probabilities
Joint ProbabilityMarginal ProbabilityConditional ProbabilityWhat Do You Think?What Do You Think?
8.2 Dependent Events
The Multiplication RuleWhat Do You Think?Order MattersIndependence in Venn DiagramsWhat Do You Think?
8.3 Organizing Probabilities
Probability TreesProbability Tables
8.4 Order in Conditional Probabilities
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsBayes’ RuleMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasConditional ProbabilityMultiplication RuleBayes’ RuleAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 9 Random Variables
Day Trading Is Popular with Young People Around the World.
9.1 Random Variables
Graphs of Random VariablesRandom Variables as ModelsWhat Do You Think?
9.2 Properties of Random Variables
Mean of a Random VariableExpected ValueWhat Do You Think?Variance and Standard DeviationMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
9.3 Properties of Expected Values
Adding or Subtracting a ConstantMultiplying by a ConstantWhat Do You Think?
9.4 Comparing Random Variables
Sharpe Ratio
Chapter Summary
Key TermsObjectivesFormulasMean or Expected Value of a Discrete Random Variable XVariance of a Discrete Random VariableAdding a Constant to a Random VariableMultiplying a Random Variable by a ConstantAdding a Constant and Multiplying by a ConstantSharpe Ratio of the Random Variable X with Mean μ and Variance σ2About the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 10 Association between Random Variables
The Two Largest Stock Exchanges in the World
10.1 Portfolios and Random Variables
Two Random VariablesComparisons and the Sharpe Ratio
10.2 Joint Probability Distribution
Independent Random VariablesWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsDependent Random Variables
10.3 Sums of Random Variables
What Do You Think?
10.4 Dependence Between Random Variables
CovarianceCovariance and SumsCorrelationCovariance, Correlation, and Independence
10.5 IID Random Variables
IID DataWhat Do You Think?
10.6 Weighted Sums
Variance of DifferencesMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasJoint Probability Distribution of Two Random VariablesMultiplication Rule for Expected Values.Addition Rule for Expected Values of SumsAddition Rule for Variances of SumsCovariance Between Random VariablesCorrelation Between Random VariablesAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 11 Probability Models for Counts
Pharmaceutical Advertising Appears On Television,
11.1 Random Variables for Counts
Bernoulli Random VariableCounting SuccessesWhat Do You Think?
11.2 Binomial Model
AssumptionsFinite Populations
11.3 Properties of Binomial Random Variables
Mean and VarianceBinomial ProbabilitiesSummaryWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
11.4 Poisson Model
Poisson Random VariablePoisson ModelMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasBinomial Random Variable, Y ~ Bi(n, p)Poisson Random Variable, X ~ Poisson (λ)
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 12 The Normal Probability Model
Prices of Stock Plummeted in October 1987.
12.1 Normal Random Variable
Central Limit TheoremNormal Probability DistributionShifts and Scales
12.2 The Normal Model
What Do You Think?StandardizingThe Empirical Rule, RevisitedMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsUsing Normal TablesWhat Do You Think?
12.3 Percentiles
What Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
12.4 Departures from Normality
Normal Quantile PlotSkewness and Kurtosis
Chapter Summary
Key TermsObjectivesFormulasProbability DistributionSkewnessKurtosisAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Statistics in Action Case: Managing Financial Risk
Investment Risk
The Dice Game
Dice InvestmentsSimulationA Two-Investment Portfolio
Understanding What Happens
Random VariablesProperties of a PortfolioVolatility Drag
Real Investments
Broader Implications
Case Summary
Key Terms
Questions for Thought
Statistics in Action Case: Modeling Sampling Variation
A Sampling Experiment
A Model for CountsUsing the Model
The Central Limit Theorem
Counting Possibilities
Using a Normal Model
Weighing in Place of CountingUsing NormalityDoes It Matter?
Case Summary
Questions for Thought
Part III Inference
Chapter 13 Samples and SurveysThe Claim “Ranked Tops in Initial Quality” Often Appears in Advertisements for New Cars.
13.1 Two Surprising Properties of Samples
RandomizationSample SizeWhat Do You Think?Simple Random SampleIdentifying the Sampling FrameWhat Do You Think?
13.2 Variation
Estimating ParametersSampling VariationMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
13.3 Alternative Sampling Methods
Stratified and Cluster SamplesMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsCensusVoluntary ResponseConvenience SamplesWhat Do You Think?
13.4 Questions to Ask
Chapter Summary
Key TermsObjectivesAbout the Data
Exercises
Mix and MatchTrue/FalseThink About It
Chapter 14 Sampling Variation and Quality
Delivery Services Love GPS Devices.
14.1 Sampling Distribution of the Mean
Benefits of AveragingNormal ModelsWhat Do You Think?Standard Error of the MeanSampling DistributionWhat Do You Think?
14.2 Control Limits
