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Chapter 2 in Undergraduate Econometrics
Chapter 2 in Undergraduate Econometrics

... Random Variable (r.v.) is a variable whose value is unknown until it is observed. The value of a random variable results from an experiment. Experiments can be either controlled (laboratory) or uncontrolled (observational). Most economic variables are random and are the result of uncontrolled experi ...
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... Would a randomly selected man who is 72 inches tall be considered unusually tall? Would it be unusual to have a random sample of 100 men where the sample average is 72 inches? ...
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Thu Jan 29 - Wharton Statistics

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... We would then be able to (and would prefer to) make a decision based on the probability Pr (d | s1 , s2 , h) instead of the probability Pr (d | s1 , s2 ), which again, is only an average over possible mechanisms h. Consider for example Table 1 which enumerates all nine causal mechanisms. In only fou ...
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... • Lowering  will result in a wider non- rejection region which makes it more likely that a false null hypothesis will be retained. • To see this redo the above exercise with  = 005 and .05. • Since our interest is usually centered on the null hypothesis  is usually chosen to be small; 10 percent ...
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Means and Variances of Random Variables

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... significance level you are using, the expected effect size and the sample size you are planning to use. When these values are entered, a power value between 0 and 1 will be generated. If the power is less than 0.8, you will need to increase your sample size. It is generally accepted that power shoul ...
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Review: Statistics

... Statistics for a data set o Find the mean, mean absolute deviation, variance, and standard deviation. o Prove and use this alternate formula for variance: x 2  (x ) 2 . o Prove and use that means and variances are additive. Repeated experiments o Given the statistics (mean, variance, and/or standar ...
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Register Number - IndiaStudyChannel
Register Number - IndiaStudyChannel

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Foundations of statistics

Foundations of statistics is the usual name for the epistemological debate in statistics over how one should conduct inductive inference from data. Among the issues considered in statistical inference are the question of Bayesian inference versus frequentist inference, the distinction between Fisher's ""significance testing"" and Neyman-Pearson ""hypothesis testing"", and whether the likelihood principle should be followed. Some of these issues have been debated for up to 200 years without resolution.Bandyopadhyay & Forster describe four statistical paradigms: ""(1) classical statistics or error statistics, (ii) Bayesian statistics, (iii) likelihood-based statistics, and (iv) the Akaikean-Information Criterion-based statistics"".Savage's text Foundations of Statistics has been cited over 10000 times on Google Scholar. It tells the following.It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics, there has seldom been such complete disagreement and breakdown of communication since the Tower of Babel. Doubtless, much of the disagreement is merely terminological and would disappear under sufficiently sharp analysis.
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