AP+Statistics

Links Legend: [T] - Teacher Created [S] - Student Created [E] - External Link

1. Descriptive Statistics
Stemplots and Modified Boxplots (T) Normal Probability Plots (T) || Standard Deviation (T) Normal Distributions (T) Z-Scores (T) || Linear Association (T) Regression Lines (T) Residuals and LSRL (T) Residual Plots (T) ||
 * 1) Students will be able to create and interpret graphical displays of univariate data (dotplots, stemplots, histograms, boxplots and cumulative frequency plots)
 * center, unusual features, shape and spread and will be specific in the descriptions of the data described by these graphs. ||= Mean and Median (T)
 * 2) Students will be able to describe distributions of univariate data
 * calculating/interpreting mean, median, range, IQR, standard deviation, quartiles, percentiles, z-scores and the effects of changing units on summary measures. || IQR and Boxplots (T)
 * 3) Students will be able to compare distributions of univariate data with dual dotplots, back to back stemplots and/or parallel boxplots.
 * compare center, spread, clusters, gaps, outliers and shape. || Podcast ||
 * 4) Students will be able to explore patterns in bivariate data.
 * scatterplots, correlation, linearity, LSRL, residual plots, outliers, influential points and transformations to linearity via logarithmic/power rules. || Scatterplots (T)
 * 5) Students will be able to explore categorical data:
 * frequency tables, bar charts, marginal/joint frequencies, and conditional relative frequencies. || Podcast ||

2. Probability

 * 1) Students will be able to use probability to describe and predict random phenomena.
 * interpret probability in the long run, law of large numbers, addition rule, multiplication rule, conditional probability, independence, discrete random variables, binomial and geometric distributions, simulations of random events, expected value and standard deviation of random variables. || Podcast ||
 * 2) Students will be able to combine independent random variables
 * independence vs. dependence and mean and standard deviation for sums/differences of independent random variables. || Podcast ||
 * 3) Students will be able to apply probability concepts to the normal distribution of data.
 * properties of normal distribution, use of tables of normal distribution and the normal distribution as a model of measurements. || Podcast ||
 * 4) Students will be able to apply probability to sampling distributions.
 * via sample proportions, sample means, CLT, 2-sample proportions, 2-sample means, simulations, t-distribution and chi-square distributions. || Podcast ||

3. Designing Studies
[|SRS and Random Number Table (T)] [|Randomization (T)] [|Categorical and Quantitative (T)] ||
 * 1) Students will be able to give an overview of the methods of data collection:
 * census, sample survey, experimental and observational studies. || Podcast ||
 * 2) Students will be able to plan and conduct surveys:
 * describe characteristics of a well-designed, well-conducted study, populations, samples, random selections, bias and sampling methods including simple random sampling, stratified random sample and cluster sampling. || [|Matched Pairs Design #1 (T)]
 * 3) Students will be able to plan and conduct experiments:
 * describe characteristics of a well-designed, well-conducted experiment, treatments, control groups, experimental units, random assignments, replication, bias, confounding variables, randomized designs, block design and matched pair. || Principles of Experimental Design (T) ||
 * 4) Students will be able to generalize results and conclusions that result from studies, experiments and surveys. || Podcast ||

4. Inference/Hypothesis Testing
AP Statistics Review for AP Exam
 * 1) Students will be able to use statistical inference to estimate:
 * population parameters, margins of error, point estimators, confidence intervals and their application to proportions, difference between proportions, means, difference between two means and for slope of LSRL. || Podcast ||
 * 2) Students will be able to use tests of significance:
 * logic of, null/alternative hypotheses, p-values, one and two-sided tests, Type I and II errors, power, tests for a proportion, test for a mean, test for difference between two proportions/means, chi-square test for GOF, homogeneity and independence, and test for slope of LSRL. || Podcast ||