Type I and Type II ErrorsWhat Do You Think?Setting the Control LimitsBalancing Type I and Type II ErrorsWhat Do You Think?
14.3 Using a Control Chart
Repeated TestingRecognizing a Problem
14.4 Control Charts for Variation
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasSampling DistributionStandard Error of the MeanSample Size ConditionUpper and Lower Control LimitsAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 15 Confidence Intervals
The Federal Reserve Says That U.S. Households Have Accumulated Close to $12 Trillion in Outstanding Debt.
15.1 Ranges for Parameters
Confidence Interval for the ProportionWhat Do You Think?ChecklistAssumptions and ConditionsSample SizeWhat Do You Think?
15.2 Confidence Interval for the Mean
Student’s t-Distributiont-Interval for the MeanChecklistWhat Do You Think?
15.3 Interpreting Confidence Intervals
Common Confusions
15.4 Manipulating Confidence Intervals
Combining Confidence IntervalsChanging the ProblemWhat Do You Think?
15.5 Margin of Error
Determining Sample SizeWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasz-Interval for the Proportiont-Interval for the MeanMargin of ErrorAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 16 Statistical Tests
Spam Is Annoying. Junk Email Takes Time to Delete, and You Sometimes Mistakenly Throw Out Messages You Want Along with the Junk.
16.1 Concepts of Statistical Tests
Null and Alternative HypothesesWhat Do You Think?Type I and II ErrorsPrevious Hypothesis TestsWhat Do You Think?Test StatisticWhat Do You Think?
16.2 Testing the Proportion
α Levelz-TestWhat Do You Think?p-ValueType II ErrorSummaryMotivation State The QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize The Results
16.3 Testing The Mean
t-Statistict-Test and p-ValueSummaryMotivation State the QuestionMethod Describe The Data and Select an ApproachMechanics Do The AnalysisMessage Summarize The Results
16.4 Significance Versus Importance
16.5 Confidence Interval or Test?
Chapter Summary
Key TermsObjectivesFormulasOne-Sided z-Test of a ProportionOne-Sided t-Test of a MeanAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 17 Comparison
Diets Are Big Business.
17.1 Types of Comparisons
17.2 Data for Comparisons
ExperimentsConfoundingWhat Do You Think?
17.3 Two-Sample z-Test for Proportions
17.4 Two-Sample Confidence Interval for Proportions
Interpreting the Confidence IntervalChecklistWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
17.5 two-Sample t-Test
What Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize The Results
17.6 Confidence Interval for the Difference Between Means
Interpreting the Confidence IntervalChecklist (see the two-sample t-test)Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
17.7 Paired Comparisons
ChecklistMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasStandard Error of the Difference Between Two Sample MeansTwo-Sample t-Test for the Difference in MeansTwo-Sample Confidence Interval for the Difference in MeansAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 18 Inference for Counts
Online Marketing Allows a Retailer to Customize Sales Pitches.
18.1 Chi-Squared Tests
18.2 Test of Independence
Hypotheses for the chi-squared testWhat Do You Think?Calculating χ2What Do You Think?Plots of the Chi-Squared Test of IndependenceConditionsThe Chi-Squared DistributionGetting the p-ValueSummary: Chi-Squared Test of IndependenceConnection to Two-Sample TestsWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
18.3 General versus Specific Hypotheses
18.4 Tests of Goodness of Fit
Testing for RandomnessMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsTesting the Fit of a Probability ModelMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasDegrees of Freedom (d)Chi-Squared StatisticAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Statistics in Action Case: Rare Events
Rare Events
Clinical TrialsDefaults on Corporate BondsElectronic Components
Inference for Rare Events
Rule of ThreeUsing the Rule of ThreeDetermining nConsiderations in Finance
Case Summary
Key Terms
Questions for Thought
About the Data
Statistics in Action Case: Data Mining Using Chi-Squared
Managing Inventories
Looking for AssociationChi-squared Test of AssociationEquality of Several Proportions
Data Mining
Standardizing with p-valuesRelated Methods
Case Summary
Key Terms
Questions for Thought
About the Data
Part IV Regression Models
Chapter 19 Linear PatternsMany Factors Affect the Price of a Commodity.
19.1 Fitting a Line to Data
Equation of a LineLeast Squares
19.2 Interpreting the Fitted Line
Interpreting the InterceptInterpreting the SlopeWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsWhat Do You Think?
19.3 Properties of Residuals
Standard Deviation of the Residuals
19.4 Explaining Variation
Summarizing the Fit of a Line
19.5 Conditions for Simple Regression
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasLinear EquationSlopeInterceptFitted ValueResidualStandard Deviation of the Residualsr-squaredAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 20 Curved Patterns
The Price of Gasoline Can Be a Painful Reminder of the Laws of Supply and Demand.
20.1 Detecting Nonlinear Patterns
ScatterplotsResidual Plots
20.2 Transformations
20.3 Reciprocal Transformation
