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ISBN: 9789354640179

INR 1199

Description

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.

1. Introduction to Statistics and Business Analytics

1.1 Basic Statistical Concepts

1.2 Data Measurement

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

4.5 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

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