Lecture 4 2010

# Lecture 4 2010 - I. II. A. B. Introduction

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1 I. Introduction II. Statistical and Notational Preliminaries A. Introduction B. Elements of Statistical Theory 1. Prerequisites: ± Rd Vib l Random Variables ± Distributions: Population vs. Sample ± Descriptive Measures ± Estimation ± Sampling distributions. ± Inference: CIs and Hypothesis Testing. Today The population distribution from which I drew your samples. Any concerns? \$3,473,078 Y σ = \$2,581,563 Y μ = Experiment: Sampling Distribution for samples from population of 2003 MLB ball players. Will the sampling distribution be normally distributed for n = 36? Why? Characterize the n = 36 sampling distribution. 1. Shape: 2. Center: 3. Variation: \$3,473,078 9 Y Y n == μ Y = \$2,581,563. It will be normally distributed.

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2 0.0000005 0.0000006 Sampling Distribution for Mean 2003 MLB Salaries, n=36. \$2,581,563 Y Y μ == ( 1.645 1.645 ) 0.90 YY PY μσμσ −⋅ < < +⋅ = \$3,473,078 \$578,846 36 Y Y n σ = 0 0.0000001 0.0000002 0.0000003 0.0000004 \$0 \$1,000,000 \$2,000,000 \$3,000,000 \$4,000,000 \$5,000,000 \$1,629,361 \$3,533,765 Example: Confidence Interval Estimation – Assume no knowledge of population standard deviation. Draw a sample.
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## This note was uploaded on 12/08/2011 for the course ECON 312 taught by Professor Daniellass during the Winter '10 term at UMass (Amherst).

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Lecture 4 2010 - I. II. A. B. Introduction

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