Basic Statistical Concepts

Badi Baltagi

Chapter Chapter 2 in Econometrics, 2011, pp 13-47 from Springer

Abstract: Abstract One chapter cannot possibly review what one learned in one or two pre-requisite courses in statistics. This is an econometrics book, and it is imperative that the student have taken at least one solid course in statistics. The concepts of a random variable, whether discrete or continuous, and the associated probability function or probability density function (p.d.f.) are assumed known. Similarly, the reader should know the following statistical terms: Cumulative distribution function, marginal, conditional and joint p.d.f.’s. The reader should be comfortable with computing mathematical expectations, and familiar with the concepts of independence, Bayes Theorem and several continuous and discrete probability distributions. These distributions include: the Bernoulli, Binomial, Poisson, Geometric, Uniform, Normal, Gamma, Chi-squared (X 2), Exponential, Beta, t and F distributions.

Keywords: Maximum Likelihood Estimation; Unbiased Estimator; Moment Generate Function; Wald Statistic; Moment Estimator (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-3-642-20059-5_2

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DOI: 10.1007/978-3-642-20059-5_2

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