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Chapter 5

Linneman Chapter 5.docx

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John Kervin

Linneman Chapter 5 –Confidence Intervals Sampling error: simply by chance, the sample that was drawn was not representative on the population • The more samples you take, the closer the distribution of sample means would get to being a bell curve Sampling distribution: the distribution of sample means of samples taken from the same population • Form the basis for theories and practices in inferential statistical techniques Standard Error • Standard deviation: average distance scores were from the mean • Standard error: is the average distance the means (sample distribution) are from the mean (sample’s distribution’s standard deviation) • Sample mean (x) and population mean (“mu” (u) • Standard error (sigma x, SE) = standard deviation / square root (n-1) SE= s √(N−1) Confidence Intervals (margin of error) • Procedure with claimed population mean o Actual distancenumber of standard errorspercentage probability • Procedure with confidence intervals: o Percentage probabilitynumber of standard errorsactual distance Confidence Intervals with Proportions • Pp 177 Conclusion • Using a sample mean to judge the legitimacy of claims about a population; • Using a sample mean to build a confidence interval within which we can confidently predict that the population mean falls Kranzler (Chapter 6 & 7) Kranzler Chapter 6-The Normal Curve • A curve with normal distribution Features of Normal Curve (all differ in terms of their means and standard deviation) • Symmetric around the mean (so the left side mirror the right side of the mean) • Unimodal (because they are symmetric so the most frequently observed –mode-is the same as the mean) • Since unimodal and symmetric, the mean, mode and median are equal • Asymptotic to the horizontal axis of distribution. The curved descends rapidly from the center, as you move along the horizontal axis. But it never touches the horizontal axis and is continuous as it is used to describe an infinity of observations • All normal curves have the same proportion of scores under the curve relative to particular location on the horizontal axis when the scores
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