Test Bank for Statistics for Business and Economics 8th edition by Paul Newbold – Ebook PDF Instant Download/Delivery: 0273767062 , 978-0273767060
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ISBN 10: 0273767062
ISBN 13: 978-0273767060
Author: Paul Newbold
For courses in Business Statistics. A classic text for accuracy and statistical precision. Statistics for Business and Economics enables students to conduct serious analysis of applied problems rather than running simple “canned” applications. This text is also at a mathematically higher level than most business statistics texts and provides students with the knowledge they need to become stronger analysts for future managerial positions. The eighth edition of this book has been revised and updated to provide students with improved problem contexts for learning how statistical methods can improve their analysis and understanding of business and economics. Available with MyStatLab. MyStatLab courses use the rock solid Pearson MathXL technology for online tutorial and homework exercises that regenerate algorithmically for unlimited practice and mastery. MyStatLab courses also include essay questions that can be assigned for online tests and quizzes and other resources designed specifically to help students succeed in Statistics. Use this proven technology to help you and your students succeed. Visit mystatlab.com for more information.
Statistics for Business and Economics 8th Table of contents:
CHAPTER 1 Using Graphs to Describe Data
1.1 Decision Making in an Uncertain Environment
Random and Systematic Sampling
Sampling and Nonsampling Errors
1.2 Classification of Variables
Categorical and Numerical Variables
Measurement Levels
1.3 Graphs to Describe Categorical Variables
Tables and Charts
Cross Tables
Pie Charts
Pareto Diagrams
1.4 Graphs to Describe Time-Series Data
1.5 Graphs to Describe Numerical Variables
Frequency Distributions
Histograms and Ogives
Shape of a Distribution
Stem-and-Leaf Displays
Scatter Plots
1.6 Data Presentation Errors
Misleading Histograms
Misleading Time-Series Plots
CHAPTER 2 Using Numerical Measures to Describe Data
2.1 Measures of Central Tendency and Location
Mean, Median, and Mode
Shape of a Distribution
Geometric Mean
Percentiles and Quartiles
2.2 Measures of Variability
Range and Interquartile Range
Box-and-Whisker Plots
Variance and Standard Deviation
Coefficient of Variation
Chebyshev’s Theorem and the Empirical Rule
z-Score
2.3 Weighted Mean and Measures of Grouped Data
2.4 Measures of Relationships Between Variables
Case Study: Mortgage Portfolio
CHAPTER 3 Elements of Chance: Probability Methods
3.1 Random Experiment, Outcomes, and Events
3.2 Probability and Its Postulates
Classical Probability
Permutations and Combinations
Relative Frequency
Subjective Probability
3.3 Probability Rules
Conditional Probability
Statistical Independence
3.4 Bivariate Probabilities
Odds
Overinvolvement Ratios
3.5 Bayes’ Theorem
Subjective Probabilities in Management Decision Making
CHAPTER 4 Discrete Probability Distributions
4.1 Random Variables
4.2 Probability Distributions for Discrete Random Variables
4.3 Properties of Discrete Random Variables
Expected Value of a Discrete Random Variable
Variance of a Discrete Random Variable
Mean and Variance of Linear Functions of a Random Variable
4.4 Binomial Distribution
Developing the Binomial Distribution
4.5 Poisson Distribution
Poisson Approximation to the Binomial Distribution
Comparison of the Poisson and Binomial Distributions
4.6 Hypergeometric Distribution
4.7 Jointly Distributed Discrete Random Variables
Conditional Mean and Variance
Computer Applications
Linear Functions of Random Variables
Covariance
Correlation
Portfolio Analysis
CHAPTER 5 Continuous Probability Distributions
5.1 Continuous Random Variables
The Uniform Distribution
5.2 Expectations for Continuous Random Variables
5.3 The Normal Distribution
Normal Probability Plots
5.4 Normal Distribution Approximation for Binomial Distribution
Proportion Random Variable
5.5 The Exponential Distribution
5.6 Jointly Distributed Continuous Random Variables
Linear Combinations of Random Variables
Financial Investment Portfolios
Cautions Concerning Finance Models
CHAPTER 6 Distributions of Sample Statistics
6.1 Sampling from a Population
Development of a Sampling Distribution
6.2 Sampling Distributions of Sample Means
Central Limit Theorem
Monte Carlo Simulations: Central Limit Theorem
Acceptance Intervals
6.3 Sampling Distributions of Sample Proportions
6.4 Sampling Distributions of Sample Variances
CHAPTER 7 Confidence Interval Estimation: One Population
7.1 Properties of Point Estimators
Unbiased
Most Efficient
7.2 Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Known
Intervals Based on the Normal Distribution
Reducing Margin of Error
7.3 Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Unknown
Student’s t Distribution
Intervals Based on the Student’s t Distribution
7.4 Confidence Interval Estimation for Population Proportion (Large Samples)
7.5 Confidence Interval Estimation for the Variance of a Normal Distribution
7.6 Confidence Interval Estimation: Finite Populations
Population Mean and Population Total
Population Proportion
7.7 Sample-Size Determination: Large Populations
Mean of a Normally Distributed Population, Known Population Variance
Population Proportion
7.8 Sample-Size Determination: Finite Populations
Sample Sizes for Simple Random Sampling: Estimation of the Population Mean or Total
Sample Sizes for Simple Random Sampling: Estimation of Population Proportion
CHAPTER 8 Confidence Interval Estimation: Further Topics
