Linear Regression

This module reviews the basic principles of probability and statistics covered in the FE Exam. We first review some basic parameters and definitions in statistics, such as mean and dispersion properties followed by computation of permutations and combinations. We then give the definitions of probability and the laws governing it and apply Bayes theorem. We study probability distributions and cumulative functions, and learn how to compute an expected value. Particular probability distributions covered are the binomial distribution, applied to discrete binary events, and the normal, or Gaussian, distribution. We show the meaning of confidence levels and intervals and how to use and apply them. We define and apply the central limit theorem to sampling problems and brieflyt- and c2. We define hypothesis testing and show how to apply it to random data. Finally, we show how to apply linear regression estimates to data and estimate the degree of fit including correlation coefficients and variances.In all cases, basic ideas and equations are presented along with sample problems that illustrate the major ideas and provide practice on expected exam questions. Time: Approximately 3 hours | Difficulty Level: Medium

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