What Do You Think?Comparing Linear and Nonlinear EquationsVisual ComparisonsSubstantive ComparisonWhat Do You Think?
20.4 Logarithm Transformation
Scatterplots and Residual PlotsComparing EquationsElasticityMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 21 The Simple Regression Model
Customized Manufacturing Is a Growth Industry in the United States As Businesses Adjust to Worldwide Competition.
21.1 The Simple Regression Model
Linear on AverageDeviations from the MeanData-Generating ProcessWhat Do You Think?
21.2 Conditions for the SRM
Modeling ProcessWhat Do You Think?
21.3 Inference in Regression
Standard ErrorsRole of SoftwareConfidence IntervalsHypothesis TestsWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
21.4 Prediction Intervals
Leveraging the SRMReliability of Prediction IntervalsMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasSimple Regression ModelChecklist of Conditions for the Simple Regression ModelStandard Error of the SlopeStandard Error of the InterceptStandard Error of PredictionAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 22 Regression Diagnostics
The Internet Has Changed Many Business Models, Generating New Approaches to Retail Marketing, Medical Care, and Service Industries.
22.1 Changing Variation
Fixed Costs, Marginal Costs, and Variable CostsDetecting Differences in VariationConsequences of Different VariationWhat Do You Think?Fixing the Problem: Revise the ModelMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsComparing Models with Different Responses
22.2 Outliers
Consequences of an OutlierWhat Do You Think?Extrapolating Prediction IntervalsFixing the Problem: More Information
22.3 Dependent Errors and Time Series
Detecting Dependence Using the Durbin-Watson StatisticConsequences of DependenceMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasDurbin-Watson StatisticAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 23 Multiple Regression
Expanding Businesses Must Decide Where to Locate New Outlets.
23.1 The Multiple Regression Model
What Do You Think?
23.2 Interpreting Multiple Regression
Scatterplot MatrixWhat Do You Think?R-squared and seWhat Do You Think?Marginal and Partial SlopesPath DiagramWhat Do You Think?
23.3 Checking Conditions
Residual Plots
23.4 Inference in Multiple Regression
Inference for the Model: F-testWhat Do You Think?Inference for One CoefficientPrediction Intervals
23.5 Steps in Fitting a Multiple Regression
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasEstimates of b0, b1 and b2Adjusted R2F-statisticStandard Deviation of ResidualsAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 24 Building Regression Models
Which Explanatory Variables Belong in a Regression Model?
24.1 Identifying Explanatory Variables
The Initial ModelIdentifying Other VariablesAdding Explanatory Variables
24.2 Collinearity
Variance Inflation FactorSigns of CollinearityWhat Do You Think?Remedies for Collinearity
24.3 Removing Explanatory Variables
Motivation State the QuestionMethod Describe the Data and Select An ApproachMechanics Do the AnalysisMessage Summarize the ResultsMotivation State the QuestionMethod Describe the Data and Select An ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasStandard Error of a Slope in Multiple RegressionVariance Inflation Factor (VIF)About the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 25 Categorical Explanatory Variables
In 2001, Six Women Filed a Lawsuit Against Wal-Mart in Federal Court in San Francisco.
25.1 Two-Sample Comparisons
Confounding VariablesSubsets and ConfoundingWhat Do You Think?
25.2 Analysis of Covariance
Regression on SubsetsCombining RegressionsWhat Do You Think?Interpreting CoefficientsWhat Do You Think?
25.3 Checking Conditions
Checking for Similar Variances
25.4 Interactions and Inference
Interactions and CollinearityParallel FitsWhat Do You Think?Motivation State the QuestionMethod Describe The Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
25.5 Regression with Several Groups
What Do You Think?
Chapter Summary
Key TermsObjectivesAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 26 Analysis of Variance
At the Dawn of the 20th Century, More Than 10 Million Americans Toiled On Farms, More Than One-Third of the Domestic Workforce.
26.1 Comparing Several Groups
Comparing Groups in PlotsRelating the t-Test to RegressionWhat Do You Think?Comparing Several Groups Using RegressionInterpreting the EstimatesANOVA Regression ModelWhat Do You Think?
26.2 Inference in Anova Regression Models
Checking ConditionsWhat Do You Think?F-Test for the Difference among MeansUnderstanding the F-TestConfidence Intervals
26.3 Multiple Comparisons
Tukey Confidence IntervalsBonferroni Confidence IntervalsWhat Do You Think?Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
26.4 Groups of Different Size
Chapter Summary
Key TermsObjectivesFormulasNotation for DataFitted Values and ResidualsStandard Error for the Difference Between Two MeansTukey Percentiles q.025,n,JAbout the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Chapter 27 Time Series
The Value of Goods Shipped Measures the Health of a Business.
27.1 Decomposing a Time Series
SmoothingExponential SmoothingWhat Do You Think?
