Solution Manual for Introduction to Business Statistics, 7th Edition
Product details:
- ISBN-10 : 053845217X
- ISBN-13 : 978-0538452175
- Author: Dr. Ron Weiers
Highly praised for its exceptional clarity, technical accuracy, and useful examples, Weiers’ INTRODUCTION TO BUSINESS STATISTICS, Seventh Edition, introduces fundamental statistical concepts with an engaging, conversational presentation and a strong emphasis on the practical relevance of course material to students’ lives and careers. The text’s outstanding illustrations, friendly language, non-technical terminology, and current examples involving real-world business and personal settings will capture students’ interest and prepare them for success from day one. Continuing cases, contemporary business applications, and more than 300 new or revised exercises and problems reflect important trends and the latest developments in today’s dynamic business environment — all with an accuracy you and your students can trust.
Table contents:
- CHAPTER 1: A Preview of Business Statistics
- 1.1: INTRODUCTION
- 1.2: STATISTICS: YESTERDAY AND TODAY
- 1.3: DESCRIPTIVE VERSUS INFERENTIAL STATISTICS
- 1.4: TYPES OF VARIABLES AND SCALES OF MEASUREMENT
- 1.5: STATISTICS IN BUSINESS DECISIONS
- 1.6: BUSINESS STATISTICS: TOOLS VERSUS TRICKS
- 1.7: SUMMARY
- STATISTICS IN ACTION
- CHAPTER EXERCISES
- CHAPTER 2: Visual Description of Data
- 2.1: INTRODUCTION
- 2.2: THE FREQUENCY DISTRIBUTION AND THE HISTOGRAM
- 2.3: THE STEM-AND-LEAF DISPLAY AND THE DOTPLOT
- 2.4: OTHER METHODS FOR VISUAL REPRESENTATION OF THE DATA
- 2.5: THE SCATTER DIAGRAM
- 2.6: TABULATION, CONTINGENCY TABLES, AND THE EXCEL PivotTable
- 2.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- CHAPTER 3: Statistical Description of Data
- 3.1: INTRODUCTION
- 3.2: STATISTICAL DESCRIPTION: MEASURES OF CENTRAL TENDENCY
- 3.3: STATISTICAL DESCRIPTION: MEASURES OF DISPERSION
- 3.4: ADDITIONAL DISPERSION TOPICS
- 3.5: DESCRIPTIVE STATISTICS FROM GROUPED DATA
- 3.6: STATISTICAL MEASURES OF ASSOCIATION
- 3.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- SEEING STATISTICS: APPLET 1
- SEEING STATISTICS: APPLET 2
- CHAPTER 4: Data Collection and Sampling Methods
- 4.1: INTRODUCTION
- 4.2: RESEARCH BASICS
- 4.3: SURVEY RESEARCH
- 4.4: EXPERIMENTATION AND OBSERVATIONAL RESEARCH
- 4.5: SECONDARY DATA
- 4.6: THE BASICS OF SAMPLING
- 4.7: SAMPLING METHODS
- 4.8: SUMMARY
- CHAPTER EXERCISES
- INTEGRATED CASE
- SEEING STATISTICS: APPLET 3
- CHAPTER 5: Probability: Review of Basic Concepts
- 5.1: INTRODUCTION
- 5.2: PROBABILITY: TERMS AND APPROACHES
- 5.3: UNIONS AND INTERSECTIONS OF EVENTS
- 5.4: ADDITION RULES FOR PROBABILITY
- 5.5: MULTIPLICATION RULES FOR PROBABILITY
- 5.6: BAYES’ THEOREM AND THE REVISION OF PROBABILITIES
- 5.7: COUNTING: PERMUTATIONS AND COMBINATIONS
- 5.8: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- CHAPTER 6: Discrete Probability Distributions
- 6.1: INTRODUCTION
- 6.2: THE BINOMIAL DISTRIBUTION
- 6.3: THE HYPERGEOMETRIC DISTRIBUTION
- 6.4: THE POISSON DISTRIBUTION
- 6.5: SIMULATING OBSERVATIONS FROM A DISCRETE PROBABILITY DISTRIBUTION
- 6.6: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASE
- CHAPTER 7: Continuous Probability Distributions
