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Lab 1 Issues Basic Statistical Issues R Issues
Inappropriate measures of central tendency & dispersion for particular variable scales R Issues Some parts of the lab not done, e.g., didn’t make tables Code incorrect
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Concept Review Week 1 Week 2 Week 3 Week 4 Data, variables
Variable scales Hypotheses Populations, samples Week 2 Research Proposals Data visualizations Operationalizing your variables, validity and reliability Week 3 Sample representativeness and sampling strategies Central tendency and dispersion Week 4 Independence of variables Probability, empirical and theoretical determination The normal distribution
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Hypothesis Testing: review
Z = -1.96 Z = +1.96 You already know two methods to test H0: Yi = µ Calculate probability associated with particular Z score (or higher), if p < 0.05 reject H0 Compare particular Z score to a critical value such as 1.96 (for α of .05). Difficult to do this for lots of Yi
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Hypothesis Testing: Confidence Limits
Confidence limits define the empirical values outside of which we reject H0 Using our statistical population of monkey teeth pits: µ = 31, σ = 9 for 95% CL L = µ ± (1.96 x σ) L1 = 31 – (1.96 x 9) = 13.36 L2 = 31 + (1.96 x 9) = 48.64 Can compare any number of new Yi using 95% CL to test H0: Yi = µ 13.36 pits 48.64 pits
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Hypothesis Testing: Sample Means
σ = 1.5 Three methods to compare variates to population mean (H0: Yi = µ) H0: Yi = Y H0: Y = µ H0: Yi = Y2 15 samples of 3 flakes 20 samples of 10 flakes 30 samples of 20 flakes = = = Y = 3.1 Y = 3.1 Y = 3.0
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Hypothesis Testing: Sample Means
.87 Standard deviation: Standard error: σ = s = σY = .34 σY = σY = .27
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Hypothesis Testing: Sample Means
Three ways to test this Calculate 95% confidence limits for µ L = µ ± (1.96 x σY ) 7 + (1.96 x 2.3/√25) = 7.90 mm 7 - (1.96 x 2.3/√25) = 6.10 mm Calculate a Z-score/Probability Z = (6.2-7) / (2.3/√25) = Fail to reject H0 based on Z score ? 100s of storage pots across large site 25 pots in a room, is their Ythick different from pot population at the site? µ = 7, σ = 2.3; Ythick =6.2
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