Basic Statistics in Business and Economics, 10th Edition
- Length: 640 pages
- Edition: 10
- Language: English
- Publisher: McGraw-Hill Education
- Publication Date: 2021-01-20
- ISBN-10: 1260716317
- ISBN-13: 9781260716313
- Sales Rank: #0 (See Top 100 Books)
Basic Statistics in Business & Economics provides students majoring in management, marketing, finance, accounting, economics, and other fields of business administration with an introductory survey of descriptive and inferential statistics. Many examples and exercises that focus on business applications are used to illustrate the application of statistics, but also relate to the current world of the college student. A previous course in statistics is not necessary, and the mathematical requirement is first-year algebra.
Students are given every step needed to be successful in a basic statistics course. This step-by-step approach enhances performance, accelerates preparedness, and significantly improves motivation. Understanding the concepts, seeing and doing plenty of examples and exercises, and comprehending the application of statistical methods in business and economics are the focus of this book.
Today, the practice of data analytics is widely applied to big data. The practice of data analytics requires skills and knowledge in several areas. Computer skills are needed to process large volumes of information. Analytical skills are needed to evaluate, summarize, organize, and analyze the information. Critical thinking skills are needed to interpret and communicate the results of processing the information. This text supports the development of basic data analytical skills with the end of each chapter sections called Data Analytics providing the instructor and student with opportunities to apply statistical knowledge and statistical software to explore several business environments. Interpretation of the analytical results is an integral part of these exercises.
A variety of statistical software is available to complement the 10th edition. Microsoft Excel includes an add-in with many statistical analyses. MegaStat is an add-in available for Microsoft Excel. Minitab and JMP are stand-alone statistical software packages available to download for either PC or Mac. In the text, Microsoft Excel, Minitab, and MegaStat are used to illustrate statistical software analyses. The text also includes references or links to Excel tutorials in Connect. These provide users with clear demonstrations using statistical software to create graphical and descriptive statistics and statistical analyses to test hypotheses.
Digital resources within McGraw Hill Connect® help students apply what they’ve learned and achieve higher outcomes in the course. Connect is the only integrated learning system that empowers students by continuously adapting to deliver precisely what they need when they need it and how they need it so that class time is more engaging and effective.
Cover Halftitle The McGraw Hill Series in Operations and Decision Sciences Title Copyright Dedication A Note from the Authors How are Chapters Organized to Engage Students and Promote Learning? How Does this Text Reinforce Student Learning? Connect Additional Resources Acknowledgments Enhancements to Basic Statistics for Business & Economics, 10e Brief Contents Contents Basic Statistics for Business & Economics 1 What Is Statistics? Introduction Why Study Statistics? What Is Meant by Statistics? Types of Statistics Descriptive Statistics Inferential Statistics Types of Variables Levels of Measurement Nominal-Level Data Ordinal-Level Data Interval-Level Data Ratio-Level Data Exercises Ethics and Statistics Basic Business Analytics Chapter Summary Chapter Exercises Data Analytics Practice Test 2 Describing Data: FREQUENCY TABLES, FREQUENCY DISTRIBUTIONS, AND GRAPHIC PRESENTATION Introduction Constructing Frequency Tables Relative Class Frequencies Graphic Presentation of Qualitative Data Exercises Constructing Frequency Distributions Relative Frequency Distribution Exercises Graphic Presentation of a Distribution Histogram Frequency Polygon Exercises Cumulative Distributions Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 3 Describing Data: NUMERICAL MEASURES Introduction Measures of Location The Population Mean The Sample Mean Properties of the Arithmetic Mean Exercises The Median The Mode Software Solution Exercises The Relative Positions of the Mean, Median, and Mode Exercises The Weighted Mean Exercises Why Study Dispersion? Range Variance Exercises Population Variance Population Standard Deviation Exercises Sample Variance and Standard Deviation Software Solution Exercises Interpretation and Uses of the Standard Deviation Chebyshev’s Theorem The Empirical Rule Exercises Ethics and Reporting Results Chapter Summary Chapter Exercises Data Analytics Practice Test 4 Describing Data DISPLAYING AND EXPLORING DATA Introduction Dot Plots Exercises Measures of Position Quartiles, Deciles, and Percentiles Exercises Box Plots Exercises Skewness Exercises Describing the Relationship between Two Variables Correlation Coefficient Contingency Tables Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 5 A Survey of Probability Concepts Introduction What Is a Probability? Approaches to Assigning Probabilities Classical Probability Empirical Probability Subjective Probability Exercises Rules of Addition for Computing Probabilities Special Rule of Addition Complement Rule The General Rule of Addition Exercises Rules of Multiplication to Calculate Probability Special Rule of Multiplication General Rule of Multiplication Contingency Tables Tree Diagrams Exercises Principles of Counting The Multiplication Formula The Permutation Formula