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AP Statistics

AP Statistics is a college-level course that introduces students to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. The course covers topics such as experimental design, probability, statistical inference, and regression analysis. Students will learn how to use statistical to analyze data and communicate their findings effectively. By the end of the course, students will be prepared to take the AP Statistics exam and have a solid in statistical analysis.

Tentative schedule: 32 classes: 16 Prof Lectures+ 16 TA sessions, 2 Free exams

Source

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Unit 1: Exploring One-Variable Data

  • Variation in categorical and quantitative variables

  • Representing data using tables or graphs

  • Calculating and interpreting statistics

  • Describing and comparing distributions of data

  • The normal distribution

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Unit 2: Exploring Two-Variable Data

  • Comparing representations of 2 categorical variables

  • Calculating statistics for 2 categorical variables

  • Representing bivariate quantitative data using scatter plots

  • Describing associations in bivariate data and interpreting correlation

  • Linear regression models

  • Residuals and residual plots

  • Departures from linearity

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Unit 3: Collecting Data

  • Planning a study

  • Sampling methods

  • Sources of bias in sampling methods

  • Designing an experiment

  • Interpreting the results of an experiment

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Unit 4: Probability, Random Variables, and Probability Distributions

  • Using simulation to estimate probabilities

  • Calculating the probability of a random event

  • Random variables and probability distributions

  • The binomial distribution

  • The geometric distribution

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Unit 5: Sampling Distributions

  • Variation in statistics for samples collected from the same population

  • The central limit theorem

  • Biased and unbiased point estimates

  • Sampling distributions for sample proportions

  • Sampling distributions for sample means

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Unit 6: Inference for Categorical Data: Proportions

  • Constructing and interpreting a confidence interval for a population proportion

  • Setting up and carrying out a test for a population proportion

  • Interpreting a p-value and justifying a claim about a population proportion

  • Type I and Type II errors in significance testing

  • Confidence intervals and tests for the difference of 2 proportions

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Unit 7: Inference for Quantitative Data: Means

  • Constructing and interpreting a confidence interval for a population mean

  • Setting up and carrying out a test for a population mean

  • Interpreting a p-value and justifying a claim about a population mean

  • Confidence intervals and tests for the difference of 2 population means

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Unit 8: Inference for Categorical Data: Chi-Square

  • The chi-square test for goodness of fit

  • The chi-square test for homogeneity

  • The chi-square test for independence

  • Selecting an appropriate inference procedure for categorical data

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Unit 9: Inference for Quantitative Data: Slopes

  • Confidence intervals for the slope of a regression model

  • Setting up and carrying out a test for the slope of a regression model

  • Selecting an appropriate inference procedure

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