Name: Understanding Business Statistics 1st Edition Freed Test Bank
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ISBN-10 ‏ : ‎ 1118145259
ISBN-13 ‏ : ‎ 978-1118145258
Author: Ned Freed, Stacey Jones, Timothy Bergquist

Written in a conversational tone, Freed, Understanding Business Statistics presents topics in a systematic and organized manner to help students navigate the material. Demonstration problems appear alongside the concepts, making the content easier to understand. By explaining the reasoning behind each exercise, students are more inclined to engage with the material and gain a clear understanding of how to apply statistics to the business world. Freed, Understanding Business Statistics is accompanied by WileyPLUS, a research-based, online environment for effective teaching and learning. This online learning system gives students instant feedback on homework assignments, provides video tutorials and variety of study tools, and offers instructors thousands of reliable, accurate problems (including every problem from the book) to deliver automatically graded assignments or tests. Available in or outside of the Blackboard Learn Environment, WileyPLUS resources help reach all types of learners and give instructors the tools they need to enhance course material.

Table of Content:

1 An Introduction to Statistics
1.1 Statistics Defined
1.2 Branches of Statistics
Descriptive Statistics
Statistical Inference
Probability Theory: The Link
1.3 Two Views of the World
A World of Certainty
A World of Uncertainty
1.4 The Nature of Data
Qualitative and Quantitative Data
Time Series and Cross-Sectional Data
Levels of Measurement
1.5 Data Sources
The Internet
Government Agencies and Private-Sector Companies
Original Studies
1.6 Ethics in Statistics
1.7 The Text
Goals
Key Elements
1.8 A Final Comment
Glossary
Chapter Exercises
2 Descriptive Statistics I: Elementary Data Presentation and Description
2.1 Measures of Central Location or Central Tendency
Mean
Median
Mode
2.2 Measures of Dispersion
Range
Mean Absolute Deviation
Variance
Standard Deviation
2.3 Frequency Distributions
Frequency Distribution Shapes
Computing Descriptive Measures for Frequency Distributions
The Effect of Distribution Shape on Descriptive Measures
2.4 Relative Frequency Distributions
Relative Frequency Bar Charts
Computing Descriptive Measures for Relative Frequency Distributions
2.5 Cumulative Distributions
Cumulative Frequency Distributions
Cumulative Relative Frequency Distribution
2.6 Grouped Data
Histograms
Approximating Descriptive Measures for Grouped Data
A Final Note on Grouped Data
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
3 Descriptive Statistics II: Additional Descriptive Measures and Data Displays
3.1 Percentiles and Quartiles
Percentiles
Quartiles
Measuring Dispersion with the Interquartile Range
3.2 Exploratory Data Analysis
Stem-and-Leaf Diagrams
An Ordered Stem-and-Leaf Diagram
Box Plots
3.3 Identifying Outliers
1.5 X Interquartile Range
Chebyshev’s Rule
The Empirical Rule
3.4 Measures of Association
Covariance
Correlation Coefficient
Covariance and Correlation Coefficients for Samples
A Final Note
3.5 Additional Descriptive Measures
Coefficient of Variation
The Geometric Mean
Weighted Average (Weighted Mean)
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
4 Probability
4.1 Basic Concepts
Defining Probability
Assigning Basic Probabilities
Classical Approach
Relative Frequency Approach
Subjective Approach
4.2 The Rules of Probability
Simple Probabilities
Conditional Probabilities
Statistical Independence
Joint Probabilities-the Multiplication Rule
Mutually Exclusive Events
Either/Or Probabilities-the Addition Rule
The “Conditional Equals Joint Over Simple” Rule
Complementary Events
4.3 Venn Diagrams
Showing the Addition Rule
Showing Conditional Probability
Showing Complementary Events
Showing Mutually Exclusive Events
4.4 A General Problem Solving Strategy
Probability Trees
Using a Probability Tree to Revise Probabilities
Joint Probabilities and Cross-Tabulation Tables
Choosing the Right Visual Aid
4.5 Counting Outcomes
Multiplication Method
Combinations
Permutations
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
5 Discrete Probability Distributions
5.1 Probability Experiments and Random Variables
5.2 Building a Discrete Probability Distribution
5.3 Displaying and Summarizing the Distribution
Graphing the Distribution
The Distribution Mean (Expected Value)
The Variance and Standard Deviation of the Distribution
5.4 The Binomial Probability Distribution
The Binomial Conditions
The Binomial Probability Function
Logic of the Binomial Function
Descriptive Measures for the Binomial Distribution
The Binomial Table
Shape(s) of the Binomial Distribution
5.5 The Poisson Distribution
Conditions of a Poisson Experiment
The Poisson Probability Function
The Poisson Table
Graphing the Poisson Distribution
Descriptive Measures
Using the Poisson Distribution to Approximate Binomial Probabilities
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
6 Continuous Probability Distributions
6.1 Continuous vs. Discrete Distributions
6.2 The Uniform Probability Distribution
Assigning Probabilities in a Uniform Distribution
Total Area under the Curve
General Distribution Characteristics
6.3 The Normal Distribution
The Normal Probability Density Function
Key Normal Distribution Properties
The Standard Normal Table
Calculating z-scores for Any Normal Distribution
Normal Table Applications
Using the Normal Table in Reverse
6.4 The Exponential Distribution
The Exponential Probability Density Function
Descriptive Measures for the Exponential Distribution
The Memoryless Nature of the Exponential Distribution
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
7 Statistical Inference: Estimating A Population Mean
7.1 The Nature of Sampling
Defining Statistical Inference
Why Sample?