The Combination Formula Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 6 Discrete Probability Distributions Introduction What Is a Probability Distribution? Random Variables Discrete Random Variable Continuous Random Variable The Mean, Variance, and Standard Deviation of a Discrete Probability Distribution Mean Variance and Standard Deviation Exercises Binomial Probability Distribution How Is a Binomial Probability Computed? Binomial Probability Tables Exercises Cumulative Binomial Probability Distributions Exercises Poisson Probability Distribution Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 7 Continuous Probability Distributions Introduction The Family of Uniform Probability Distributions Exercises The Family of Normal Probability Distributions The Standard Normal Probability Distribution Applications of the Standard Normal Distribution The Empirical Rule Exercises Finding Areas under the Normal Curve Exercises Exercises Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 8 Sampling, Sampling Methods, and the Central Limit Theorem Introduction Research and Sampling Sampling Methods Simple Random Sampling Systematic Random Sampling Stratified Random Sampling Cluster Sampling Exercises Sample Mean as a Random Variable Sampling Distribution of the Sample Mean Exercises The Central Limit Theorem Standard Error of the Mean Exercises Using the Sampling Distribution of the Sample Mean Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 9 Estimation and Confidence Intervals Introduction Point Estimate for a Population Mean Confidence Intervals for a Population Mean Population Standard Deviation, Known σ A Computer Simulation Exercises Population Standard Deviation, σ Unknown Exercises A Confidence Interval for a Population Proportion Exercises Choosing an Appropriate Sample Size Sample Size to Estimate a Population Mean Sample Size to Estimate a Population Proportion Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 10 One-Sample Tests of Hypothesis Introduction What Is Hypothesis Testing? Six-Step Procedure for Testing a Hypothesis Step 1: State the Null Hypothesis (H0) and the Alternate Hypothesis (H1) Step 2: Select a Level of Significance Step 3: Select the Test Statistic Step 4: Formulate the Decision Rule Step 5: Make a Decision Step 6: Interpret the Result One-Tailed and Two-Tailed Hypothesis Tests Hypothesis Testing for a Population Mean: Known Population Standard Deviation A Two-Tailed Test A One-Tailed Test p-Value in Hypothesis Testing Exercises Hypothesis Testing for a Population Mean: Population Standard Deviation Unknown Exercises A Statistical Software Solution Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 11 Two-Sample Tests of Hypothesis Introduction Two-Sample Tests of Hypothesis: Independent Samples Exercises Comparing Population Means with Unknown Population Standard Deviations Two-Sample Pooled Test Exercises Unequal Population Standard Deviations Exercises Two-Sample Tests of Hypothesis: Dependent Samples Comparing Dependent and Independent Samples Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 12 Analysis of Variance Introduction Comparing Two Population Variances The F-Distribution Testing a Hypothesis of Equal Population Variances Exercises ANOVA: Analysis of Variance ANOVA Assumptions The ANOVA Test Exercises Inferences about Pairs of Treatment Means Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 13 Correlation and Linear Regression Introduction What Is Correlation Analysis? The Correlation Coefficient Exercises Testing the Significance of the Correlation Coefficient Exercises Regression Analysis Least Squares Principle Drawing the Regression Line Exercises Testing the Significance of the Slope Exercises Evaluating a Regression Equation’s Ability to Predict The Standard Error of Estimate The Coefficient of Determination Exercises Relationships among the Correlation Coefficient, the Coefficient of Determination, and the Standard Error of Estimate Exercises Interval Estimates of Prediction Assumptions Underlying Linear Regression Constructing Confidence and Prediction Intervals Exercises Transforming Data Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test 14 Multiple Regression Analysis Introduction Multiple Regression Analysis Exercises Evaluating a Multiple Regression Equation The ANOVA Table Multiple Standard Error of Estimate Coefficient of Multiple Determination Adjusted Coefficient of Determination Exercises Inferences in Multiple Linear Regression Global Test: Testing the Multiple Regression Model Evaluating Individual Regression Coefficients Exercises Evaluating the Assumptions of Multiple Regression Linear Relationship Variation in Residuals Same for Large and Small ŷ Values Distribution of Residuals Multicollinearity Independent Observations Qualitative Independent Variables Stepwise Regression Exercises Review of Multiple Regression Chapter Summary Chapter Exercises Data Analytics Practice Test 15 Nonparametric Methods: Nominal Level Hypothesis Tests Introduction Test a Hypothesis of a Population Proportion Exercises Two-Sample Tests about Proportions Exercises Goodness-of-Fit Tests: Comparing Observed and Expected Frequency Distributions Hypothesis Test of Equal Expected Frequencies Exercises Hypothesis Test of Unequal Expected Frequencies Limitations of Chi-Square Exercises Contingency Table Analysis Exercises Chapter Summary Chapter Exercises Data Analytics Practice Test Appendixes Appendix A: Data Sets Appendix B: Tables Appendix C: Answers to Odd-Numbered Chapter Exercises & Solutions to Practice Test Appendix D: Answers to Self-Review Glossary Index Key Formula Areas under the Normal Curve
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