Essentials of Business Statistics ISE

Description

Essentials of Business Statistics: Communicating with Numbers is a core statistics textbook that sparks student interest and bridges the gap between how statistics is taught and how practitioners think about and apply statistical methods. Throughout the text, the emphasis is on communicating with numbers rather than on number crunching. By incorporating the perspective of professional users, the subject matter is more relevant and the presentation of material more straightforward for students. Cover Essentials of Business Statistics Dedication About the Authors Acknowledgments Brief Contents Contents Chapter 1: Data and Data Preparation 1.1: Types of Data Sample and Population Data Cross-Sectional and Time Series Data Structured and Unstructured Data Big Data Data on the Web 1.2: Variables and Scales of Measurement The Measurement Scales The Nominal Scale The Ordinal Scale The Interval Scale The Ratio Scale 1.3: Data Preparation Counting and Sorting A Note on Handling Missing Values Subsetting 1.4: WRITING WITH DATA Chapter 2: Data Visualization 2.1: Methods to Visualize a Categorical Variable A Frequency Distribution for a Categorical Variable A Bar Chart A Pie Chart Cautionary Comments When Constructing Graphs 2.2: Methods to Visualize a Numerical Variable A Frequency Distribution for a Numerical Variable A Histogram 2.3: Methods to Visualize the Relationship between Two Categorical Variables A Contingency Table Clustered and Stacked Column Charts 2.4: Methods to Visualize the Relationship between Two Numerical Variables A Scatterplot A Line Chart More Cautionary Comments When Constructing Graphs 2.5: WRITING WITH DATA Chapter 3: SUMMARY Measures 3.1: Measures of Location Measures of Central Location The Mean The Median The Mode Using Excel to Calculate Measures of Central Location Note on Symmetry The Weighted Mean Calculating the Means of Subgroups Another Measure of Location A Percentile 3.2: Measures of Dispersion The Range and the Interquartile Range The Mean Absolute Deviation The Variance and the Standard Deviation The Coefficient of Variation 3.3: Mean-Variance Analysis and the Sharpe Ratio 3.4: Analysis of Relative Location ChebyshevÕs Theorem The Empirical Rule A z-Score A Boxplot 3.5: Measures of Association 3.6: WRITING WITH DATA Chapter 4: Introduction to Probability 4.1: Fundamental Probability Concepts Events Assigning Probabilities 4.2: Rules of Probability 4.3: Contingency Tables and Probabilities 4.4: The Total Probability Rule and BayesÕ Theorem Extensions of the Total Probability Rule and BayesÕ Theorem 4.4: WRITING WITH DATA Chapter 5: Discrete Probability Distributions 5.1: Random Variables and Discrete Probability Distributions The Discrete Probability Distribution 5.2: Expected Value, Variance, and Standard Deviation 5.3: The Binomial Distribution Using Excel to Find Binomial Probabilities 5.4: The Poisson Distribution Using Excel to Find Poisson Probabilities 5.5: The Hypergeometric Distribution Using Excel to Find Hypergeometric Probabilities 5.6: WRITING WITH DATA Chapter 6: Continuous Probability Distributions 6.1: Continuous Random Variables and the Uniform Distribution The Continuous Uniform Distribution 6.2: The Normal Distribution Characteristics of the Normal Distribution Finding the Probability for a Given Variable Value The Standard Normal Distribution Finding the Variable Value for a Given Probability A Note on the Normal Approximation of the Binomial Distribution 6.3: The Exponential Distribution 6.4: WRITING WITH DATA Chapter 7: Sampling and Sampling Distributions 7.1: Sampling Sampling Biases Classic Case of a ÒBadÓ Sample: The Literary Digest Debacle of 1936 TrumpÕs Stunning Victory in 2016 Sampling Methods Using Excel to Generate a Simple Random Sample 7.2: The Sampling Distribution of the Sample Mean The Expected Value and the Standard Error of the Sample Mean Sampling from a Normal Population The Central Limit Theorem for the Sample Mean 7.3: The Sampling Distribution of the Sample Proportion The Expected Value and the Standard Error of the Sample Proportion The Central Limit Theorem for the Sample Proportion 7.4: Statistical Quality Control A Control Chart The x Chart The p Chart Using Excel