|Essentials of Probability & Statistics for Engineers & Scientists: Pearson New International Edition||
Essentials of Probability & Statistics for Engineers & Scientists: Pearson New International Edition
|85.90||approx. 7-9 days|
For junior/senior undergraduates taking a one-semester probability and statistics course as applied to engineering, science, or computer science.
This text covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of engineering and the sciences. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. Students using this text should have the equivalent of the completion of one semester of differential and integral calculus.
1. Introduction to Statistics and Probability
1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability
1.2 Sampling Procedures; Collection of Data
1.3 Discrete and Continuous Data.
1.4 Probability: Sample Space and Events
1.5 Counting Sample Points
1.6 Probability of an Event
1.7 Additive Rules
1.8 Conditional Probability, Independence, and the Product Rule
1.9 Bayes' Rule
2. Random Variables, Distributions, and Expectations
2.1 Concept of a Random Variable
2.2 Discrete Probability Distributions
2.3 Continuous Probability Distributions
2.4 Joint Probability Distributions
2.5 Mean of a Random Variable
2.6 Variance and Covariance of Random Variables.
2.7 Means and Variances of Linear Combinations of Random Variables
2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
3. Some Probability Distributions
3.1 Introduction and Motivation
3.2 Binomial and Multinomial Distributions
3.3 Hypergeometric Distribution
3.4 Negative Binomial and Geometric Distributions
3.5 Poisson Distribution and the Poisson Process
3.6 Continuous Uniform Distribution
3.7 Normal Distribution
3.8 Areas under the Normal Curve
3.9 Applications of the Normal Distribution
3.10 Normal Approximation to the Binomial
3.11 Gamma and Exponential Distributions
3.12 Chi-Squared Distribution.
3.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
4. Sampling Distributions and Data Descriptions
4.1 Random Sampling
4.2 Some Important Statistics
4.3 Sampling Distributions
4.4 Sampling Distribution of Means and the Central Limit Theorem
4.5 Sampling Distribution of S2
4.8 Graphical Presentation
4.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
5. One- and Two-Sample Estimation Problems
5.2 Statistical Inference
5.3 Classical Methods of Estimation.
5.4 Single Sample: Estimating the Mean
5.5 Standard Error of a Point Estimate
5.6 Prediction Intervals
5.7 Tolerance Limits
5.8 Two Samples: Estimating the Difference between Two Means
5.9 Paired Observations
5.10 Single Sample: Estimating a Proportion
5.11 Two Samples: Estimating the Difference between Two Proportions
5.12 Single Sample: Estimating the Variance
5.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
6. One- and Two-Sample Tests of Hypotheses.
6.1 Statistical Hypotheses: General Concepts
6.2 Testing a Statistical Hypothesis
6.3 The Use of P-Values for Decision Making in Testing Hypotheses
6.4 Single Sample: Tests Concerning a Single Mean
6.5 Two Samples: Tests on Two Means
6.6 Choice of Sample Size for Testing Means
6.7 Graphical Methods for Comparing Means
6.8 One Sample: Test on a Single Proportion.
6.9 Two Samples: Tests on Two Proportions
6.10 Goodness-of-Fit Test
6.11 Test for Independence (Categorical Data)
6.12 Test for Homogeneity
6.13 Two-Sample Case Study
6.14 Potential Misconceptions and Hazards;
Relationship to Material in Other Chapters
7. One-Factor Experiments: General
7.1 Analysis-of-Variance Technique and the Strategy of Experimental Design
7.2 One-Way Analysis of Variance (One-Way ANOVA): Completely Randomized Design
7.3 Tests for the Equality of Several Variances
7.4 Multiple Comparisons
7.5 Concept of Blocks and the Randomized Complete Block Design
7.6 Random Effects Models
7.7 Case Study for One-Way Experiment
7.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters
8. Linear Regression
8.1 Introduction to Linear Regression
8.2 The Simple Linear Regression (SLR) Model and the Least Squares Method.
8.3 Inferences Concerning the Regression Coefficients.
8.5 Analysis-of-Variance Approach
8.6 Test for Linearity of Regression: Data with Repeated Observations
8.7 Diagnostic Plots of Residuals: Graphical Detection of Violation of Assumptions
8.9 Simple Linear Regression Case Study.
8.10 Multiple Linear Regression and Estimation of the Coefficients
8.11 Inferences in Multiple Linear Regression
9. Factorial Experiments (Two or More Factors)
9.2 Interaction in the Two-Factor Experiment
9.3 Two-Factor Analysis of Variance
9.4 Three-Factor Experiments.
9.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters