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STATFINAL1 - ECON321(Economics Statistics Final Study Guide...

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ECON321(Economics Statistics) Final Study Guide Probability P (A | B ) – Conditional Probability (the probability of A given B) P (A | B) = P ( A B ) P ( B ) Union (both probabilities) Intersect (where they overlap) 1
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2
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Discrete Probability Distribution Probability Function / Distribution (understand which each means) Discrete = exact number value Continuous = interval value Expected Value (or mean) is a measure of central location for a random variable. Variance is used to measure variability in data Standard Deviation ( σ )– the positive square root of the variance Binomial Probability Distribution 3
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Binomial Probability Function Expected value for binomial distribution equals the mean ( μ ) NO POISSON & NO HYPER GEOMETRIC DISTRIBUTIONS! END OF CH.5 4
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Chapter 6, Continuous Probability Distributions Probability density function (instead of probability function w/ discrete variables) Normal Probability Distribution Function for Normal Distributions (on test, you will standardize so just understand the formula and terms, and remember to standardize on the exam) 5
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*********************************** NOT ON TEST********************************** Standard Normal Distribution -Mean ( μ ) =0 and a Standard Deviation ( σ ) =1 (look at z table for values) How to standardize (REMEMBER THIS! NOT ON FORMULA SHEET!) 6
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Example of how to standardize and how to interpret the curve information. X 2 , t , and F tests all later to come Chapter 7 Sampling and Sampling Distributions Parameters – numerical characterics of a population such as mean, standard deviation, variance etc… 7
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Sample Statistic – estimated value of a population parameter, computed by taking corresponding parameters from the sample. Sample Mean (x –bar ), Population (p), sample population (p-bar), mean ( μ ) Examples of each… Sampling Distribution Probability distribution of x-bar is the sampling distribution of x-bar P-bar is the sample proportion (of probability) 8
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Unbiased - When the expected value of a point estimator equals the population parameter 9
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***************** ABOVE FORMULAS WILL BE ON FORMULA SHEET *************** Standard Error = Standard deviation of x-bar Sampling Distribution of P-bar Properties of Point Estimators 10
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Efficiency – point estimators with a smaller standard error is said to have a “greater relative efficiency” Consistency indicates a good point estimator 11
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