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### Test Bank for Basic Business Statistics 4th Australian edition by Mark Berenson

#### Test Bank for Basic Business Statistics 4th Australian edition by Mark Berenson

Student-friendly stats! Berenson’s clear and consistent explanations of how and why accepted statistical techniques are used and fresh, conversational writing style helps students with their comprehension of the concepts. Berenson’s real-world business focus takes students beyond the pure theory by connecting statistical concepts to functional areas of business through engaging examples. Examples of real people working in real business environments, and using statistics to tackle real business challenges bring the subject to life.

Brief contents
Detailed contents
Preface
Acknowledgments
How to use this book
Part 1: Presenting and Describing Information
Chapter 1: Introduction and data collection
1.1: Basic concepts of statistics
1.2: The growth of statistics and information technology
1.3: Collecting data
Identifying Sources of Data
1.4: Types of variables
Levels of Measurement and Types of Measurement Scales
Summary
Key terms
Chapter review problems
Appendix 1: Introduction to using Microsoft Windows and Excel
Chapter 2: Presenting data in tables and charts
2.1: Tables and charts for categorical data
Summary Tables
Bar Charts
Pie Charts
2.2: Organising numerical data
Ordered Arrays
Stem-and-Leaf Displays
2.3: Tables and graphs for numerical data
Frequency Distributions
Relative Frequency Distributions and Percentage Distributions
Cumulative Distributions
Histograms
Polygons
Cumulative Percentage Polygons (Ogives)
2.4: Cross-tabulations
Contingency Tables
Side-by-Side Bar Charts
2.5: Scatter diagrams and time-series plots
Scatter Diagrams
Time-series Plots
2.6: Misusing graphs and ethical issues
Ethical Concerns
Summary
Key terms
Chapter review problems
Appendix 2: Using statistical software for tables and charts
Chapter 3: Numerical descriptive measures
3.1: Measures of central tendency, variation and shape
Mean
Median
Mode
Quartiles
Geometric Mean
Range
Interquartile Range
Variance and Standard Deviation
Coefficient of Variation
Z Scores
Shape
Microsoft Excel Descriptive Statistics Output
3.2: Numerical descriptive measures for a population
Population Mean
Population Variance and Standard Deviation
The Empirical Rule
The Chebyshev Rule
3.3: Calculating numerical descriptive measures from a frequency distribution
3.4: Five-number summary and box-and-whisker plots
Five-Number Summary
Box-and-Whisker Plots
3.5: Covariance and the coefficient of correlation
Covariance
Coefficient of Correlation
3.6: Pitfalls in numerical descriptive measures and ethical issues
Ethical Issues
Summary
Key formulas
Key terms
Chapter review problems
Appendix 3: Using statistical software for descriptive statistics
End of Part 1 problems
Part 2: Measuring Uncertainty
Chapter 4: Basic probability
4.1: Basic probability concepts
Events and Sample Spaces
Contingency Tables and Venn Diagrams
Marginal Probability
Joint Probability
4.2: Conditional probability
Calculating Conditional Probabilities
Decision Trees
Statistical Independence
Multiplication Rules
Marginal Probability Using the General Multiplication Rule
4.3: Bayes’ theorem
4.4: Counting rules
4.5: Ethical issues and probability
Summary
Key formulas
Key terms
Chapter review problems
Appendix 4: Using statistical software for basic probability
Chapter 5: Some important discrete probability distributions
5.1: Probability distribution for a discrete random variable
Expected Value of a Discrete Random Variable
Variance and Standard Deviation of a Discrete Random Variable
5.2: Covariance and its application in finance
Covariance
Expected Value, Variance and Standard Deviation of the Sum of Two Random Variables
Portfolio Expected Return and Portfolio Risk
5.3: Binomial distribution
5.4: Poisson distribution
5.5: Hypergeometric distribution
5.6: (Online Topic) Using the poisson distribution to approximate the binomial distribution
Summary
Key formulas
Key terms
Chapter review problems
Appendix 5: Using statistical software for discrete probability distributions
Chapter 6: The normal distribution and other continuous distributions
6.1: Continuous probability distributions
6.2: The normal distribution
6.3: Evaluating normality
Evaluating the Properties
Constructing a Normal Probability Plot
