infrnc127s08_bw_p1

# infrnc127s08_bw_p1 - Stat-Bus 127 Confidence Intervals Dr...

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Stat-Bus 127 Confidence Intervals Dr. Linda M. Penas Spring 2008 1 Review from Sampling 2 ~( , ) X XN Z μ μσ σ →= 2 ~, X Z n n ⎛⎞ ⎜⎟ ⎝⎠ Review from Sampling 0 , 1 ) / X ZN n = CASE 1 : σ 2 known Review from Sampling 0 , 1 ) / X sn = CASE 2 : σ 2 unknown, n 30 (large) Use sample variance (s 2 ) for population variance ( σ 2 ) and apply Case 1 Review from Sampling 1 ) / X Tt d f n = =− CASE 3 : σ 2 unknown, n < 30 Cannot just use sample variance (s 2 ) for population variance ( σ 2 ) and still get N(0,1) SAMPLING DIST CHART See pages 237- 238 in Lecture Notes ESTIMATION (p. 69) Estimation : area of statistical inference that deals with predicting the values of specific population parameters. Two types of estimation: point estimation : single numerical value used to estimate the corresponding parameter value.

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Stat-Bus 127 Confidence Intervals Dr. Linda M. Penas Spring 2008 2 EXAMPLE : Sample mean is a pt estimate for pop mean Sample variance is pt estimate for pop variance interval estimation : An interval estimate of the parameter θ is an interval of the form a < θ < b, where a and b depend on the point estimate for θ and its distribution. a is called the lower confidence limit (LCL) and b is called the upper confidence limit (UCL). We will be interested in constructing 100(1 - α )% confidence intervals (CI) for a parameter θ where 1 - α represents the confidence coefficient; I.e., P(a < θ < b) = 1 - α , 0 < α < 1 General Form of CI: Diagram CI for Mean, μ NOTE
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infrnc127s08_bw_p1 - Stat-Bus 127 Confidence Intervals Dr...

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