Introduction to Probability and Statistics 15th Edition by William Mendenhall – Ebook PDF Instant Download/Delivery: 8214346755 , 9798214346755
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ISBN 10: 8214346755
ISBN 13: 9798214346755
Author: William Mendenhall
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Introduction to Probability and Statistics 15th Table of contents:
1. Describing Data with Graphs
1.1. Variables and Data
Types of Variables
1.1. Exercises
1.2. Graphs for Categorical Data
1.2. Exercises
1.3. Graphs for Quantitative Data
Pie Charts and Bar Charts
Line Charts
Dotplots
Stem and Leaf Plots
Interpreting Graphs with a Critical Eye
1.3. Exercises
1.4. Relative Frequency Histograms
1.4. Exercises
Key Concepts
Technology Today
Technology Today
Technology Today
Reviewing What You’ve Learned
Case Study
2. Describing Data with Numerical Measures
Introduction
2.1. Measures of Center
2.1. Exercises
2.2. Measures of Variability
2.2. Exercises
2.3. Understanding and Interpreting the Standard Deviation
The Empirical Rule
Approximating s Using the Range
2.3. Exercises
2.4. Measures of Relative Standing
z -Scores
Percentiles and Quartiles
The Five-Number Summary and the Box Plot
2.4. Exercises
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
3. Describing Bivariate Data
Introduction
3.1. Describing Bivariate Categorical Data
3.1. Exercises
3.2. Describing Bivariate Quantitative Data
Scatterplots
The Correlation Coefficient
The Least-Squares Line
3.2. Exercises
Key Concepts
Technology Today
Reviewing What You’ve Learned
Case Study
4. Probability
Introduction
4.1. Events and the Sample Space
4.1. Exercises
4.2. Calculating Probabilities Using Simple Events
4.2. Exercises
4.3. Useful Counting Rules
4.3. Exercises
4.4. Rules for Calculating Probabilities
Calculating Probabilities for Unions and Complements
Calculating Probabilities for Intersections
4.4. Exercises
4.5. Bayes’ Rule
Colorblindness
4.5. Exercises
Key Concepts and Formulas
Reviewing What You’ve Learned
Case Study
5. Discrete Probability Distributions
5.1. Discrete Random Variables and Their Probability Distributions
Random Variables
Probability Distributions
The Mean and Standard Deviation for a Discrete Random Variable
5.1. Exercises
5.2. The Binomial Probability Distribution
5.2. Exercises
5.3. The Poisson Probability Distribution
5.3. Exercises
5.4. The Hypergeometric Probability Distribution
5.4. Exercises
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
6. The Normal Probability Distribution
6.1. Probability Distributions for Continuous Random Variables
The Continuous Uniform Probability Distribution
The Exponential Probability Distribution
6.1. Exercises
6.2. The Normal Probability Distribution
The Standard Normal Random Variable
Calculating Probabilities for a General Normal Random Variable
6.2. Exercises
6.3. The Normal Approximation to the Binomial Probability Distribution
6.3. Exercises
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
7. Sampling Distributions
Introduction
7.1. Sampling Plans and Experimental Designs
7.1. Exercises
7.2. Statistics and Sampling Distributions
7.2. Exercises
7.3. The Central Limit Theorem and the Sample Mean
The Central Limit Theorem
The Sampling Distribution of the Sample Mean
Standard Error of the Sample Mean
7.3. Exercises
7.4. Assessing Normality
7.5. The Sampling Distribution of the Sample Proportion
7.5. Exercises
7.6. A Sampling Application: Statistical Process Control (Optional)
A Control Chart for the Process Mean: The x ¯ Chart
A Control Chart for the Proportion Defective: The p Chart
7.6. Exercises
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
8. Large-Sample Estimation
8.1. Where We’ve Been and Where We’re Going
Statistical Inference
Types of Estimators
8.2. Point Estimation
8.2. Exercises
8.3. Interval Estimation
Constructing a Confidence Interval
Large-Sample Confidence Interval for a Population Mean μ
Interpreting the Confidence Interval
Large-Sample Confidence Interval for a Population Proportion p
8.3. Exercises
8.4. Estimating the Difference Between Two Population Means
8.4. Exercises
8.5. Estimating the Difference Between Two Binomial Proportions
8.5. Exercises
8.6. One-Sided Confidence Bounds
8.6. Exercises
8.7. Choosing the Sample Size
8.7. Exercises
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
9. Large-Sample Tests of Hypotheses
Introduction
9.1. A Statistical Test of Hypothesis
9.1. Exercises
9.2. A Large-Sample Test About a Population Mean
The Essentials of the Test
Calculating the p -Value
Two Types of Errors
The Power of a Statistical Test
9.2. Exercises
9.3. A Large-Sample Test of Hypothesis for the Difference Between Two Population Means
Hypothesis Testing and Confidence Intervals
9.3. Exercises
9.4. A Large-Sample Test of Hypothesis for a Binomial Proportion
Statistical Significance and Practical Importance
9.4. Exercises
9.5. A Large-Sample Test of Hypothesis for the Difference Between Two Binomial Proportions
9.5. Exercises
9.6. Concluding Comments on Testing Hypotheses
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
10. Inference from Small Samples
Introduction
10.1. Student’s t Distribution
Assumptions behind Student’s t Distribution
10.1. Exercises
10.2. Small-Sample Inferences Concerning A Population Mean
10.2. Exercises
10.3. Small-Sample Inferences for the Difference Between Two Population Means: Independent Random Samples
10.3. Exercises
10.4. Small-Sample Inferences for the Difference Between Two Means: A Paired-Difference Test
10.4. Exercises
10.5. Inferences Concerning a Population Variance
10.5. Exercises
10.6. Comparing Two Population Variances
10.6. Exercises
10.7. Revisiting the Small-Sample Assumptions
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
11. The Analysis of Variance
11.1. The Design of an Experiment
Basic Definitions
What Is an Analysis of Variance?