6.4: The uniform distribution
6.5: The exponential distribution
6.6: The normal approximation to the binomial distribution
Need for a Continuity Correction
Approximating the Binomial Distribution
Calculating a Probability Approximation for an Individual Value
Summary
Key formulas
Key terms
Chapter review problems
Appendix 6: Using statistical software for continuous probability distributions
Chapter 7: Sampling distributions
7.1: Sampling distributions
7.2: Sampling distribution of the mean
The Unbiased Property of the Sample Mean
Standard Error of the Mean
Sampling from Normally Distributed Populations
Sampling from Non-normally Distributed Populations – The Central Limit Theorem
7.3: Sampling distribution of the proportion
7.4: Types of survey sampling methods
Simple Random Sample
Systematic Sample
Stratified Sample
Cluster Sample
7.5: Evaluating survey worthiness
Survey Errors
Ethical Issues
Summary
Key formulas
Key terms
Chapter review problems
Appendix 7: Using statistical software for sampling distributions
End of Part 2 problems
Part 3: Drawing Conclusions about Populations Based only on Sample Information
Chapter 8: Confidence interval estimation
8.1: Confidence interval estimation for the mean (σ known)
8.2: Confidence interval estimation for the mean (σ unknown)
Student’s t Distribution
Properties of the t Distribution
The Concept of Degrees of Freedom
The Confidence Interval Statement
8.3: Confidence interval estimation for the proportion
8.4: Determining sample size
Sample Size Determination for the Mean
Sample Size Determination for the Proportion
8.5: Applications of confidence interval estimation in auditing
Estimating the Population Total Amount
Difference Estimation
One-Sided Confidence Interval Estimation of the Rate of Non-Compliance with Internal Controls
8.6: More on confidence interval estimation and ethical issues
Summary
Key formulas
Key terms
Chapter review problems
Appendix 8: Using statistical software for confidence intervals and sample size determination
Chapter 9: Fundamentals of hypothesis testing: One-sample tests
9.1: Hypothesis-testing methodology
The Null and Alternative Hypotheses
The Critical Value of the Test Statistic
Regions of Rejection and Non-Rejection
Risks in Decision Making Using Hypothesis Testing
9.2: Z test of hypothesis for the mean (σ known)
The Critical Value Approach to Hypothesis Testing
The p-Value Approach to Hypothesis Testing
A Connection between Confidence Interval Estimation and Hypothesis Testing
9.3: One-tail tests
The Critical Value Approach
The p-Value Approach
9.4: t test of hypothesis for the mean (σ unknown)
The Critical Value Approach
The p-Value Approach
Checking Assumptions
9.5: Z test of hypothesis for the proportion
The Critical Value Approach
The p-Value Approach
9.6: The power of a test
9.7: Potential hypothesis-testing pitfalls and ethical issues
Data-Collection Method – Randomisation
Informed Consent from the Human Respondents
Type of Test: Two-Tail or One-Tail
Choice of Level of Significance, 𝛂
Data Snooping
Reporting of Findings
Statistical Significance Versus Practical Significance
Summary
Key formulas
Key terms
Chapter review problems
Appendix 9: Using statistical software for one-sample tests of hypothesis
Chapter 10: Hypothesis testing: Two-sample tests
10.1: Comparing the means of two independent populations
Z Test for the Difference between Two Means
Pooled-Variance t Test for the Difference between Two Means
Confidence Interval Estimate for the Difference between the Means of Two Independent Populations
Separate-Variance t Test for the Difference between Two Means
10.2: Comparing the means of two related populations
Paired t Test
Confidence Interval Estimate for the Mean Difference
10.3: F test for the difference between two variances
Finding Lower-Tail Critical Values
10.4: Comparing two population proportions
Z Test for the Difference between Two Proportions
Confidence Interval Estimate for the Difference between Two Proportions
Summary
Key formulas
Key terms
Chapter review problems
Appendix 10: Using statistical software for two-sample tests
Chapter 11: Analysis of variance
11.1: The completely randomised design: One-way analysis of variance
F Test for Differences between More than Two Means
Multiple Comparisons: The Tukey–Kramer Procedure
ANOVA Assumptions
The Levene Test for Homogeneity of Variance
11.2: The randomised block design