8.1 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Dependent Samples
8.2 Confidence Interval Estimation of the Difference Between Two Normal Population Means: Independent Samples
Two Means, Independent Samples, and Known Population Variances
Two Means, Independent Samples, and Unknown Population Variances Assumed to Be Equal
Two Means, Independent Samples, and Unknown Population Variances Not Assumed to Be Equal
8.3 Confidence Interval Estimation of the Difference Between Two Population Proportions (Large Samples)
CHAPTER 9 Hypothesis Tests of a Single Population
9.1 Concepts of Hypothesis Testing
9.2 Tests of the Mean of a Normal Distribution: Population Variance Known
p-Value
Two-Sided Alternative Hypothesis
9.3 Tests of the Mean of a Normal Distribution: Population Variance Unknown
9.4 Tests of the Population Proportion (Large Samples)
9.5 Assessing the Power of a Test
Tests of the Mean of a Normal Distribution: Population Variance Known
Power of Population Proportion Tests (Large Samples)
9.6 Tests of the Variance of a Normal Distribution
CHAPTER 10 Two Population Hypothesis Tests
10.1 Tests of the Difference Between Two Normal Population Means: Dependent Samples
Two Means, Matched Pairs
10.2 Tests of the Difference Between Two Normal Population Means: Independent Samples
Two Means, Independent Samples, Known Population Variances
Two Means, Independent Samples, Unknown Population Variances Assumed to Be Equal
Two Means, Independent Samples, Unknown Population Variances Not Assumed to Be Equal
10.3 Tests of the Difference Between Two Population Proportions (Large Samples)
10.4 Tests of the Equality of the Variances Between Two Normally Distributed Populations
10.5 Some Comments on Hypothesis Testing
CHAPTER 11 Two Variable Regression Analysis
11.1 Overview of Linear Models
11.2 Linear Regression Model
11.3 Least Squares Coefficient Estimators
Computer Computation of Regression Coefficients
11.4 The Explanatory Power of a Linear Regression Equation
Coefficient of Determination, R2
11.5 Statistical Inference: Hypothesis Tests and Confidence Intervals
Hypothesis Test for Population Slope Coefficient Using the F Distribution
11.6 Prediction
11.7 Correlation Analysis
Hypothesis Test for Correlation
11.8 Beta Measure of Financial Risk
11.9 Graphical Analysis
CHAPTER 12 Multiple Variable Regression Analysis
12.1 The Multiple Regression Model
Model Specification
Model Objectives
Model Development
Three-Dimensional Graphing
12.2 Estimation of Coefficients
Least Squares Procedure
12.3 Explanatory Power of a Multiple Regression Equation
12.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients
Confidence Intervals
Tests of Hypotheses
12.5 Tests on Regression Coefficients
Tests on All Coefficients
Test on a Subset of Regression Coefficients
Comparison of F and t Tests
12.6 Prediction
12.7 Transformations for Nonlinear Regression Models
Quadratic Transformations
Logarithmic Transformations
12.8 Dummy Variables for Regression Models
Differences in Slope
12.9 Multiple Regression Analysis Application Procedure
Model Specification
Multiple Regression
Effect of Dropping a Statistically Significant Variable
Analysis of Residuals
CHAPTER 13 Additional Topics in Regression Analysis
13.1 Model-Building Methodology
Model Specification
Coefficient Estimation
Model Verification
Model Interpretation and Inference
13.2 Dummy Variables and Experimental Design
Experimental Design Models
Public Sector Applications
13.3 Lagged Values of the Dependent Variable as Regressors
13.4 Specification Bias
13.5 Multicollinearity
13.6 Heteroscedasticity
13.7 Autocorrelated Errors
Estimation of Regressions with Autocorrelated Errors
Autocorrelated Errors in Models with Lagged Dependent Variables
CHAPTER 14 Introduction to Nonparametric Statistics
14.1 Goodness-of-Fit Tests: Specified Probabilities
14.2 Goodness-of-Fit Tests: Population Parameters Unknown
A Test for the Poisson Distribution
A Test for the Normal Distribution
14.3 Contingency Tables
14.4 Nonparametric Tests for Paired or Matched Samples
Sign Test for Paired or Matched Samples
Wilcoxon Signed Rank Test for Paired or Matched Samples
Normal Approximation to the Sign Test
Normal Approximation to the Wilcoxon Signed Rank Test
Sign Test for a Single Population Median
14.5 Nonparametric Tests for Independent Random Samples
Mann-Whitney U Test
Wilcoxon Rank Sum Test
14.6 Spearman Rank Correlation
14.7 A Nonparametric Test for Randomness
Runs Test: Small Sample Size
Runs Test: Large Sample Size
CHAPTER 15 Analysis of Variance
15.1 Comparison of Several Population Means
15.2 One-Way Analysis of Variance
Multiple Comparisons Between Subgroup Means
Population Model for One-Way Analysis of Variance
15.3 The Kruskal-Wallis Test
15.4 Two-Way Analysis of Variance: One Observation per Cell, Randomized Blocks
15.5 Two-Way Analysis of Variance: More Than One Observation per Cell
CHAPTER 16 Forecasting with Time-Series Models
16.1 Components of a Time Series
16.2 Moving Averages
Extraction of the Seasonal Component Through Moving Averages
16.3 Exponential Smoothing
The Holt-Winters Exponential Smoothing Forecasting Model
Forecasting Seasonal Time Series
16.4 Autoregressive Models
16.5 Autoregressive Integrated Moving Average Models
CHAPTER 17 Sampling: Stratified, Cluster, and Other Sampling Methods
17.1 Stratified Sampling
Analysis of Results from Stratified Random Sampling
Allocation of Sample Effort Among Strata
Determining Sample Sizes for Stratified Random Sampling with SpecifiedDegree of Precision
17.2 Other Sampling Methods
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