book

Principles of Managerial Statistics and Data Science

Name: Principles of Managerial Statistics and Data Science
Author: Roberto Rivera
ISBN: 9781119486411

by Roberto Rivera

February 2020

Intermediate to advanced

688 pages

18h 35m

English

Wiley

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Cover
Preface
Acknowledgments
Acronyms
About the Companion Website
Principles of Managerial Statistics and Data Science
Project Objective
1 Statistics Suck; So Why Do I Need to Learn About It?
1.1 IntroductionPractice Problems1.2 Data‐Based Decision Making: Some Applications1.3 Statistics Defined1.4 Use of Technology and the New Buzzwords: Data Science, Data Analytics, and Big DataChapter ProblemsFurther Reading
2 Concepts in Statistics
2.1 IntroductionPractice Problems2.2 Type of DataPractice Problems2.3 Four Important Notions in StatisticsPractice Problems2.4 Sampling MethodsPractice Problems2.5 Data Management2.6 Proposing a Statistical StudyChapter ProblemsFurther Reading
3 Data Visualization
3.1 Introduction3.2 Visualization Methods for Categorical VariablesPractice Problems3.3 Visualization Methods for Numerical VariablesPractice Problems3.4 Visualizing Summaries of More than Two Variables SimultaneouslyPractice Problems3.5 Novel Data VisualizationChapter ProblemsFurther Reading
4 Descriptive Statistics
4.1 Introduction4.2 Measures of CentralityPractice Problems4.3 Measures of DispersionPractice Problems4.4 PercentilesPractice Problems4.5 Measuring the Association Between Two VariablesPractice Problems4.6 Sample Proportion and Other Numerical Statistics4.7 How to Use Descriptive StatisticsChapter ProblemsFurther Reading