Tests for the Treatment and Block Effects
Multiple Comparisons: The Tukey Procedure
11.3: The factorial design: Two-way analysis of variance
Testing for Factor and Interaction Effects
Interpreting Interaction Effects
Multiple Comparisons: The Tukey Procedure
Summary
Key formulas
Key terms
Chapter review problems
Appendix 11: Using statistical software for ANOVA
End of Part 3 problems
Part 4: Determining Cause and Making Reliable Forecasts
Chapter 12: Simple linear regression
12.1: Types of regression models
12.2: Determining the simple linear regression equation
The Least-Squares Method
Predictions in Regression Analysis: Interpolation versus Extrapolation
Calculating the Y Intercept, b0, and the Slope, b1
12.3: Measures of variation
Calculating the Sum of Squares
The Coefficient of Determination
Standard Error of the Estimate
12.4: Assumptions
12.5: Residual analysis
Evaluating the Assumptions
12.6: Measuring autocorrelation: The Durbin–Watson statistic
Residual Plots to Detect Autocorrelation
The Durbin–Watson Statistic
12.7: Inferences about the slope and correlation coefficient
t Test for the Slope
F Test for the Slope
Confidence Interval Estimate of the Slope (𝛃1)
t Test for the Correlation Coefficient
12.8: Estimation of mean values and prediction of individual values
The Confidence Interval Estimate
The Prediction Interval
12.9: Pitfalls in regression and ethical issues
Summary
Key formulas
Key terms
Chapter review problems
Appendix 12: Using statistical software for simple linear regression
Chapter 13: Introduction to multiple regression
13.1: Developing the multiple regression model
Interpreting the Regression Coefficients
Predicting the Dependent Variable, Y
13.2: R2, adjusted R2 and the overall F test
Coefficients of Multiple Determination
Test for the Significance of the Overall Multiple Regression Model
13.3: Residual analysis for the multiple regression model
13.4: Inferences concerning the population regression coefficients
Tests of Hypothesis
Confidence Interval Estimation
13.5: Testing portions of the multiple regression model
Coefficients of Partial Determination
13.6: Using dummy variables and interaction terms in regression models
Interactions
13.7: Collinearity
Summary
Key formulas
Key terms
Chapter review problems
Appendix 13: Using statistical software for multiple regression
Chapter 14: Time-series forecasting and index numbers
14.1: The importance of business forecasting
14.2: Component factors of the classical multiplicative time-series model
14.3: Smoothing the annual time series
Moving Averages
Exponential Smoothing
14.4: Least-squares trend-fitting and forecasting
Linear Trend Model
Exponential Trend Model
Model Selection Using First, Second and Percentage Differences
14.5: The Holt–Winters method for trend-fitting and forecasting
14.6: Autoregressive modelling for trend-fitting and forecasting
14.7: Choosing an appropriate forecasting model
Performing a Residual Analysis
Measuring the Magnitude of the Residual Error with Squared or Absolute Differences
Principle of Parsimony
Comparison of Five Forecasting Methods
14.8: Time-series forecasting of seasonal data
Least-Squares Forecasting with Monthly or Quarterly Data
14.9: Index numbers
The Price Index
Aggregate Price Indices
Weighted Aggregate Price Indices
Some Common Price Indices
14.10: Pitfalls in time-series forecasting
Summary
Key formulas
Key terms
Chapter review problems
Appendix 14: Using statistical software for time-series forecasting and index numbers
Chapter 15: Chi-square tests
15.1: Chi-square test for the difference between two proportions (independent samples)
15.2: Chi-square test for differences between more than two proportions
The Marascuilo Procedure
15.3: Chi-square test of independence
15.4: Chi-square goodness-of-fit tests
Chi-Square Goodness-of-Fit Test for a Poisson Distribution
Chi-Square Goodness-of-Fit Test for a Normal Distribution
15.5: Chi-square test for a variance or standard deviation
Summary
Key formulas
Key terms
Chapter review problems
Appendix 15: Using statistical software for chi-square tests
End of Part 4 problems
Part 5: Further Topics in Stats
Chapter 16: Multiple regression model building
Finding the Regression Coefficients and Predicting Y
Testing for the Significance of the Quadratic Model
The Coefficient of Multiple Determination
16.2: Using transformations in regression models
The Square-Root Transformation
The Log Transformation
