AP®︎ Statistics Unit 3: Notes & Study Guide
Prepare for your quiz, test, or the AP exam with a comprehensive review on Unit 3 of AP Statistics – Inference for Categorical Data: Proportions.
Unit 3: Inference for Categorical Data: Proportions
How to build confidence intervals and carry out significance tests for one and two population proportions, including conditions, errors, and power.
Begin with Topic 3.1: EstimatorsTo review Unit 3, go through each of the 15 topics below.
Everything you actually need to know for your Unit 3 test, pulled directly from the AP® Statistics curriculum.
Sampling Distributions for Sample Proportions
Sampling Distributions for Sample Proportions
- What the Sampling Distribution of p̂ Is
- Center, Spread, and Shape of the Sampling Distribution
- Conditions to Use the Model
Constructing a Confidence Interval for a Population Proportion
Constructing a Confidence Interval for a Population Proportion
- What a Confidence Interval for a Population Proportion Does
- Conditions for a One-Sample Proportion Interval
- Building the Interval
Justifying a Claim Based on a Confidence Interval for a Population Proportion
Justifying a Claim Based on a Confidence Interval for a Population Proportion
- What a Confidence Interval for a Population Proportion Means
- Using the Interval to Judge a Claim
- What the Interval Can and Cannot Establish
Setting Up a Test for a Population Proportion
Setting Up a Test for a Population Proportion
- What a One-Sample z-Test for a Population Proportion Is
- Writing the Parameter and Hypotheses
- How to Set Up the Test
P-Values
P-Values
- What a P-Value Is
- Finding the P-Value from the Null Distribution
- P-Values from Simulation
Carrying Out a Test for a Population Proportion
Carrying Out a Test for a Population Proportion
- What a One-Sample z-Test for a Population Proportion Does
- Conditions and the Null Distribution
- Carrying Out the Test
Potential Errors When Performing Tests
Potential Errors When Performing Tests
- What Type I Error, Type II Error, and Power Are
- Probabilities Attached to the Errors
- How to Identify and Write Each Error in Context
Sampling Distributions for the Difference Between Sample Proportions
Sampling Distributions for the Difference Between Sample Proportions
- What the Sampling Distribution of p̂₁ − p̂₂ Is
- Center, Spread, and Shape
- Conditions for Using the Normal Model
Constructing a Confidence Interval for the Difference Between Two Population Proportions
Constructing a Confidence Interval for the Difference Between Two Population Proportions
- What a Two-Proportion z-Interval Estimates
- Conditions for a Two-Proportion z-Interval
- Building the Interval
Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Proportions
Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Proportions
- What a Confidence Interval for \(p_1-p_2\) Means
- Interpreting the Interval and the Confidence Level
- Using the Interval to Judge Claims
Setting Up a Test for the Difference Between Two Population Proportions
Setting Up a Test for the Difference Between Two Population Proportions
- What a Test for Two Population Proportions Is
- Hypotheses for \(p_1 - p_2\)
- Why Pooling Happens Under the Null
Carrying Out a Test for the Difference Between Two Population Proportions
Carrying Out a Test for the Difference Between Two Population Proportions
- What a Two-Sample z-Test for Proportions Does
- Conditions and Why the Test Pools
- Carrying Out the Test
Setting Up a Chi-Square Test for Homogeneity or Independence
Setting Up a Chi-Square Test for Homogeneity or Independence
- What Chi-Square Tests in a Two-Way Table
- Choosing Between Homogeneity and Independence
- Hypotheses and Expected Counts
Carrying Out a Chi-Square Test for Homogeneity or Independence
Carrying Out a Chi-Square Test for Homogeneity or Independence
- What a Chi-Square Test for Homogeneity or Independence Does
- Expected Counts and the Chi-Square Statistic
- Carrying Out the Test
Notes
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