- 7.1: INTRODUCTION
- 7.2: THE NORMAL DISTRIBUTION
- 7.3: THE STANDARD NORMAL DISTRIBUTION
- 7.4: THE NORMAL APPROXIMATION TO THE BINOMIAL DISTRIBUTION
- 7.5: THE EXPONENTIAL DISTRIBUTION
- 7.6: SIMULATING OBSERVATIONS FROM A CONTINUOUS PROBABILITY DISTRIBUTION
- 7.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- SEEING STATISTICS: APPLET 4
- SEEING STATISTICS: APPLET 5
- SEEING STATISTICS: APPLET 6
- CHAPTER 8: Sampling Distributions
- 8.1: INTRODUCTION
- 8.2: A PREVIEW OF SAMPLING DISTRIBUTIONS
- 8.3: THE SAMPLING DISTRIBUTION OF THE MEAN
- 8.4: THE SAMPLING DISTRIBUTION OF THE PROPORTION
- 8.5: SAMPLING DISTRIBUTIONS WHEN THE POPULATION IS FINITE
- 8.6: COMPUTER SIMULATION OF SAMPLING DISTRIBUTIONS
- 8.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASE
- SEEING STATISTICS: APPLET 7
- SEEING STATISTICS: APPLET 8
- CHAPTER 9: Estimation from Sample Data
- 9.1: INTRODUCTION
- 9.2: POINT ESTIMATES
- 9.3: A PREVIEW OF INTERVAL ESTIMATES
- 9.4: CONFIDENCE INTERVAL ESTIMATES FOR THE MEAN: σ KNOWN
- 9.5: CONFIDENCE INTERVAL ESTIMATES FOR THE MEAN: σ UNKNOWN
- 9.6: CONFIDENCE INTERVAL ESTIMATES FOR THE POPULATION PROPORTION
- 9.7: SAMPLE SIZE DETERMINATION
- 9.8: WHEN THE POPULATION IS FINITE
- 9.9: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- SEEING STATISTICS: APPLET 9
- SEEING STATISTICS: APPLET 10
- SEEING STATISTICS: APPLET 11
- CHAPTER 10: Hypothesis Tests Involving a Sample Mean or Proportion
- 10.1: INTRODUCTION
- 10.2: HYPOTHESIS TESTING: BASIC PROCEDURES
- 10.3: TESTING A MEAN, POPULATION STANDARD DEVIATION KNOWN
- 10.4: CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
- 10.5: TESTING A MEAN, POPULATION STANDARD DEVIATION UNKNOWN
- 10.6: TESTING A PROPORTION
- 10.7: THE POWER OF A HYPOTHESIS TEST
- 10.8: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- SEEING STATISTICS: APPLET 12
- SEEING STATISTICS: APPLET 13
- CHAPTER 11: Hypothesis Tests Involving Two Sample Means or Proportions
- 11.1: INTRODUCTION
- 11.2: THE POOLED-VARIANCES t-TEST FOR COMPARING THE MEANS OF TWO INDEPENDENT SAMPLES
- 11.3: THE UNEQUAL-VARIANCES t-TEST FOR COMPARING THE MEANS OF TWO INDEPENDENT SAMPLES
- 11.4: THE z-TEST FOR COMPARING THE MEANS OF TWO INDEPENDENT SAMPLES
- 11.5: COMPARING TWO MEANS WHEN THE SAMPLES ARE DEPENDENT
- 11.6: COMPARING TWO SAMPLE PROPORTIONS
- 11.7: COMPARING THE VARIANCES OF TWO INDEPENDENT SAMPLES
- 11.8: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- SEEING STATISTICS: APPLET 14
- CHAPTER 12: Analysis of Variance Tests
- 12.1: INTRODUCTION
- 12.2: ANALYSIS OF VARIANCE: BASIC CONCEPTS
- 12.3: ONE-WAY ANALYSIS OF VARIANCE
- 12.4: THE RANDOMIZED BLOCK DESIGN
- 12.5: TWO-WAY ANALYSIS OF VARIANCE
- 12.6: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- SEEING STATISTICS: APPLET 15
- SEEING STATISTICS: APPLET 16
- CHAPTER 13: Chi-Square Applications
- 13.1: INTRODUCTION
- 13.2: BASIC CONCEPTS IN CHI-SQUARE TESTING
- 13.3: TESTS FOR GOODNESS OF FIT AND NORMALITY
- 13.4: TESTING THE INDEPENDENCE OF TWO VARIABLES
- 13.5: COMPARING PROPORTIONS FROM k INDEPENDENT SAMPLES
- 13.6: ESTIMATION AND TESTS REGARDING THE POPULATION VARIANCE
- 13.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- SEEING STATISTICS: APPLET 17