7.2 Developing a Sampling Plan
Choosing a Sample Size
Random Sampling
Alternative Selection Procedures
Using a Random Number Generator or a Random Number Table
Sampling with or without Replacement
A Note on the Use of Random Numbers
7.3 Confidence Intervals and the Role of the Sampling Distribution
The Basic Nature of a Confidence Interval
Sampling Distributions
The Sampling Distribution of the Sample Mean
Properties of the Sampling Distribution of the Sample Mean
Using Sampling Distribution Properties to Build a Confidence Interval
Visualizing the Role of the Sampling Distribution in Interval Estimation
Standard Error versus Margin of Error
7.4 Building Intervals when the Population Standard Deviation Is Unknown
Estimating the Population Standard Deviation with the Sample Standard Deviation, s
Using the t Distribution when s Estimates σ
Constructing Intervals with the t Distribution
Application to the Social Media Example
The Normal Distribution as an Approximation to the t Distribution
7.5 Determining Sample Size
Factors Influencing Sample Size
The Basic Procedure
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
8 Interval Estimates for Proportions, Mean Differences, and Proportion Differences
8.1 Estimating a Population Proportion
An Example
The Sampling Distribution of the Sample Proportion
Predictable Sampling Distribution Properties
Using Sampling Distribution Properties to Build a Confidence Interval
Determining Sample Size
Determining Sample Size with No Information about π
8.2 Estimating the Difference between Two Population Means (Independent Samples)
An Example
The Sampling Distribution of the Sample Mean Difference
Predictable Sampling Distribution Properties
Building the Interval
Small Sample Adjustments
8.3 Estimating the Difference between Two Population Proportions
An Example
The Sampling Distribution of the Sample Proportion Difference
Building the Interval
8.4 Matched Samples
An Example
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
9 Statistical Hypothesis Testing: Hypothesis Tests For A Population Mean
9.1 The Nature of Hypothesis Testing
Comparing Hypothesis Testing to Interval Estimation
Illustrating the Logic of Hypothesis Testing
9.2 Establishing the Hypotheses
Choosing the Null Hypothesis
Standard Forms for the Null and Alternative Hypotheses
9.3 Developing a One-Tailed Test
A Preliminary Step: Evaluating Potential Sample Results
The Key: The Sampling Distribution of the Sample Mean
Choosing a Significance Level
Establishing a Decision Rule
Applying the Decision Rule
Accepting vs. Failing to Reject the Null Hypothesis
Summarizing Our Approach
Another Way to State the Decision Rule
p-values
Generalizing the Test Procedure
9.4 The Possibility of Error
The Risk of Type I Error
The Risk of Type II Error
Choosing a Significance Level
9.5 Two-Tailed Tests
Designing a Two-Tailed Test
Two-Tailed Tests and Interval Estimation
Deciding Whether a Two-Tailed Test Is Appropriate
9.6 Using the t Distribution
An Illustration
p-value Approach
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
10 Hypothesis Tests for Proportions, Mean Differences, and Proportion Differences
10.1 Tests for a Population Proportion
Forming the Hypotheses
The Sampling Distribution of the Sample Proportion
The Null Sampling Distribution
Choosing a Significance Level
Establishing the Critical Value
Putting the Sample Result through the Test
Reporting the Critical p
p-value Approach
The Possibility of Error
The Probability of Error
10.2 Tests for the Difference Between Two Population Means (Independent Samples)
Forming the Hypotheses
The Sampling Distribution of the Sample Mean Difference
The Null Sampling Distribution
Separating Likely from Unlikely Sample Results
Putting the Sample Result though the Test
p-value Approach
When Population σs Are Unknown
A Final Note
10.3 Tests for the Difference Between Two Population Proportions
Forming the Hypotheses
The Sampling Distribution
The Null Sampling Distribution
Establishing the Critical Value
Computing the Value of the Test Statistic
Computing the Standard Error of the Null Sampling Distribution
Completing the Test
p-value Approach
Minimum Sample Sizes
10.4 Matched Samples
An Example
Key Formulas
Chapter Exercises
Excel Exercises (Excel 2013)
