Black, Ken <br/><br/>
Business statistics:  contemporary decision making 

 - 10th 
 - New Delhi  Wiley India Pvt. Ltd.  2022 
 - xviii, 832 p. 
<br/><br/> Table of content:<br/>Introduction to Statistics and Business Analytics<br/>1.1 Basic Statistical Concepts<br/><br/>1.2 Data Measurement<br/><br/>1.3 Introduction to Business Analytics<br/><br/>2     Visualizing Data with Charts and Graphs<br/><br/>2.1 Frequency Distributions<br/><br/>2.2 Quantitative Data Graphs<br/><br/>2.3 Qualitative Data Graphs<br/><br/>2.4 Charts and Graphs for Two Variables<br/><br/>2.5 Visualizing Time-Series Data<br/><br/>3 Descriptive Statistics<br/><br/>3.1 Measures of Central Tendency<br/><br/>3.2 Percentiles and Quartiles<br/><br/>3.3 Measures of Variability<br/><br/>3.4 Measures of Shape<br/><br/>3.5 Business Analytics Using Descriptive Statistics<br/><br/>4 Probability<br/><br/>4.1 Introduction to Probability<br/><br/>4.2 Structure of Probability<br/><br/>4.3 Marginal, Union, Joint, and Conditional Probabilities<br/><br/>4.4 Addition Laws<br/><br/>4.5 Multiplication Laws<br/><br/>4.6 Conditional Probability<br/><br/>5 Discrete Probability Distributions<br/><br/>5.1 Random Variables<br/><br/>5.2 Discrete Random Variables<br/><br/>5.3 Describing a Discrete Distribution<br/><br/>5.4 Bernoulli Distribution<br/><br/>5.5 Binomial Distribution<br/><br/>5.6 Negative Binomial Distribution<br/><br/>5.7 Poisson Distribution<br/><br/>5.8 Geometric Distribution<br/><br/>5.9 Hypergeometric Distribution<br/><br/>6 Continuous Probability Distributions<br/><br/>6.1 Discrete versus Continuous Probability Distributions<br/><br/>6.2 The Uniform Distribution<br/><br/>6.3 Normal Distribution<br/><br/>6.4 Using the Normal Curve to Approximate Binomial Distribution Problems<br/><br/>6.5 Exponential Distribution<br/><br/>7 Sampling and Sampling Distributions<br/><br/>7.1 Sampling<br/><br/>7.2 Sampling Distribution of Sample Mean<br/><br/>7.3 Sampling Distribution of Sample Proportion<br/><br/>8 Statistical Inference: Estimation for Single Populations<br/><br/>8.1 Estimating the Population Mean Using the z Statistic (σ Known)<br/><br/>8.2 Estimating the Population Mean Using the t Statistic (σ Unknown)<br/><br/>8.3 Estimating the Population Proportion<br/><br/>8.4 Estimating the Population Variance<br/><br/>8.5 Estimating Sample Size<br/><br/>9 Statistical Inference: Hypothesis Testing for Single Populations<br/><br/>9.1 Introduction to Hypothesis Testing<br/><br/>9.2 Testing Hypotheses About a Population Mean Using the z Statistic (σ Known)<br/><br/>9.3 Testing Hypotheses About a Population Mean Using the t Statistic (σ Unknown)<br/><br/>9.4 Testing Hypotheses About a Proportion<br/><br/>9.5 Testing Hypotheses About a Variance<br/><br/>9.6 Solving for Type II Errors<br/><br/>10 Statistical Inferences About Two Populations<br/><br/>10.1 Hypothesis Testing and Confidence Intervals About the Difference in Two Means Using the z Statistic (Population Variances Known)<br/><br/>10.2 Hypothesis Testing and Confidence Intervals About the Difference in Two Means: Independent Samples and Population Variances Unknown<br/><br/>10.3 Statistical Inferences for Two Related Populations<br/><br/>10.4 Statistical Inferences About Two Population Proportions, p1 − p2<br/><br/>10.5 Testing Hypotheses About Two