AP Statistics Learning Target Outline, 7-18-14

AP Statistics
The entire class is built on the idea that if you want to complete a statistics problem you must
think like a statistician. According to LMU faculty, this means going through the following
Stats Method
Ask a QUESTION about a population
ANALYZE the data using appropriate numerical or graphical methods
INTERPRET results in the context of the question
Standards are from the AP Statistics Course Description pdf pages 11-14.
Standards are from the California Common Core State Standards pages 121-122.
Unit 1: Stats Method (introduction to)
• Simulation IIIA5
o Tables of random digits
o RandInt (83-84) & rand (89)
o Suggestion: use simulation to solve probability problems similar to topics they
have seen in prior classes.
• Properties of Random Variables IIIA6
o Linear Combinations of RVs IIB
o Focus on Discrete RVs: Binomial & Geometric RVs IIIA4 3.0
• Categorical Data IE
o Definition (v. Quantitative Data)
o Graphical representations of Categorical Data
Unit 2: Collect Data
• Sampling v. Experiments v. Observational Studies IIA
o Identifying when to use each
o Pros & Cons of each in a particular context
• Sampling Methods IIB
o Unbiased methods: SRS, Stratified RS, Cluster RS
o Biased methods: Convenience, Voluntary Response
• Experimental Design IIC
o Basic design, including proper randomization.
o Blocking
• Sampling Distributions IIID 15.0, 16.0
o Name different types of RVs with their graphical representations where
appropriate 7.0
o Conditions for each different RV (begin inference cheat sheet)
o Formulas for µx and σx. 5.0, 6.0
Unit 3: Normal Distribution IIIC 4.0, 7.0, 8.0
© ESHS (Munger), 7-18-14
Note: this is a continuation of the Collect Data and Sampling Distributions topic with special
focus on what is arguably the most important sampling distribution in this class.
• Graphing Normal Distributions
o Standard Normal N(0, 1)
o Graphing N(µ,σ)
o Use the z-score to transform N(µ,σ) to N(0, 1)
• Areas & Percentiles
o Explain why P(x < k) is the area it is on a graph (link to geometric probability:
since this is a sampling distribution the denominator is one)
o Given a P(x < k) or similar statement, students correctly shade a graph
o Compute P(x < k) or similar statements using both calculator and z-score/table
o Given P(x < k) = m, correctly use calculator (invNorm) or z-score/table to find k.
• 65-95-99.7 rule
o Happens after above so that students understand it is a special case
o Present as a shortcut
Unit 4: Analyze and Interpret Univariate Data
Note: this is a long unit and 30%-40% of the AP exam is about inference. Schedule/emphasize
• Construct, Interpret & Compare Graphical Displays of Univariate Data IABC
o Given a set of data compute the numerical descriptors of center (mean, median,
mode), spread (standard deviation, IQR, range) and shape (outliers) 10.0, 11.0
o When to use mean/standard deviation v. median/IQR
o Construct stem & leaf, boxplots, histograms and how to decide which is
appropriate in a given context 14.0
o Given a graph, describe its SOCS (shape, outliers, center spread).
o Given two graphs, compare/contrast them in context (SOCS in context)
• Inference: Confidence Intervals IVA
o Work on inference cheat sheet
• Inference: Hypothesis Tests IVB
o Complete inference cheat sheet
Unit 5: Analyze & Interpret Bivariate Data
• Create & Interpret Scatterplots ID1
o Form, direction, strength
o Causation v. Correlation
• Compute LSRL equation ID2,3
o Compute and interpret correlation coefficient and coefficient of determination
o Explain slope and y-intercept of LSRL equation in context
o Use LSRL to extrapolate; constraints on extrapolation
• Residuals ID4
o Show residuals on scatterplot and residual graph
o Create & interpret residual plot
• Transformations to include linearity: logarithmic and power transformations ID5 (this is a
topic I skipped this year since it is rarely on the exam)
© ESHS (Munger), 7-18-14
Unit 6: Classical Probability
• Interpreting probability IIIA1 3.0
• Law of Large Numbers IIIA2
• Rules: addition rule, multiplication rule, conditional probability and independence IIIA3
1.0, 2.0
© ESHS (Munger), 7-18-14