TY - BOOK AU - Black, Ken TI - Business statistics: contemporary decision making SN - 9789354640179 U1 - 519.5 PY - 2022/// CY - New Delhi PB - Wiley India Pvt. Ltd. KW - Singh, Sanjeet N1 - Table of content: Introduction to Statistics and Business Analytics 1.1 Basic Statistical Concepts 1.2 Data Measurement 1.3 Introduction to Business Analytics 2 Visualizing Data with Charts and Graphs 2.1 Frequency Distributions 2.2 Quantitative Data Graphs 2.3 Qualitative Data Graphs 2.4 Charts and Graphs for Two Variables 2.5 Visualizing Time-Series Data 3 Descriptive Statistics 3.1 Measures of Central Tendency 3.2 Percentiles and Quartiles 3.3 Measures of Variability 3.4 Measures of Shape 3.5 Business Analytics Using Descriptive Statistics 4 Probability 4.1 Introduction to Probability 4.2 Structure of Probability 4.3 Marginal, Union, Joint, and Conditional Probabilities 4.4 Addition Laws 4.5 Multiplication Laws 4.6 Conditional Probability 5 Discrete Probability Distributions 5.1 Random Variables 5.2 Discrete Random Variables 5.3 Describing a Discrete Distribution 5.4 Bernoulli Distribution 5.5 Binomial Distribution 5.6 Negative Binomial Distribution 5.7 Poisson Distribution 5.8 Geometric Distribution 5.9 Hypergeometric Distribution 6 Continuous Probability Distributions 6.1 Discrete versus Continuous Probability Distributions 6.2 The Uniform Distribution 6.3 Normal Distribution 6.4 Using the Normal Curve to Approximate Binomial Distribution Problems 6.5 Exponential Distribution 7 Sampling and Sampling Distributions 7.1 Sampling 7.2 Sampling Distribution of Sample Mean 7.3 Sampling Distribution of Sample Proportion 8 Statistical Inference: Estimation for Single Populations 8.1 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 Proportion 8.4 Estimating the Population Variance 8.5 Estimating Sample Size 9 Statistical Inference: Hypothesis Testing for Single Populations 9.1 Introduction to Hypothesis Testing 9.2 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 Proportion 9.5 Testing Hypotheses About a Variance 9.6 Solving for Type II Errors 10 Statistical Inferences About Two Populations 10.1 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 Unknown 10.3 Statistical Inferences for Two Related Populations 10.4 Statistical Inferences About Two Population Proportions, p1 − p2 10.5 Testing Hypotheses About Two Population Variances 11 Analysis of Variance and Design of Experiments 11.1 Introduction to Design of Experiments 11.2 The Completely Randomized Design (One-Way ANOVA) 11.3 Multiple Comparison Tests 11.4 The Randomized Block Design 11.5 A Factorial Design (Two-Way ANOVA) 12 Simple Linear Regression and Correlation 12.1 Correlation 12.2 Introduction to Simple Linear Regression 12.3 Determining the Equation of the Regression Line 12.4 Residual Analysis 12.5 Standard Error of the Estimate 12.6 Coefficient of Determination 12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model 12.8 Estimation 12.9 Using Regression to Develop a Forecasting Trend Line 12.10 Interpreting the Output 13 Multiple Regression Analysis 13.1 The Multiple Regression Model 13.2 Significance Tests of the Regression Model and Its Coefficients 13.3 Residuals, Standard Error of the Estimate, and R2 13.4 Interpreting Multiple Regression Computer Output 14 Building Multiple Regression Models 14.1 Nonlinear Models: Mathematical Transformation 14.2 Indicator (Dummy) Variables 14.3 Model-Building: Search Procedures 14.4 Multicollinearity 14.5 Logistic Regression 15 Time-Series Forecasting and Index Numbers 15.1 Introduction to Forecasting 15.2 Smoothing Techniques 15.3 Trend Analysis 15.4 Seasonal Effects 15.5 Autocorrelation and Autoregression 15.6 Choosing an Appropriate Forecasting Model 15.7 Index Numbers 16 Analysis of Categorical Data 16.1 Chi-Square Goodness-of-Fit Test 16.2 Contingency Analysis: Chi-Square Test of Independence 17 Nonparametric Statistics 17.1 Runs Test 17.2 Mann-Whitney U Test 17.3 Wilcoxon Matched-Pairs Signed Rank Test 17.4 Kruskal-Wallis Test 17.5 Friedman Test 17.6 Spearman’s Rank Correlation 18 Statistical Quality Control 18.1 Introduction to Quality Control 18.2 Process Analysis 18.3 Control Charts 19 Bayesian Statistics and Decision Analysis 19.1 Revision of Probabilities: Bayes’ Theorem 19.2 An Overview of Decision Analysis 19.3 The Decision Table and Decision-making Under Certainty 19.4 Decision-making Under Uncertainty 19.5 Decision-making Under Risk 19.6 Utility 19.7 Revising Probabilities in Light of Sample Information Appendix A Tables Appendix B Answers to Selected Odd-Numbered Quantitative Problems Glossary Index [https://www.wileyindia.com/business-statistics-10ed-an-indian-adaptation-for-contemporary-decision-making.html] N2 - 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. (https://www.wileyindia.com/business-statistics-10ed-an-indian-adaptation-for-contemporary-decision-making.html) ER -