Final Comprehensive AP Stats Review
Final review tips for the AP Statistics exam
1) Know your vocabulary!!!
2) Know your formulas!!!!
3) Know where to find the different tests using your TI-83, TI-84 or Casio calculator
4) Know the difference in describing a sample as large versus small and what this means when selecting the formulas.
5) When the observed value is greater than the critical value - REJECT THE NULL
6) When the p - value is less than the alpha value - REJECT THE NULL
7) Remember we never accept the alternative - REJECT THE NULL
When we reject the null the statement should read: We have sufficient evidence to doubt that the _____ is ______.
8)
When stating hypothesis, remember that the null contains the condition
of equality, the alternative hypothesis does NOT!!!!
9) When asked to describe a one-variable data set, always discuss shape, center, and spread.
10) Understand how skew-ness can be used to differentiate between the mean and the median.
11) Know how transformations of a data set affect summary statistics.
12)
Be careful when using normal as an adjective. Normal refers to a
specific distribution, not the general shape of a graph of a data set.
It’s better to use approximately normal, mound shaped, bell-shaped
instead. You will be docked for saying something like “the shape of
the data set is normal”. Also remember that when you do not hav the
standard deviation the sample or population are said to be unpooled
versus pooled, which is when you have the standard deviation of the
population.
13) Remember that a correlation does not necessarily
imply a real association between two variables. Remember correlation
is not causation. Conversely, the absence of a strong correlation does
not mean there is no relationship (it might not be linear).
14)
Be able to use a residual plot to help determine if a linear model for
a data set is appropriate. Be able to explain your reasoning.
15) Be able to determine in context, the slope, y-intercept, of a least squares regression line.
16) Be able to read and interpret a computer regression output.
17) Know the definition of: simple random sample SRS.
18) Know what blinding and confounding variables are:
19) Understand the meaning of Simpson's Paradox
20) Know the explanation behind Hawthorne's effect
21) Understand the aspects of the Rosenthal effect and its impact on expected outcomes.
22) Know the difference between randomization and blocking.
23) Know how to create a simulation for a probability problem.
24) Be clear on the distinction between independent events and mutually exclusive events.
25) Know why can’t mutually exclusive events be independent.
26) Be able to find the mean and standard deviation of a discrete random variable.
27) Recognize binomial and geometric situations.
28) Never forget hypothesis are always about parameters, never about statistics.
29) Any inference procedure involves four steps.
30) Know the difference between type I & type II errors.
31) Know how to construct a confidence interval and interpret what a confidence interval means.
32) Be aware of the scale used in creating graphs.
33) Know the difference between descriptive versus inferential statistics.
34) Know the meaning of statistics.
35) Quantitative versus qualitative statistics.
36) Know the process involved when collecting data.
37) Know your 5 number summary and how it relates to the box plot.
38) What you need to know about one-variable data analysis
39) Shape of a distribution
40) Dotplot
41) Stemplot
42) Histogram
43) Measures of center
44) Measures of spread
45) 5-number summary
46) boxplot
47) z-score
48) density curve
49) normal distribution
50) the empirical rule
51) Chebyshev’s rule
52) What you need to know about two-variable data analysis
53) Scatterplots
54) Lines of best fit
55) The correlation coefficient
56) Least squares regression line
57) Coefficient of determination
58) Residuals
59) Outliers and influential points
60) Transformation to achieve linearity
61) You need to know about the design of a study, sampling, surveys, and experiments
62) Samples and sampling
63) Surveys
64) Sampling bias
65) Experiments and observational studies
66) Statistical significance
67) Completely randomized design
68) Blocking
What you need to know about random variables and probability
69) Probability
70) Random variables
71) Discrete random variables
72) Continuous random variables
73) Probability distributions
74) Normal probability
75) Simulation
76) Transforming and combining random variables
What you need to know about binomial distributions, geometric distributions, and sampling distributions
77) Binomial distribution
78) Normal approximation to the binomial
79) Geometric distribution
80) Sampling distribution
81) Central limit theorem
What you need to know about confidence interval and inference
82) Estimation
83) Confidence intervals
84) t-procedures
85) choosing a sample size for a confidence interval
86) p-value
87) statistical significance
88) hypothesis testing procedure
89) errors in hypothesis testing
90) power of test (not likely on the AP exam)
What you need to know about inference for means and proportions
91) The logic of hypothesis testing
92) Z=procedures and t procedures
93) Inference for a population mean
94) Inference for the difference between two population means
95) Inference for a population proportion
96) Inference for the difference between two population proportions
What you need to know about the inference for regression
97) Simple linear regression
98) Significance test for the slope of a regression line
99) Confidence interval for the slope of a regression line
100) Interference for regression using technology
What you need to know for categorical data: chi-square
101) Chi-square goodness of fit test
102) Chi-square test for independence
103) Chi-square test for homogeneity of proportions (populations)
104) X^2 versus z^2