11 Basic Regression Analysis
11.1 An Introduction to Regression
The Nature of Regression Analysis
Regression Analysis Variations
Simple vs. Multiple Regression
Linear vs. Nonlinear Regression
The Base Case: Simple Linear Regression
11.2 Simple Linear Regression: The Basic Procedure
The Data
Showing the Scatter Diagram
Fitting a Line to the Data
The Least Squares Criterion
Identifying the Least Squares Line
Producing the Slope and Intercept of the Best-Fitting Line
Locating the Values for a and b in a Computer Printout
11.3 Performance Measures in Regression: How Well Did We Do?
Standard Error of Estimate
Coefficient of Determination (r2)
Total Variation
Explained Variation
Correlation Coefficient (r)
Reading an Expanded Computer Printout
11.4 The Inference Side of Regression Analysis
Treating the Set of Observations as a Sample
Bridging the Gap between Sample and Population
11.5 Estimating the Intercept and Slope Terms for the Population Regression Line
The Sampling Distribution of the Sample Intercept
The Sampling Distribution of the Sample Slope
Building Confidence Intervals
Identifying Interval Estimates of α and β in a Computer Printout
11.6 The Key Hypothesis Test in Simple Regression
The Competing Positions
The Slope is the Key
Formalizing the Test
Identifying Hypothesis Testing Information in the Computer Printout
11.7 Estimating Values of y
Estimating an Expected Value of y
Estimating an Individual Value of y
11.8 A Complete Printout for Simple Linear Regression
11.9 Checking Errors (Residuals)
Identifying Problems
Autocorrelation
A Final Comment
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
12 Multiple Regression
12.1 The F Distribution
Basics of the F Distribution
Reading an F Table
12.2 Using the F Distribution in Simple Regression
The Mobile Apps Example Revisited
The t Test
The F Test
Reasonableness of the F test
Connection Between the t Test and the F Test
12.3 An Introduction to Multiple Regression
Getting Started
Interpreting the Coefficients
Performance Measures in Multiple Regression
12.4 The Inference Side of Multiple Regression
Testing the Statistical Significance of the Relationship
F Test Results
Using t tests to Test Individual Coefficients
Interpreting t Test Results
Confidence Intervals for Individual Coefficients
12.5 Building a Regression Model
What do we Learn from a Multiple Regression Model?
Why Not Conduct a Series of Simple Regressions?
The “Best” Set of Independent Variables
Adding Variables
Multicollinearity
Adjusted r2
Qualitative Variables
Interaction Effects
A Final Note: Be Prepared for Anything
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
13 F Tests and Analysis of Variance
13.1 The F Distribution
Basics of the F Distribution
Reading the F Table
13.2 Testing the Equality of Two Population Variances
Two-Tailed Tests
One-Tailed Tests
13.3 Testing the Equality of Means for Multiple Populations: One-Way Analysis of Variance
Preliminary Comments
The Formal Test
Within-Groups Sum of Squares
Between-Groups Sum of Squares
Mean Squares
Computing the Variance Ratio for Sample Results
Applying the Critical Value Rule
p-value Version of the Test
ANOVA Table
Summarizing the Test
Determining Which Means Are Different
13.4 Experimental Design
Completely Randomized Design
Block Designs
Factorial Designs
Other Designs
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
14 Chi-Square Tests
14.1 The Chi-Square (X2) Distribution
Basics of the Chi-Square Distribution
Distribution Shapes
Reading the Chi-Square Table
14.2 Chi-Square Tests for Differences in Population Proportions
Setting Up the Test
Calculating z-scores for Sample Results
Computing X2 stat
Using the X2 stat Value in the Test
Reaching a Conclusion
Summarizing the Test
A Table Format to Test Proportion Differences
14.3 Chi-Square Goodness-of-Fit Tests
An Example
Summarizing the Test
Extending the Approach
14.4 Chi-Square Tests of Independence
The Hypotheses
Calculating Expected Frequencies
Computing the Chi-Square Statistic
Reaching a Conclusion
Summarizing the Test
Minimum Cell Sizes
Key Formulas
Glossary
Chapter Exercises
Excel Exercises (Excel 2013)
End Notes
Appendix A: Tables
Appendix B: Selected Exercise Solutions
Index
EULA