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Statistics 13th Edition by McClave and Sincich solution manual


Table of Content: 1. Statistics, Data, and Statistical Thinking
1.1 The Science of Statistics
1.2 Types of Statistical Applications
1.3 Fundamental Elements of Statistics
1.4 Types of Data
1.5 Collecting Data: Sampling and Related Issues
1.6 The Role of Statistics in Critical Thinking and Ethics
2. Methods for Describing Sets of Data
2.1 Describing Qualitative Data
2.2 Graphical Methods for Describing Quantitative Data
2.3 Numerical Measures of Central Tendency
2.4 Numerical Measures of Variability
2.5 Using the Mean and Standard Deviation to Describe Data
2.6 Numerical Measures of Relative Standing
2.7 Methods for Detecting Outliers: Box Plots and z-Scores
2.8 Graphing Bivariate Relationships (Optional)
2.9 Distorting the Truth with Descriptive Statistics
3. Probability
3.1 Events, Sample Spaces, and Probability
3.2 Unions and Intersections
3.3 Complementary Events
3.4 The Additive Rule and Mutually Exclusive Events
3.5 Conditional Probability
3.6 The Multiplicative Rule and Independent Events
3.7 Some Additional Counting Rules (Optional)
3.8 Bayes’s Rule (Optional)
4. Discrete Random Variables
4.1 Two Types of Random Variables
4.2 Probability Distributions for Discrete Random Variables
4.3 Expected Values of Discrete Random Variables
4.4 The Binomial Random Variable
4.5 The Poisson Random Variable (Optional)
4.6 The Hypergeometric Random Variable (Optional)
5. Continuous Random Variables
5.1 Continuous Probability Distributions
5.2 The Uniform Distribution
5.3 The Normal Distribution
5.4 Descriptive Methods for Assessing Normality
5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional)
5.6 The Exponential Distribution (Optional)
6. Sampling Distributions
6.1 The Concept of a Sampling Distribution
6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance
6.3 The Sampling Distribution of (x-bar) and the Central Limit Theorem
6.4 The Sampling Distribution of the Sample Proportion
7. Inferences Based on a Single Sample: Estimation with Confidence Intervals
7.1 Identifying and Estimating the Target Parameter
7.2 Confidence Interval for a Population Mean: Normal (z) Statistic
7.3 Confidence Interval for a Population Mean: Student’s t-Statistic
7.4 Large-Sample Confidence Interval for a Population Proportion
7.5 Determining the Sample Size
7.6 Confidence Interval for a Population Variance (Optional)
8. Inferences Based on a Single
Sample: Tests of Hypothesis
8.1 The Elements of a Test of Hypothesis
8.2 Formulating Hypotheses and Setting Up the Rejection Region
8.3 Observed Significance Levels: p-Values
8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
8.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic
8.6 Large-Sample Test of Hypothesis about a Population Proportion
8.7 Calculating Type II Error Probabilities: More about β (Optional)
8.8 Test of Hypothesis about a Population Variance (Optional)
9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
9.1 Identifying the Target Parameter
9.2 Comparing Two Population Means: Independent Sampling
9.3 Comparing Two Population Means: Paired Difference Experiments
9.4 Comparing Two Population Proportions: Independent Sampling
9.5 Determining the Sample Size
9.6 Comparing Two Population Variances: Independent Sampling (Optional)
10. Analysis of Variance: Comparing More than Two Means
10.1 Elements of a Designed Study
10.2 The Completely Randomized Design: Single Factor
10.3 Multiple Comparisons of Means
10.4 The Randomized Block Design
10.5 Factorial Experiments: Two Factors
11. Simple Linear Regression
11.1 Probabilistic Models
11.2 Fitting the Model: The Least Squares Approach
11.3 Model Assumptions
11.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
11.5 The Coefficients of Correlation and Determination
11.6 Using the Model for Estimation and Prediction
11.7 A Complete Example
12. Multiple Regression and Model Building
12.1 Multiple-Regression Models
PART I: First-Order Models with Quantitative Independent Variables
12.2 Estimating and Making Inferences about the β Parameters
12.3 Evaluating Overall Model Utility
12.4 Using the Model for Estimation and Prediction
PART II: Model Building in Multiple Regression
12.5 Interaction Models
12.6 Quadratic and Other Higher Order Models
12.7 Qualitative (Dummy) Variable Models
12.8 Models with Both Quantitative and Qualitative Variables (Optional)
12.9 Comparing Nested Models (Optional)
12.10 Stepwise Regression (Optional)
PART III: Multiple Regression Diagnostics
12.11 Residual Analysis: Checking the Regression Assumptions
12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
13. Categorical Data Analysis
13.1 Categorical Data and the Multinomial Experiment
13.2 Testing Categorical Probabilities: One-Way
Table
13.3 Testing Categorical Probabilities: Two-Way (Contingency)
Table
13.4 A Word of Caution about Chi-Square Tests
14. Nonparametric Statistics (available online)
14.1 Introduction: Distribution-Free Tests
14.2 Single-Population Inferences
14.3 Comparing Two Populations: Independent Samples
14.4 Comparing Two Populations: Paired Difference Experiment
14.5 Comparing Three or More Populations: Completely Randomized Design
14.6 Comparing Three or More Populations: Randomized Block Design
14.7 Rank Correlation 14-48
Product Details: Language: English
ISBN-10: 0134080211
ISBN-13: 978-0134080215
ISBN-13: 9780134080215
Author: James T. McClave, Terry T Sincich
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