Population Variances<br/><br/>11 Analysis of Variance and Design of Experiments<br/><br/>11.1 Introduction to Design of Experiments<br/><br/>11.2 The Completely Randomized Design (One-Way ANOVA)<br/><br/>11.3 Multiple Comparison Tests<br/><br/>11.4 The Randomized Block Design<br/><br/>11.5 A Factorial Design (Two-Way ANOVA)<br/><br/>12 Simple Linear Regression and Correlation<br/><br/>12.1 Correlation<br/><br/>12.2 Introduction to Simple Linear Regression<br/><br/>12.3 Determining the Equation of the Regression Line<br/><br/>12.4 Residual Analysis<br/><br/>12.5 Standard Error of the Estimate<br/><br/>12.6 Coefficient of Determination<br/><br/>12.7 Hypothesis Tests for the Slope of the Regression Model and Testing the Overall Model<br/><br/>12.8 Estimation<br/><br/>12.9 Using Regression to Develop a Forecasting Trend Line<br/><br/>12.10 Interpreting the Output<br/><br/>13 Multiple Regression Analysis<br/><br/>13.1 The Multiple Regression Model<br/><br/>13.2 Significance Tests of the Regression Model and Its Coefficients<br/><br/>13.3 Residuals, Standard Error of the Estimate, and R2<br/><br/>13.4 Interpreting Multiple Regression Computer Output<br/><br/>14 Building Multiple Regression Models<br/><br/>14.1 Nonlinear Models: Mathematical Transformation<br/><br/>14.2 Indicator (Dummy) Variables<br/><br/>14.3 Model-Building: Search Procedures<br/><br/>14.4 Multicollinearity<br/><br/>14.5 Logistic Regression<br/><br/>15 Time-Series Forecasting and Index Numbers<br/><br/>15.1 Introduction to Forecasting<br/><br/>15.2 Smoothing Techniques<br/><br/>15.3 Trend Analysis<br/><br/>15.4 Seasonal Effects<br/><br/>15.5 Autocorrelation and Autoregression<br/><br/>15.6 Choosing an Appropriate Forecasting Model<br/><br/>15.7 Index Numbers<br/><br/>16 Analysis of Categorical Data<br/><br/>16.1 Chi-Square Goodness-of-Fit Test<br/><br/>16.2 Contingency Analysis: Chi-Square Test of Independence<br/><br/>17 Nonparametric Statistics<br/><br/>17.1 Runs Test<br/><br/>17.2 Mann-Whitney U Test<br/><br/>17.3 Wilcoxon Matched-Pairs Signed Rank Test<br/><br/>17.4 Kruskal-Wallis Test<br/><br/>17.5 Friedman Test<br/><br/>17.6 Spearman’s Rank Correlation<br/><br/>18 Statistical Quality Control<br/><br/>18.1 Introduction to Quality Control<br/><br/>18.2 Process Analysis<br/><br/>18.3 Control Charts<br/><br/>19 Bayesian Statistics and Decision Analysis<br/><br/>19.1 Revision of Probabilities: Bayes’ Theorem<br/><br/>19.2 An Overview of Decision Analysis<br/><br/>19.3 The Decision Table and Decision-making Under Certainty<br/><br/>19.4 Decision-making Under Uncertainty<br/><br/>19.5 Decision-making Under Risk<br/><br/>19.6 Utility<br/><br/>19.7 Revising Probabilities in Light of Sample Information<br/><br/>Appendix A Tables<br/><br/>Appendix B Answers to Selected Odd-Numbered Quantitative Problems<br/><br/>Glossary<br/><br/>Index<br/>[https://www.wileyindia.com/business-statistics-10ed-an-indian-adaptation-for-contemporary-decision-making.html] 
<br/><br/> 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.<br/>(https://www.wileyindia.com/business-statistics-10ed-an-indian-adaptation-for-contemporary-decision-making.html) 
<br/><br/><label>ISBN: </label>9789354640179 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Singh, Sanjeet
<br/><br/><label>Dewey Class. No.: </label>519.5  / BLA