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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