27.2 Regression Models
Polynomial TrendsMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsAutoregressionForecasting an AutoregressionWhat Do You Think?
27.3 Checking the Model
Motivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the ResultsMotivation State the QuestionMethod Describe the Data and Select an ApproachMechanics Do the AnalysisMessage Summarize the Results
Chapter Summary
Key TermsObjectivesFormulasDurbin-Watson Statistic and AutocorrelationExponential Smoothing, Exponentially Weighted Moving Average (EWMA)About the Data
Exercises
Mix and MatchTrue/FalseThink About ItYou Do It
Statistics in Action Case: Analyzing Experiments
A Pricing Experiment
Balanced ExperimentPreliminary Data Analysis
Two-Way Analysis of Variance
ModelPlan for AnalysisChecking the ModelTesting the FactorsRegression CoefficientsInteractions and One-way Anova
Case Summary
Key Terms
Questions for Thought
Statistics in Action Case: Automated Modeling
Preparations
Data for ModelingPreliminary Analysis: Outliers and Collinearity
Saturated Model
Stepwise Regression
Avoiding Over-fittingRunning Stepwise RegressionInterpreting the Stepwise Model
Related Algorithms
Case Summary
Key Terms
Questions for Thought
Appendix Tables
Answers
Chapter 2
Mix and Match
True/False
Think About It
Chapter 3
Mix and Match
True/False
Think About It
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Chapter 4
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True/False
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You Do It
Chapter 5
Mix and Match
True/False
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Chapter 6
Mix and Match
True/False
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You Do It
Statistics in Action 1–2
Pfizer case
Executive Compensation case
Chapter 7
Mix and Match
True/False
Think About It
You Do It
Chapter 8
Mix and Match
True/False
Think About It
You Do It
Chapter 9
Mix and Match
True/False
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You Do It
Chapter 10
Mix and Match
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You Do It
Chapter 11
Mix and Match
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Chapter 12
Mix and Match
True/False
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Statistics in Action 3–4
Dice Simulation
M&Ms
Chapter 13
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Chapter 14
True/False
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Chapter 15
Mix and Match
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Chapter 16
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Chapter 17
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Chapter 18
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Statistics in Action 5–6
Page 484
Page 491
Chapter 19
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Chapter 20
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Chapter 21
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Chapter 22
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Chapter 23
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Chapter 24
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Chapter 25
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Chapter 26
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Chapter 27
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Statistics in Action 7–8
Answers for Questions, page 814
Answers for Questions, page 821
Credits
Photo CreditsFront MatterChapter 1Chapter 2Chapter 3Chapter 4Chapter 5Chapter 6Chapter 7Chapter 8Chapter 9Chapter 10Chapter 11Chapter 12Chapter 13Chapter 14Chapter 15Chapter 16Chapter 17Chapter 18Chapter 19Chapter 20Chapter 21Chapter 22Chapter 23Chapter 24Chapter 25Chapter 26Chapter 27Text CreditsChapter 5Chapter 9Back Matter
Subject Index
Numbers
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
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X
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Content preview from Statistics for Business: Decision Making and Analysis, 3rd Edition

15.4 Manipulating Confidence Intervals

We can manipulate a confidence interval to obtain ranges for related quantities. For instance, suppose that federal regulators require a bank to maintain cash reserves equivalent to 10% of its outstanding balances. How much cash is that on average per customer? Because the 95% confidence interval for μ is [$1,520 to $2,460], the 95% confidence interval for 10% of μ (0.1 × μ) is [$152 to $246].

If [L to U] is a 100(1 − α)% confidence interval for μ, then

[c \times L to c \times U]

is a 100(1 − α)% confidence interval for c × μ and

[c + L to c + U]

is a 100(1 − α)% confidence interval for c + μ.

The same rule applies if the parameter is p rather than μ. More generally, you can substitute a confidence interval for a parameter ...

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ISBN: 9780136759102

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

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Business

Soft Skills

Statistics for Business: Decision Making and Analysis, 3rd Edition

by Robert Stine, Dean Foster

15.4 Manipulating Confidence Intervals

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