to Create a Control Chart 7.5: WRITING WITH DATA Chapter 8: Interval Estimation 8.1: Confidence Interval for the Population Mean when is Known Constructing a Confidence Interval for When Is Known The Width of a Confidence Interval Using Excel to Construct a Confidence Interval for When Is Known 8.2: Confidence Interval for the Population Mean when is Unknown The t Distribution Summary of the tdf Distribution Finding tdf Values Constructing a Confidence Interval for When Is Unknown Using Excel to Construct a Confidence Interval for When Is Unknown 8.3: Confidence Interval for the Population Proportion 8.4: Selecting the Required Sample Size Selecting n to Estimate Selecting n to Estimate p 8.5: WRITING WITH DATA Chapter 9: Hypothesis Testing 9.1: Introduction to Hypothesis Testing Defining the Null and the Alternative Hypotheses Type I and Type II Errors 9.2: Hypothesis Test for the Population Mean When is Known The p-Value Approach Confidence Intervals and Two-Tailed Hypothesis Tests One Last Remark 9.3: Hypothesis Test for the Population Mean When is Unknown 9.4: Hypothesis Test for the Population Proportion 9.5: WRITING WITH DATA Appendix 9.1: The Critical Value Approach Chapter 10: Comparisons Involving Means 10.1: Inference Concerning the Difference between Two Population Means Confidence Interval for 1 - 2 Hypothesis Test for 1 - 2 Using Excel for Testing Hypotheses about 1 - 2 10.2: Inference Concerning the Mean Difference Recognizing a Matched-Pairs Experiment Confidence Interval for D Hypothesis Test for D Using ExcelÕs Analysis Toolpak for Testing Hypotheses about D 10.3: Inference Concerning Differences Among Many Means Between-Treatments Estimate of 2: MSTR Within-Treatments Estimate of 2: MSE The F Distribution Using Excel to Construct a One-Way ANOVA Table 10.4: WRITING WITH DATA Chapter 11: Comparisons Involving Proportions 11.1: Inference Concerning the Difference between Two Proportions Confidence Interval for p1 _ p2 Hypothesis Test for p1 _ p2 11.2: Goodness-of-Fit Test forÊaÊMultinomial Experiment Finding Expected Frequencies The X2 Distribution 11.3: Chi-Square Test for Independence Finding Expected Frequencies 11.4: WRITING WITH DATA Chapter 12: Regression Analysis 12.1: The Simple Linear Regression Model Model Development and Estimation Using Excel to Estimate a Simple Linear Regression Model 12.2: The Multiple Linear Regression Model Using Dummy Variables in Regression Using Excel to Estimate a Multiple Linear Regression Model 12.3: Model Selection Goodness-of-Fit Measures The Standard Error of the Estimate, se The Coefficient of Determination, R2 The Adjusted R2 Tests of Significance Test of Joint Significance Test of Individual Significance A Test for a Nonzero Slope Coefficient A Note on Tests of Significance with Big Data Reporting Regression Results 12.4: Model Assumptions and Common Violations Residual Plots Detecting Nonlinearities Remedy Detecting Multicollinearity Remedy Detecting Changing Variability Remedy Detecting Correlated Observations Remedy Summary of Regression Modeling Using Excel to Construct Residual Plots 12.5: WRITING WITH DATA Chapter 13: More Topics in Regression Analysis 13.1: Categorical Variable with Multiple Categories 13.2: Regression Models with Interaction Variables The Interaction of Two Dummy Variables The Interaction of a Dummy Variable and a Numerical Variable 13.3: The Quadratic Regression Model 13.4: WRITING WITH DATA Chapter 14: Forecasting with Time Series Data 14.1: The Forecasting Process for Time Series Forecasting Methods Model Selection Criteria 14.2: Simple Smoothing Techniques The Moving Average Technique The Simple Exponential Smoothing Technique Using Excel for Moving Averages and Exponential Smoothing 14.3: Linear Regression Models for Trend and Seasonality The Linear Trend Model The Linear Trend Model with Seasonality 14.4: Polynomial Regression Models for Trend and Seasonality The Polynomial Trend Model The Polynomial Trend Model with Seasonality 14.5: Causal Forecasting Methods Lagged Regression Models 14.6: WRITING WITH DATA Appendixes Appendix: A: Big Data Sets: Variable Description and Data Dictionary Appendix: B: Getting Started with Excel APPENDIX: C: Brief Answers to Select Even-Numbered Exercises Index