Probability models, statistical methods, and the information to be gained from them is vital for work in business, engineering, sciences (including social and behavioral), and other fields. Data must be properly collected, analyzed and interpreted in order for the results to be used with confidence.
Award-winning author George Roussas introduces readers with no prior knowledge in probability or statistics to a thinking process to guide them toward the best solution to a posed question or situation. An Introduction to Probability and Statistical Inference provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations.
Chapter 1 Some Motivating Examples and Some Fundamental Concepts
Chapter 2 The Concept of Probability and Some Basic Results
Chapter 3 Numerical Characteristics of a Random Variable, Some Special Random Variables
Chapter 4 Joint and Conditional p.d.f.'s, Conditional Expectation and Variance, Moment Generating Function, Covariance and Correlation Coefficient
Chapter 5 Independence of Random Variables and Some Applications
Chapter 6 Transformation of Random Variables
Chapter 7 Some Modes of Convergence of Random Variables, Applications
Chapter 8 An Overview of Statistical Inference
Chapter 9 Point Estimation
Chapter 10 Confidence Intervals and Confidence Regions
Chapter 11 Testing Hypotheses
Chapter 12 More About Testing Hypotheses
Chapter 13 A Simple Linear Regression Model
Chapter 14 Two Analysis of Variance Models
Chapter 15 Some Topics in Nonparametric Inference
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