AP Stats - Trimester 2 Learning Targets
Unit 1 – Organizing Data
Students will be able to say “I can…”
Tell the difference between categorical and quantitative variables.
Appropriately display categorical variables using pie charts and bar graphs and
understand how graphs can be use to mislead.
Determine whether frequencies or relative frequencies are more informative in
displaying categorical data.
Identify several ways to display quantitative data distributions including dotplots,
stemplots, histograms, ogives, and time plots
Make a dotplot, stemplot, histogram, ogive, and time plot by hand.
Make a histogram on the TI-84 Graphing calculator .
Describe a quantitative display using a verbal description of shape, center, spread
and outliers. (Symmetry/Skewness, peaks, etc.)
Calculate & determine which measure of center is most appropriate for a
distribution.( mean, median, mode) based on the shape of the distribution.
Calculate & determine which measure of spread is most appropriate for a
distribution (variance, standard deviation, 5 # summary, range, IQR)
Calculate & justify whether or not a data point is an outlier.
Explain how an outlier influences the measures of center and spread.
Make a boxplot by hand and on the TI-84 graphing calculator.
Make comparative data displays, (side-by-side boxplots, back-to-back stemplots)
Use descriptions of shape, center and spread to communicate the similarities and
differences between distributions.
Clearly explain the effects of linear transformations on a distribution in terms of
center and spread.
Explain what a density curve is.
Explain how density curves are used as idealized descriptions for distributions.
Explain the fundamental principles of a density curve.
Explain how the shape of a density curve affects the measures of center.
Identify the Normal Distribution as one type of density curve.
Identify key features of the Normal Distribution (pts. of inflection, tails, etc.)
Use the 68-95-99.7 rule to describe the spread of a Normal Distribution.
Transform a Normal Distribution to a Standard Normal Distribution using z-scores.
Find the area under the curve for any region using the z-tables.
Find z-scores for a given percentile using z-tables.
Use my TI Graphing calculator to find areas under the curve (normalcdf)
Use my TI Graphing calculator to find z-scores for percentiles (invnorm)
Communicate mathematically the process for finding areas under the curve.
Assess the normality of a distribution using the 68-95-99.7 rule, as well as by using a
normal probability plot.
Make and interpret a normal probability plot on the TI Graphing calculator.
Identify explanatory/response variables, and when it doesn’t matter.
Make a scatterplot using the TI Graphing calculator and Microsoft Excel(optional
Microsoft or Fathom).
Describe a scatterplot as an association between two variables.
Describe a scatterplot in terms of strength, form, and direction.
Resources
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Calculate and interpret the correlation coefficient (r) for a linear association.
Calculate and interpret the coefficient of determination (r2) for a linear association.
Identify facts about correlation and its limitations.
Calculate the least-squares regression line for a linear association.
Clearly explain a and b (bo and b1) context.
Can use a computer regression output to identify various features and measures of
linear regression.
Communicate what r2 means in context.
Use a least-squares regression line to make predictions/estimates.
Calculate a residual and explain what it is.
Make and interpret a residuals plot.
Assess the linearity of the data using a residuals plot.
Differentiate between a regression outlier and an influential observation.
Explain how an influential observation may affect a least-squares regression line.
Transform non-linear bivariate data into linear bivariate data by using logarithms to
scale the y-axis or both axes on a scatterplot.
Use logarithmic transformations to transform a scaled linear regression model into
an exponential or power regression model.
Use exponential regression and power regression models directly to model nonlinear associations using the TI Graphing calculator.
Explain several cautions with regression such as extrapolation, lurking variables,
averaged data, etc…
Distinguish between Causation, Confounding, and Common Response lurking
variables.
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Unit 2 – Experimental Design
Students will be able to say “I can…”
Distinguish between an observational study and an experiment.
Distinguish between populations/samples and census/sampling
Identify sources of bias in collecting survey data. (voluntary, convenience, response,
wording…)
Properly assign digits and use a table of digits to perform a SRS.
Use my TI Graphing calculator to generate random digits and perform a SRS.
Identify “other” sampling designs (probability, stratified, multistage,…)
Identify and explain how undercoverage and nonresponse affect data collection.
Utilize the basic structure of an experiment (units, subjects, treatments, response
variable, factors and levels) to create simple experiments.
Can identify the basic principles of experimentation: Randomization, Control,
Replication, and Blocking
Understand the role of a control group for comparative designs, as well as the
placebo effect.
Can explain when an experiment should be single-blind or double-blind.
Can design a completely randomized design experiment.
Can explain why blocking is a useful way to reduce variability.
Can explain how a matched pairs design works.
Understand why simulations are useful models for many situations.
Set up and perform a simulation using a random digit table.
Understand the limitations of simulation data.
Resources
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Can explain why results obtained for one group may not be generalized to all
groups.
Unit 3 – Probability
Students will be able to say “I can…”
Identify a sample space for an activity.
Solve basic problems that involve counting techniques, including the multiplication
principle, permutations, and combinations.
Find the probability of a single event.
Create and use a probability model.
Find the probability of multiple events (with and without replication)
Use a tree diagram to model multiple events.
Apply the multiplication principle to probabilities of outcomes, multiple events
Use basic probability rules. (including the union rule)
Use Venn diagrams as a probability model.
Can communicate effectively using correct probability notation.
Can justify when events are dependent/independent.
Can explain why conditional events limit the sample space.
Can find conditional probabilities using formulas and diagrams.
Distinguish between discrete and random variables.
Display discrete probability distributions with a probability histogram.
Use the TI Graphing calculator to make a probability histogram.
Display continuous probability distributions with a density curve.
Understand why P(x<3) = P(x≤3) is the same for continuous probability distributions,
but not for discrete probability distributions.
Find probabilities using the Normal Distribution and z-scores.
Calculate the mean(expected value) of a discrete random probability distribution.
Calculate the Variance and Standard Deviation for a discrete random probability
distribution.
Can explain the Law of Large numbers
Can apply rules for combining means of random variables.
Can apply rules for combining variances of independent and dependent random
variables.
Can find the mean of a random variable with a linear transformation.
Understand why linear combinations of normal distributions are normal.
Recognize a Bernoulli trial.
Recognize a binomial setting.
Utilize the formula for a single binomial probability event.
Find the probability of a single binomial event.
Find the probability of a cumulative binomial event.
Find the mean and standard deviation of a binomial probability distribution.
Utilize the TI Graphing calculator to calculate binomial probabilities. (binompdf/cdf)
Recognize when I am able to use the normal approximation to the binomial
distribution. “Rules of thumb”
Simulate binomial and geometric settings using a table of random digits.
Find the probability of 1st success on the nth trial for geometric settings.
Ch 5
Resources
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Worksheets )
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Find the probability it takes more than n trials for 1st success for geometric settings.
Find the mean and standard deviation for geometric probability distributions.
Use the TI Graphing calculator to calculate geometric probabilities (geompdf/cdf)
Find the probability of 1st success on the nth trial for geometric settings.
Find the probability it takes more than n trials for 1st success for geometric settings.
Find the mean and standard deviation for geometric probability distributions.
Use the TI Graphing calculator to calculate geometric probabilities (geompdf/cdf)
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