- CHAPTER 14: Nonparametric Methods
- 14.1: INTRODUCTION
- 14.2: WILCOXON SIGNED RANK TEST FOR ONE SAMPLE
- 14.3: WILCOXON SIGNED RANK TEST FOR COMPARING PAIRED SAMPLES
- 14.4: WILCOXON RANK SUM TEST FOR COMPARING TWO INDEPENDENT SAMPLES
- 14.5: KRUSKAL-WALLIS TEST FOR COMPARING MORE THAN TWO INDEPENDENT SAMPLES
- 14.6: FRIEDMAN TEST FOR THE RANDOMIZED BLOCK DESIGN
- 14.7: OTHER NONPARAMETRIC METHODS
- 14.8: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASE
- BUSINESS CASE
- CHAPTER 15: Simple Linear Regression and Correlation
- 15.1: INTRODUCTION
- 15.2: THE SIMPLE LINEAR REGRESSION MODEL
- 15.3: INTERVAL ESTIMATION USING THE SAMPLE REGRESSION LINE
- 15.4: CORRELATION ANALYSIS
- 15.5: ESTIMATION AND TESTS REGARDING THE SAMPLE REGRESSION LINE
- 15.6: ADDITIONAL TOPICS IN REGRESSION AND CORRELATION ANALYSIS
- 15.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- SEEING STATISTICS: APPLET 18
- SEEING STATISTICS: APPLET 19
- SEEING STATISTICS: APPLET 20
- CHAPTER 16: Multiple Regression and Correlation
- 16.1: INTRODUCTION
- 16.2: THE MULTIPLE REGRESSION MODEL
- 16.3: INTERVAL ESTIMATION IN MULTIPLE REGRESSION
- 16.4: MULTIPLE CORRELATION ANALYSIS
- 16.5: SIGNIFICANCE TESTS IN MULTIPLE REGRESSION AND CORRELATION
- 16.6: OVERVIEW OF THE COMPUTER ANALYSIS AND INTERPRETATION
- 16.7: ADDITIONAL TOPICS IN MULTIPLE REGRESSION AND CORRELATION
- 16.8: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- CHAPTER 17: Model Building
- 17.1: INTRODUCTION
- 17.2: POLYNOMIAL MODELS WITH ONE QUANTITATIVE PREDICTOR VARIABLE
- 17.3: POLYNOMIAL MODELS WITH TWO QUANTITATIVE PREDICTOR VARIABLES
- 17.4: QUALITATIVE VARIABLES
- 17.5: DATA TRANSFORMATIONS
- 17.6: MULTICOLLINEARITY
- 17.7: STEPWISE REGRESSION
- 17.8: SELECTING A MODEL
- 17.9: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- BUSINESS CASE
- CHAPTER 18: Models for Time Series and Forecasting
- 18.1: INTRODUCTION
- 18.2: TIME SERIES
- 18.3: SMOOTHING TECHNIQUES
- 18.4: SEASONAL INDEXES
- 18.5: FORECASTING
- 18.6: EVALUATING ALTERNATIVE MODELS: MAD AND MSE
- 18.7: AUTOCORRELATION, THE DURBIN-WATSON TEST, AND AUTOREGRESSIVE FORECASTING
- 18.8: INDEX NUMBERS
- 18.9: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASE
- CHAPTER 19: Decision Theory
- 19.1: INTRODUCTION
- 19.2: STRUCTURING THE DECISION SITUATION
- 19.3: NON-BAYESIAN DECISION MAKING
- 19.4: BAYESIAN DECISION MAKING
- 19.5: THE OPPORTUNITY LOSS APPROACH
- 19.6: INCREMENTAL ANALYSIS AND INVENTORY DECISIONS
- 19.7: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASE
- CHAPTER 20: Total Quality Management
- 20.1: INTRODUCTION
- 20.2: A HISTORICAL PERSPECTIVE AND DEFECT DETECTION
- 20.3: THE EMERGENCE OF TOTAL QUALITY MANAGEMENT
- 20.4: PRACTICING TOTAL QUALITY MANAGEMENT
- 20.5: SOME STATISTICAL TOOLS FOR TOTAL QUALITY MANAGEMENT
- 20.6: STATISTICAL PROCESS CONTROL: THE CONCEPTS
- 20.7: CONTROL CHARTS FOR VARIABLES
- 20.8: CONTROL CHARTS FOR ATTRIBUTES
- 20.9: ADDITIONAL STATISTICAL PROCESS CONTROL AND QUALITY MANAGEMENT TOPICS
- 20.10: SUMMARY
- EQUATIONS
- CHAPTER EXERCISES
- INTEGRATED CASES
- SEEING STATISTICS: APPLET 21
- APPENDIX A: STATISTICAL TABLES
- APPENDIX B: SELECTED ANSWERS
- INDEX/GLOSSARY
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