16.3: Influence analysis
The Hat Matrix Elements hi
The Studentised Deleted Residuals ti
Cook’s Distance Statistic Di
Overview
16.4: Model building
The Stepwise Regression Approach to Model Building
The Best-Subsets Approach to Model Building
Model Validation
16.5: Pitfalls in multiple regression and ethical issues
Pitfalls in Multiple Regression
Ethical Considerations
Summary
Key formulas
Key terms
Chapter review problems
Appendix 16: Software for multiple regression model building
Chapter 17: Decision making
17.1: Payoff tables and decision trees
17.2: Criteria for decision making
Expected Monetary Value
Expected Opportunity Loss
Return-to-Risk Ratio
17.3: Decision making with sample information
17.4: Utility
Summary
Key formulas
Key terms
Chapter review problems
Appendix 17: Using statistical software for decision making
Chapter 18: Statistical applications in quality and productivity management
18.1: Total quality management
18.2: Six sigma management
The DMAIC Model
18.3: The theory of control charts
18.4: Control chart for the proportion – the p chart
18.5: The red bead experiment: Understanding process variability
18.6: Control chart for an area of opportunity – the c chart
18.7: Control charts for the range and the mean
The R Chart
The X Chart
18.8: Process capability
Customer Satisfaction and Specification Limits
Capability Indices
CPL, CPU and Cpk
Summary
Key formulas
Key terms
Chapter review problems
Appendix 18: Using statistical software for statistical applications in quality management
Chapter 19: Further non-parametric tests
19.1: McNemar test for the difference between two proportions (related samples)
19.2: Wilcoxon rank sum test: Non-parametric analysis for two independent populations
19.3: Wilcoxon signed ranks test: Non-parametric analysis for two related populations
19.4: Kruskal–Wallis rank test: Non-parametric analysis for the one-way ANOVA
19.5: Friedman rank test: Non-parametric analysis for the randomised block design
Summary
Key formulas
Key terms
Chapter review problems
Appendix 19: Using statistical software for non-parametric tests
End of Part 5 problems
Appendices
A: Review of arithmetic, algebra and logarithms
A.1: Rules for Arithmetic Operations
A.2: Rules for Algebra: Exponents and Square Roots
A.3: Rules for Logarithms
B: Summation notation
C: Statistical symbols and Greek alphabet
C.1: Statistical Symbols
C.2: Greek Alphabet
D: Phstat2 user’s guide
D.1: Sampling Distributions Simulation
D.2: Confidence Interval Estimate for the Mean, Sigma known
D.3: Confidence Interval Estimate for the Mean, Sigma unknown
D.4: Confidence Interval Estimate for the Proportion
D.5: Sample Size Determination for the Mean
D.6: Sample Size Determination for the Proportion
D.7: Confidence Interval Estimate for the Population Total
D.8: Confidence Interval Estimate for the Total Difference
D.9: Z Test for the Mean, Sigma Known
D.10: t Test for the Mean, Sigma Unknown
D.11: Z Test for the Proportion
D.12: Z Test for Differences in two Means
D.13: Pooled-Variance t Test for Differences in two Means (Summarised and Unsummarised Data)
D.14: t Test for Difference in two means (Unsummarised Data)
D.15: F Test for Differences in two Variances
D.16: Z Test for the Differences in two Proportions
D.17: Tukey–Kramer Procedure
D.18: Levene’s Test
D.19: Simple Linear Regression
D.20: Multiple Regression
D.21: Chi-Square Test for Differences in two Proportions
D.22: Chi-Square Test
D.23: Opportunity Loss
D.24: Expected Opportunity Loss
D.25: Expected Monetary Value
E: Tables
E.1: Table of random numbers
E.2: The cumulative standardised normal distribution
E.3: Critical values of t
E.4: Critical values of χ2
E.5: Critical values of F
E.6: Table of binomial probabilities
E.7: Table of Poisson probabilities
E.8: Lower and upper critical values T1 of Wilcoxon rank sum test
E.9: Lower and upper critical values W of Wilcoxon signed ranks test
E.10: Critical values of the Studentised range Q
E.11: Critical values dL and dU of the Durbin–Watson statistic D
E.12: Selected critical values of F for Cook’s Di statistic
E.13: Control chart factors
E.14: The standardised normal distribution
F: Using Microsoft Excel with this text
F.1: Configuring Microsoft Excel
F.2: Using the Data Analysis Tools
F.3: Enhancing the Appearance of Worksheets
F.4: Mac Users and Statistical Programs
Glossary
Index
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