Lecture_3_Visualizing_Confidence_Intervals_and_Hypothesis_Testing

# Visualizing confidence intervals and hypothe

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Unformatted text preview: ZING CONFIDENCE INTERVALS and HYPOTHE Checkpoint 2 and Checkpoint 3 Visualizing CONFIDENCE INTERVALS Visualizing Hypothesis Testing Margin of Error A common Margin of Error (ME) is ME=2 · SE VISUALIZING CONFIDENCE INTERVALS and HYPOTHE Checkpoint 2 and Checkpoint 3 Visualizing CONFIDENCE INTERVALS Visualizing Hypothesis Testing 95% Conﬁdence Interval Sample Statistics ± 2 · SE VISUALIZING CONFIDENCE INTERVALS and HYPOTHE Checkpoint 2 and Checkpoint 3 Visualizing CONFIDENCE INTERVALS Visualizing Hypothesis Testing Approximating the Population Key idea: if the observed sample is representative of the population, then the population can be approximated by many copies of the sample. VISUALIZING CONFIDENCE INTERVALS and HYPOTHE Checkpoint 2 and Checkpoint 3 Visualizing CONFIDENCE INTERVALS Visualizing Hypothesis Testing Bootstrap Distribution Generate many samples (bootstrap samples) from the original sample Obtain the sample statistics (bootstrap statistics) Create a distribution for the bootstrap statistics. Assess the variability in sample statistics with a bootstrap distribution. Use a bootstrap distribution to calculate ME and Conﬁdence interval VISUALIZING CONFIDENCE INTERVALS and HYPOTHE Checkpoint 2 and Checkpoint 3 Visualizing CONFIDENCE INTERVALS Visualizing Hypothesis Testing Bootstrap Distribution To construct a bootstrap distribution we: Generate bootstrap samples with replacement from the original sample, using the same sample size Compute the statistic of interest for each of the bootstrap samples Collect the statistics from many (usually at least 1000) bootstrap samples into a bootstrap distribution VISUALIZING CONFIDENCE INTERVALS and HYPOTHE Checkpoint 2 and Checkpoint 3 Visualizing CONFIDENCE INTERVALS Visuali...
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## This note was uploaded on 03/12/2014 for the course STATS 10 taught by Professor Ioudina during the Spring '08 term at UCLA.

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