1. AP Stat Review Know Your Book!
Mr. Lynch
AP Statistics

2. Chapter 1 Describe distribution: CSSO* – one variable
Box & Whisker, Histogram, Stem-Leaf, Bar graphs, Dot plots, Pie charts
Categorical vs. Quantitative
Symmetric vs. Skewing
Relative Freq. vs. Cumulative Freq. (percentiles)
5 # Summaries – (Min,Q1,Med,Q3,Max)
*Standard Deviation vs. Variance
*Linear Transformations

3. Chapter 2 Density Curves Normal Distributions “z”
Don’t say a distribution is “normal” just b/c it looks symmetric or unimodal.
Mean vs. Median  Skewing
Note: mean>median is not sufficient evidence to show that distribution is skewed right!
Empirical Rule ( Rule)
Population parameters vs. Sample statistics

4. Chapter 3 Bi-Variate data – 2 variables - scatter plots
Explanatory vs. Response variables
Describe distribution: FSDD*
Correlation coefficient (r) - (strength)
LSRL y(hat) = a + bx (don’t confuse r with b*)
Coefficient of determination (r2) - % Variation
Residuals = Actual – Predicted
Residual Plots – randomly distributed? Fit?
Know b,r,r2 in context!

5. Chapter 4 Linear Transformations
Exponential Models (x, logy)  makes linear
Power Models (logx, logy)  makes linear
Correlation & regression only for linear data
Interpolation vs. Extrapolation
Lurking variables
Common Response/Confounding/Causation
2-way tables – (conditional distributions)

6. Chapter 5 SRS* Experiments vs. Samples (surveys)
SRS ≠ Random Assignment – experiment to assign subjects to treatment group
Stratified Random Sample vs. Blocking Experimental groups
Blocking: grouping subjects in an experiment based on a characteristic of interest, reduces unwanted variability
Well Designed Experiments – can use cause-effect language)
Control/Randomization/Replication*

7. Chapter 5 (cont’d) Bias – “favoring” Voluntary Response
Undercoverage vs. Non-response
Larger samples  more accurate results
Double blind*
Confounding Variable – could contribute to the observed “causal link” b/w variables
Matched pairs
Simulation (PAARC or DAARC)*
Cluster Sampling

8. Chapter 6 Probability Chapter
Disjoint (Mutually Exclusive) – No common outcomes
Independent – One outcome doesn’t effect the other outcome
General Addition Rule (#6)
Conditional Probability Rule (#8)
Tree Diagrams – Bayes’s Rule
Law of Large Numbers
Two-Way Tables Again!

9. Chapter 7 Random Variables Probability distributions
Discrete vs. Continuous*
Finite vs. Infinite*
Expected value = mean
Transformation Formulas:

10. Chapter 8 Binomial and Geometric Distributions
FIST* – verify these conditions
B(n,p,k) Fixed # of trials
Mean=µ=np st.dev =√ (npq)
Normal Approx for large sample sizes
Rule of Thumb: np≥10, nq≥10
G(p,k) First success – infinite # of trials
mean=1/p

11. Chapter 9 Sampling Distributions Parameters vs. Statistics
Sample, Population, Sampling Distribution
Bias/Variability (high bias/low var. graphs)
center/spread
Central Limit Theorem – (means)*

12. Inference 10-14 Confidence Intervals and Tests of Signif.
Ch 10: means/ “z” b/c known pop. st.dev.
Ch 11: means/ “t”, differences
Ch 12: proportions/ “z”, differences p1 vs. p2
Ch 13: Chi-Square (GoF, Association, H?)
Ch 14: Regression for Slope (b)

13. Type I Error, Type II Error, Power of a Test
Type I Error: Rejecting H0/when H0 is true
P(I) = α α = significance level of test
Type II Error: Accepting H0/when H0 is false
P(II) = β
Power of a Test: Probability of correctly rejecting a null hypothesis (H0)
Power = 1 – P(II) = 1 – β
How to make your test more Powerful?
Increase n, increase α, (decrease σ, move Ha further away from H0)

14. Too Many P’s! Ch 6: “the probability of” P(A)=1/2
Ch 8: “the specific probability of a success” p=.75
p = the population proportion
p(hat) = the sample proportion
P = “the p-value” area under the curve in the tail, often compared to alpha and rejection regions