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Paul Cartledge

Chapters 1-6: Population vs. sample Æ Parameter vs. statistic Statistics: Descriptive vs. inferential Types of variables Quantitative vs. Qualitative / | Discrete Continuous Tables, charts & graphs - frequency tables - qualitative: bar graph/pie chart - stem-and-leaf plot/dot plot - time plot - histogram (modality) - traits: # of modes, tail weight, overall shape (symmetry, skewness) - identify skewness by TAIL - boxplot (skewness) - outliers, overall shape (symmetry, skewness) - identify skewness inside box or entire graph Measures of center/spread/position - center: mean, median, mode Æ Outlier effect? Skewness effect? - spread: range, variance, standard deviation, IQR Æ Why use squared and (n – 1)? Ever negative? Empirical Rule? - position: min, max, percentiles (quartiles) Æ recall that we INCLUDE the median when determining quartiles Æ 5-number summary, boxplot, types of outliers Chapters 7-10: Displaying bivariate data - scatterplot: visual aid to see form/strength/direction of relationship and/or outliers (large residual, high leverage, influential) - correlation: numerical aid to see strength/direction of relationship (range?) Æ Warning: assumes linearity, sensitive to outliers Simple linear regression analysis - regression line: ŷ = b + b x 0 1 ⎛ sy ⎞ - least-squares estimation gives b1 = r⎜ ⎟ and b 0 y −b x 1 ⎝ sx ⎠ - estimation: interpolation vs. extrapolation (BAD!) - R-squared: r 2 = proportion of variation in y explained by x - causation: association does NOT imply causation - residual plots: observed vs. theoretical appearance - transformation of a variable can help improve linearity Chapter 11-13: - observational/retrospective/prospective study, experiment/controlled clinical trial Æ population and causal inferences (what needs to be present for each?) - types of bias (response, undercoverage, nonresponse) - types of sampling: with/without replacement, SRS/stratified/cluster/ voluntary/convenience/systematic - controlling factors: randomization, blocking, direct control, replication - more experiment design definitions Chapters 14-15: - types of events: marginal, conditional, union, intersection, complement, - What common words identify them? - relating events: dependent vs. disjoint vs. independent - Do these relations affect the rules below? If so, how? - Do they allow certain rules to be easily extended? - probability laws: - conditional probability: P(A| B) =P(A∩ B) P(B) - complement rule:P(A ) = 1 – P(A) - multiplication rule: P( A∩ B) = P(A and B) = P(A) × P(B | A) = P(B) × P(A | B) - addition rule: P(A or B) = P(A) + P(B) – P(A and B) - total probability rule:A) P= +()∩P ∩A B C ( ) - recall examples where we combined a few of these together Chapter 16-17: Distributions - discrete (exact probability or intervals) vs. continuous (only intervals) DisIcfrete: P(X = a) > 0, the
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