5 Introduction to Probability
5.1 Introduction5.2 PreliminariesPractice Problems5.3 The Probability of an EventPractice Problems5.4 Rules and Properties of ProbabilitiesPractice Problems5.5 Conditional Probability and Independent EventsPractice Problems5.6 Empirical ProbabilitiesPractice Problems5.7 Counting OutcomesPractice ProblemsChapter ProblemsFurther Reading
6 Discrete Random Variables
6.1 Introduction6.2 General PropertiesPractice Problems6.3 Properties of Expected Value and VariancePractice Problems6.4 Bernoulli and Binomial Random VariablesPractice Problems6.5 Poisson DistributionPractice Problems6.6 Optional: Other Useful Probability DistributionsChapter ProblemsFurther Reading
7 Continuous Random Variables
7.1 IntroductionPractice Problems7.2 The Uniform Probability DistributionPractice Problems7.3 The Normal DistributionPractice Problems7.4 Probabilities for Any Normally Distributed Random VariablePractice Problems7.5 Approximating the Binomial DistributionPractice Problems7.6 Exponential DistributionPractice ProblemsChapter ProblemsFurther Reading
8 Properties of Sample Statistics
8.1 Introduction8.2 Expected Value and Standard Deviation ofPractice Problems8.3 Sampling Distribution of When Sample Comes From a Normal DistributionPractice Problems8.4 Central Limit TheoremPractice Problems8.5 Other Properties of EstimatorsChapter ProblemsFurther Reading
9 Interval Estimation for One Population Parameter
9.1 Introduction9.2 Intuition of a Two‐Sided Confidence Interval9.3 Confidence Interval for the Population Mean: KnownPractice Problems9.4 Determining Sample Size for a Confidence Interval forPractice Problems9.5 Confidence Interval for the Population Mean: UnknownPractice Problems9.6 Confidence Interval forPractice Problems9.7 Determining Sample Size for Confidence IntervalPractice Problems9.8 Optional: Confidence Interval forChapter ProblemsFurther Reading
10 Hypothesis Testing for One Population
10.1 Introduction10.2 Basics of Hypothesis Testing10.3 Steps to Perform a Hypothesis TestPractice Problems10.4 Inference on the Population Mean: Known Standard DeviationPractice Problems10.5 Hypothesis Testing for the Mean ( Unknown)Practice Problems10.6 Hypothesis Testing for the Population ProportionPractice Problems10.7 Hypothesis Testing for the Population Variance10.8 More on the p‐Value and Final RemarksChapter ProblemsFurther Reading
11 Statistical Inference to Compare Parameters from Two Populations
11.1 Introduction11.2 Inference on Two Population Means11.3 Inference on Two Population Means – Independent Samples, Variances KnownPractice Problems11.4 Inference on Two Population Means When Two Independent Samples are Used – Unknown VariancesPractice Problems11.5 Inference on Two Means Using Two Dependent SamplesPractice Problems11.6 Inference on Two Population ProportionsPractice ProblemsChapter ProblemsReferencesFurther Reading
12 Analysis of Variance (ANOVA)
12.1 IntroductionPractice Problems12.2 ANOVA for One FactorPractice Problems12.3 Multiple ComparisonsPractice Problems12.4 Diagnostics of ANOVA AssumptionsPractice Problems12.5 ANOVA with Two FactorsPractice Problems12.6 Extensions to ANOVAChapter ProblemsFurther Reading
13 Simple Linear Regression
13.1 Introduction13.2 Basics of Simple Linear RegressionPractice Problems13.3 Fitting the Simple Linear Regression ParametersPractice Problems13.4 Inference for Simple Linear RegressionPractice Problems13.5 Estimating and Predicting the Response VariablePractice Problems13.6 A BinaryPractice Problems13.7 Model Diagnostics (Residual Analysis)Practice Problems13.8 What Correlation Doesn't MeanChapter ProblemsFurther Reading
14 Multiple Linear Regression
14.1 Introduction14.2 The Multiple Linear Regression ModelPractice Problems14.3 Inference for Multiple Linear RegressionPractice Problems14.4 Multicollinearity and Other Modeling AspectsPractice Problems14.5 Variability Around the Regression Line: Residuals and IntervalsPractice Problems14.6 Modifying PredictorsPractice Problems14.7 General Linear ModelPractice Problems14.8 Steps to Fit a Multiple Linear Regression Model14.9 Other Regression TopicsChapter ProblemsFurther Reading
15 Inference on Association of Categorical Variables
15.1 Introduction15.2 Association Between Two Categorical VariablesPractice ProblemsChapter ProblemsFurther Reading
16 Nonparametric Testing
16.1 Introduction16.2 Sign Tests and Wilcoxon Sign‐Rank Tests: One Sample and Matched Pairs ScenariosPractice Problems16.3 Wilcoxon Rank‐Sum Test: Two Independent SamplesPractice Problems16.4 Kruskal–Wallis Test: More Than Two SamplesPractice Problems16.5 Nonparametric Tests Versus Their Parametric CounterpartsChapter ProblemsFurther Reading
17 Forecasting
17.1 Introduction17.2 Time Series ComponentsPractice Problems17.3 Simple Forecasting ModelsPractice Problems17.4 Forecasting When Data Has Trend, SeasonalityPractice Problems17.5 Assessing ForecastsChapter ProblemsFurther Reading
Appendix A: Math Notation and Symbols
A.1 SummationA.2 pth PowerA.3 InequalitiesA.4 FactorialsA.5 Exponential FunctionA.6 Greek and Statistics Symbols
Appendix B: Standard Normal Cumulative Distribution Function
Appendix C: t Distribution Critical Values
Appendix D: Solutions to Odd‐Numbered Problems
Index
End User License Agreement

Content preview from Principles of Managerial Statistics and Data Science

Appendix BStandard Normal Cumulative Distribution Function

Graph depicts the standard normal cumulative distribution function.

This table shows, for example, .

	.00	.01	.02	.03	.04	.05	.06	.07	.08	.09
3.4	.0003	.0003	.0003	.0003	.0003	.0003	.0003	.0003	.0003	.0002
3.3	.0005	.0005	.0005	.0004	.0004	.0004	.0004	.0004	.0004	.0003
3.2	.0007	.0007	.0006	.0006	.0006	.0006	.0006	.0005	.0005	.0005
3.1	.0010	.0009	.0009	.0009	.0008	.0008	.0008	.0008	.0007	.0007
3.0	.0013	.0013	.0013	.0012	.0012	.0011	.0011	.0011	.0010	.0010
2.9	.0019	.0018	.0018	.0017	.0016	.0016	.0015	.0015	.0014	.0014
2.8	.0026	.0025	.0024	.0023	.0023	.0022	.0021	.0021	.0020

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Principles of Managerial Statistics and Data Science

by Roberto Rivera

Appendix BStandard Normal Cumulative Distribution Function

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.