The Assumptions for an Analysis of Variance
11.1. Exercises
11.2. The Completely Randomized Design: A One-Way Classification
Partitioning the Total Variation in the Experiment
Testing the Equality of the Treatment Means
Estimating Differences in the Treatment Means
11.2. Exercises
11.3. Ranking Population Means
11.3. Exercises
11.4. The Randomized Block Design: A Two-Way Classification
Partitioning the Total Variation in the Experiment
Testing the Equality of the Treatment and Block Means
Identifying Differences in the Treatment and Block Means
Some Cautionary Comments on Blocking
11.4. Exercises
11.5. The a × b Factorial Experiment: A Two-Way Classification
The Analysis of Variance for an a × b Factorial Experiment
11.5. Exercises
11.6. Revisiting the Analysis of Variance Assumptions
Residual Plots
11.7. A Brief Summary
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
12. Simple Linear Regression and Correlation
Introduction
12.1. Simple Linear Regression
A Simple Linear Model
The Method of Least Squares
12.1. Exercises
12.2. An Analysis of Variance for Linear Regression
12.2. Exercises
12.3. Testing the Usefulness of the Linear Regression Model
Inferences About Concerning β , the Slope of the Line of Means
The Analysis of Variance F -Test
Measuring the Strength of the Relationship: The Coefficient of Determination
Interpreting the Results of a Significant Regression
12.3. Exercises
12.4. Diagnostic Tools for Checking the Regression Assumptions
Dependent Error Terms
Residual Plots
12.4. Exercises
12.5. Estimation and Prediction Using the Fitted Line
12.5. Exercises
12.6. Correlation Analysis
12.6. Exercises
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
13. Multiple Regression Analysis
Introduction
13.1. The Multiple Regression Model
13.2. Multiple Regression Analysis
The Method of Least Squares
The Analysis of Variance
Testing the Usefulness of the Regression Model
Interpreting the Results of a Significant Regression
Best Subsets Regression
Checking the Regression Assumptions
Using the Regression Model for Estimation and Prediction
13.2. Exercises
13.3. A Polynomial Regression Model
13.3. Exercises
13.4. Using Quantitative and Qualitative Predictor Variables in a Regression Model
13.4. Exercises
13.5. Testing Sets of Regression Coefficients
13.6. Other Topics in Multiple Linear Regression
Interpreting Residual Plots
Stepwise Regression Analysis
Binary Logistic Regression
Misinterpreting a Regression Analysis
13.7. Steps to Follow When Building a Multiple Regression Model
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
14. Analysis of Categorical Data
14.1. The Multinomial Experiment and the Chi-Square Statistic
14.2. Testing Specified Cell Probabilities: The Goodness-of-Fit Test
14.2. Exercises
14.3. Contingency Tables: A Two-Way Classification
The Chi-Square Test of Independence
14.3. Exercises
14.4. Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals
14.4. Exercises
14.5. Other Topics in Categorical Data Analysis
The Equivalence of Statistical Tests
Other Applications of the Chi-Square Test
Key Concepts and Formulas
Technology Today
Reviewing What You’ve Learned
Case Study
15. Nonparametric Statistics
Introduction
15.1. The Wilcoxon Rank Sum Test: Independent Random Samples
Normal Approximation for the Wilcoxon Rank Sum Test
15.1. Exercises
15.2. The Sign Test for a Paired Experiment
Normal Approximation for the Sign Test
15.2. Exercises
15.3. A Comparison of Statistical Tests
15.4. The Wilcoxon Signed-Rank Test for A Paired Experiment
Normal Approximation for the Wilcoxon Signed-Rank Test
15.4. Exercises
15.5. The Kruskal–Wallis H -Test for Completely Randomized Designs
15.5. Exercises
15.6. The Friedman F r -Test For Randomized Block Designs
15.6. Exercises
15.7. Rank Correlation Coefficient
15.7. Exercises
15.8. Summary
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