Description
Wiley India Business Statistics 10Th Edition by Ken Black and Sanjeet Singh
Business Statistics continues the tradition of presenting and explaining the wonders of business statistics through a clear, complete, student-friendly pedagogy. In this 10th edition, author Ken Black uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today’s workplace.Introduction to Statistics and Business AnalyticsContents:1.1 Basic Statistical Concepts1.2 Data Measurement1.3 Introduction to Business Analytics 2 Visualizing Data with Charts and Graphs2.1 Frequency Distributions2.2 Quantitative Data Graphs2.3 Qualitative Data Graphs2.4 Charts and Graphs for Two Variables2.5 Visualizing Time-Series Data 3 Descriptive Statistics3.1 Measures of Central Tendency3.2 Percentiles and Quartiles3.3 Measures of Variability3.4 Measures of Shape3.5 Business Analytics Using Descriptive Statistics 4 Probability4.1 Introduction to Probability4.2 Structure of Probability4.3 Marginal, Union, Joint, and Conditional Probabilities4.4 Addition Laws4.5 Multiplication Laws4.6 Conditional Probability 5 Discrete Probability Distributions5.1 Random Variables5.2 Discrete Random Variables5.3 Describing a Discrete Distribution5.4 Bernoulli Distribution5.5 Binomial Distribution5.6 Negative Binomial Distribution5.7 Poisson Distribution5.8 Geometric Distribution5.9 Hypergeometric Distribution 6 Continuous Probability Distributions6.1 Discrete versus Continuous Probability Distributions6.2 The Uniform Distribution6.3 Normal Distribution6.4 Using the Normal Curve to Approximate Binomial Distribution Problems6.5 Exponential Distribution 7 Sampling and Sampling Distributions7.1 Sampling7.2 Sampling Distribution of Sample Mean7.3 Sampling Distribution of Sample Proportion 8 Statistical Inference: Estimation for Single Populations8.1 Estimating the Population Mean Using the z Statistic (σ Known)8.2 Estimating the Population Mean Using the t Statistic (σ Unknown)8.3 Estimating the Population Proportion8.4 Estimating the Population Variance8.5 Estimating Sample Size 9 Statistical Inference: Hypothesis Testing for Single Populations9.1 Introduction to Hypothesis Testing9.2 Testing Hypotheses About a Population Mean Using the z Statistic (σ Known)9.3 Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown)9.4 Testing Hypotheses About a Proportion9.5 Testing Hypotheses About a Variance9.6 Solving for Type II Errors 10 Statistical Inferences About Two Populations10.1 Hypothesis Testing and Confidence Intervals About the Difference in Two Means Using the z Statistic (Population Variances Known)10.2 Hypothesis Testing and Confidence Intervals About the Difference in Two Means: Independent Samples and Population Variances Unknown10.3 Statistical Inferences for Two Related Populations10.4 Statistical Inferences About Two Population Proportions, p1 − p210.5 Testing Hypotheses About Two Population Variances 11 Analysis of Variance and Design of Experiments11.1 Introduction to Design of Experiments11.2 The Completely Randomized Design (One-Way ANOVA)11.3 Multiple Comparison Tests11.4 The Randomized Block Design11.5 A Factorial Design (Two-Way ANOVA) 12 Simple Linear Regression and Correlation12.1 Correlation12.2 Introduction to Simple Linear Regression12.3 Determining the Equation of the Regression Line12.4 Residual Analysis12.5 Standard Error of the Estimate12.6 Coefficient of Determination12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model12.8 Estimation12.9 Using Regression to Develop a Forecasting Trend Line12.10 Interpreting the Output 13 Multiple Regression Analysis13.1 The Multiple Regression Model13.2 Significance Tests of the Regression Model and Its Coefficients13.3 Residuals, Standard Error of the Estimate, and R213.4 Interpreting Multiple Regression Computer Output 14 Building Multiple Regression Models14.1 Nonlinear Models: Mathematical Transformation14.2 Indicator (Dummy) Variables14.3 Model-Building: Search Procedures14.4 Multicollinearity14.5 Logistic Regression 15 Time-Series Forecasting and Index Numbers15.1 Introduction to Forecasting15.2 Smoothing Techniques15.3 Trend Analysis15.4 Seasonal Effects15.5 Autocorrelation and Autoregression15.6 Choosing an Appropriate Forecasting Model15.7 Index Numbers 16 Analysis of Categorical Data16.1 Chi-Square Goodness-of-Fit Test16.2 Contingency Analysis: Chi-Square Test of Independence 17 Nonparametric Statistics17.1 Runs Test17.2 Mann-Whitney U Test17.3 Wilcoxon Matched-Pairs Signed Rank Test17.4 Kruskal-Wallis Test17.5 Friedman Test17.6 Spearman’s Rank Correlation 18 Statistical Quality Control18.1 Introduction to Quality Control18.2 Process Analysis18.3 Control Charts 19 Bayesian Statistics and Decision Analysis19.1 Revision of Probabilities: Bayes’ Theorem19.2 An Overview of Decision Analysis19.3 The Decision Table and Decision-making Under Certainty19.4 Decision-making Under Uncertainty19.5 Decision-making Under Risk19.6 Utility19.7 Revising Probabilities in Light of Sample Information Appendix A TablesAppendix B Answers to Selected Odd-Numbered Quantitative ProblemsGlossaryIndex About the AuthorKen Black is currently professor of quantitative management in the College of Business at the University of Houston–Clear Lake. Since joining the faculty of UHCL in 1979, Professor Black has taught all levels of statistics courses, business analytics, forecasting, management science, market research, and production/operations management. He received the 2014 Outstanding Professor Alumni Award from UHCL.Sanjeet Singh is currently a professor in the Decision Sciences Area at the Indian Institute of Management (IIM) Lucknow. He was working as a professor in the Operations Management Area at IIM Calcutta. He served IIM Calcutta for more than 13 years at different faculty and administrative positions.