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Business Statistics: For Contemporary Decision Making, 8th Edition 8th Edition, Kindle Edition by Ken Black (Author) EBOOK PDF Instant Download

PREFACE

CHANGES FOR THE SEVENTH EDITION
VIDEOTAPE TUTORIALS BY KEN BLACK
FEATURES AND BENEFITS
WILEYPLUS
ANCILLARY TEACHING AND LEARNING MATERIALS www.wiley.com/college/black
ACKNOWLEDGMENTS

UNIT I: INTRODUCTION

CHAPTER 1: Introduction to Statistics
1.2 BASIC STATISTICAL CONCEPTS
1.3 VARIABLES AND DATA
1.4 DATA MEASUREMENT
DIGIORNO PIZZA: INTRODUCING A FROZEN PIZZA TO COMPETE WITH CARRY-OUT
CHAPTER 2: 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
SOAP COMPANIES DO BATTLE
CHAPTER 3: Descriptive Statistics
3.1 MEASURES OF CENTRAL TENDENCY: UNGROUPED DATA
3.2 MEASURES OF VARIABILITY: UNGROUPED DATA
3.3 MEASURES OF CENTRAL TENDENCY AND VARIABILITY: GROUPED DATA
3.4 MEASURES OF SHAPE
3.5 DESCRIPTIVE STATISTICS ON THE COMPUTER
COCA-COLA DEVELOPS THE AFRICAN MARKET
CHAPTER 4: Probability
4.1 INTRODUCTION TO PROBABILITY
4.2 METHODS OF ASSIGNING PROBABILITIES
4.3 STRUCTURE OF PROBABILITY
4.4 MARGINAL, UNION, JOINT, AND CONDITIONAL PROBABILITIES
4.6 MULTIPLICATION LAWS
4.7 CONDITIONAL PROBABILITY
4.8 REVISION OF PROBABILITIES: BAYES’ RULE
COLGATE-PALMOLIVE MAKES A “TOTAL” EFFORT

UNIT II: DISTRIBUTIONS AND SAMPLING

CHAPTER 5: Discrete Distributions
5.1 DISCRETE VERSUS CONTINUOUS DISTRIBUTIONS
5.2 DESCRIBING A DISCRETE DISTRIBUTION
5.3 BINOMIAL DISTRIBUTION
5.4 POISSION DISTRIBUTION
5.5 HYPERGEOMETRIC DISTRIBUTION
WHOLE FOODS MARKET GROWS THROUGH MERGERS AND ACQUISITIONS
CHAPTER 6: Continuous Distributions
6.1 THE UNIFORM DISTRIBUTION
6.2 NORMAL DISTRIBUTION
6.3 USING THE NORMAL CURVE TO APPROXIMATE BINOMIAL DISTRIBUTION PROBLEMS
6.4 EXPONENTIAL DISTRIBUTION
CHAPTER 7: Sampling and Sampling Distributions
7.1 SAMPLING
7.2 SAMPLING DISTRIBUTION OF
7.3 SAMPLING DISTRIBUTION OF

UNIT III: MAKING INFERENCES ABOUT POPULATION PARAMETERS

CHAPTER 8: Statistical Inference: Estimation for Single Populations
8.1 ESTIMATING THE POPULATION MEAN USING THE z STATISTIC ( s KNOWN)
8.2 ESTIMATING THE POPULATION MEAN USING THE t STATISTIC ( s UNKNOWN)
8.3 ESTIMATING THE POPULATION PROPORTION
8.4 ESTIMATING THE POPULATION VARIANCE
8.5 ESTIMATING SAMPLE SIZE
THE CONTAINER STORE
CHAPTER 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 ( s KNOWN)
9.3 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE t STATISTIC (s UNKNOWN)
9.4 TESTING HYPOTHESES ABOUT A PROPORTION
9.5 TESTING HYPOTHESES ABOUT A VARIANCE
9.6 SOLVING FOR TYPE II ERRORS
FRITO-LAY TARGETS THE HISPANIC MARKET
CHAPTER 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, p 1 – p 2
10.5 TESTING HYPOTHESES ABOUT TWO POPULATION VARIANCES
SEITZ CORPORATION: PRODUCING QUALITY GEAR-DRIVEN AND LINEAR-MOTION PRODUCTS
CHAPTER 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)
THE CLARKSON COMPANY: A DIVISION OF TYCO INTERNATIONAL

UNIT IV: REGRESSION ANALYSIS AND FORECASTING

CHAPTER 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
DELTA WIRE USES TRAINING AS A WEAPON
CHAPTER 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 R 2
13.4 INTERPRETING MULTIPLE REGRESSION COMPUTER OUTPUT
STARBUCKS INTRODUCES DEBIT CARD
CHAPTER 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
VIRGINIA SEMICONDUCTOR
CHAPTER 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
DEBOURGH MANUFACTURING COMPANY

UNIT V: NONPARAMETRIC STATISTICS AND QUALITY

CHAPTER 16: Analysis of Categorical Data
16.1 CHI-SQUARE GOODNESS-OF-FIT TEST
16.2 CONTINGENCY ANALYSIS: CHI-SQUARE TEST OF INDEPENDENCE
FOOT LOCKER IN THE SHOE MIX
CHAPTER 17: Nonparametric Statistic
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
SCHWINN
CHAPTER 18: Statistical Quality Control
18.1 INTRODUCTION TO QUALITY CONTROL
18.2 PROCESS ANALYSIS
18.3 CONTROL CHARTS
ROBOTRON-ELOTHERM
APPENDIX A: Tables
APPENDIX B: Answers to Selected Odd-Numbered Quantitative Problems

GLOSSARY
INDEX