Ken Black

Business Statistics

For Contemporary Decision Making

• Loseblattsammlung

Produktbeschreibung

Revised edition of the author's Business statistics, 2016.

Produktdetails

• Verlag: Wiley
• 10th Edition, Us edition
• Seitenzahl: 832
• Erscheinungstermin: 12. Dezember 2019
• Englisch
• Abmessung: 290mm x 229mm x 30mm
• Gewicht: 363g
• ISBN-13: 9781119607458
• ISBN-10: 1119607450
• Artikelnr.: 68054022

Inhaltsangabe

1 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 Measures of Variability
 
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 Measures of Variability
 
3.3 Measures of Shape
 
3.4 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 Random Variables and Their Probability
 
5.1 Discrete Random Variables
 
5.2 Describing a Discrete Distribution
 
5.3 Binomial Distribution
 
5.4 Poisson Distribution
 
5.5 Geometric Distribution
 
5.6 Hypergeometric Distribution
 
6 Continuous Random Variables and Normal Distributions
 
6.1 Continuous Random Variables
 
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 Data and Decision Making
 
7.1 Sampling
 
7.2 Sampling Distribution of the Sample Mean
 
7.3 Sampling Distribution of a Sample Proportion
 
8 Statistical Inference: Estimation for Single Populations
 
8.1 Estimating the Population Mean Using the z Statistic (sigma Known)
 
8.2 Estimating the Population Mean Using the t Statistic (sigma 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 (sigma
Known)
 
9.3 Testing Hypotheses About a Population Mean Using the t Statistic (sigma
Unknown)
 
9.4 Testing Hypotheses About a Proportion
 
9.5 Testing Hypotheses About a Variance
 
9.6 Solving for Type II Errors
 
10 Statistical Inference 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 Regression Analysis and Correlation
 
12.1 Correlation
 
12.2 Introduction to Simple Regression Analysis
 
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 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 Decision Analysis
 
19.1 Revision of Probabilities: Bayes' Rule
 
19.2 The Decision Table and Decision-Making Under Certainty
 
19.3 Decision-Making Under Uncertainty
 
19.4 Decision-Making Under Risk
 
19.5 Revising Probabilities in Light of Sample Information
 
APPENDIX A
 
APPENDIX B
 
GLOSSARY INDEX