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Understanding Business Statistics 1st Edition Freed Test Bank
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Understanding Business Statistics 1st Edition Freed Test Bank
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Product Details:
- 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
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