✍️ Free Response Questions (FRQs)
👆 Unit 1 - Exploring One-Variable Data
1.4Representing a Categorical Variable with Graphs
1.5Representing a Quantitative Variable with Graphs
1.6Describing the Distribution of a Quantitative Variable
1.7Summary Statistics for a Quantitative Variable
1.8Graphical Representations of Summary Statistics
1.9Comparing Distributions of a Quantitative Variable
✌️ Unit 2 - Exploring Two-Variable Data
2.0 Unit 2 Overview: Exploring Two-Variable Data
2.1Introducing Statistics: Are Variables Related?
2.2Representing Two Categorical Variables
2.3Statistics for Two Categorical Variables
2.4Representing the Relationship Between Two Quantitative Variables
2.8Least Squares Regression
🔎 Unit 3 - Collecting Data
3.5Introduction to Experimental Design
🎲 Unit 4 - Probability, Random Variables, and Probability Distributions
4.1Introducing Statistics: Random and Non-Random Patterns?
4.7Introduction to Random Variables and Probability Distributions
4.8Mean and Standard Deviation of Random Variables
4.9Combining Random Variables
4.11Parameters for a Binomial Distribution
📊 Unit 5 - Sampling Distributions
5.0Unit 5 Overview: Sampling Distributions
5.1Introducing Statistics: Why Is My Sample Not Like Yours?
5.4Biased and Unbiased Point Estimates
5.6Sampling Distributions for Differences in Sample Proportions
⚖️ Unit 6 - Inference for Categorical Data: Proportions
6.0Unit 6 Overview: Inference for Categorical Data: Proportions
6.1Introducing Statistics: Why Be Normal?
6.2Constructing a Confidence Interval for a Population Proportion
6.3Justifying a Claim Based on a Confidence Interval for a Population Proportion
6.4Setting Up a Test for a Population Proportion
6.6Concluding a Test for a Population Proportion
6.7Potential Errors When Performing Tests
6.8Confidence Intervals for the Difference of Two Proportions
6.9Justifying a Claim Based on a Confidence Interval for a Difference of Population Proportions
6.10Setting Up a Test for the Difference of Two Population Proportions
😼 Unit 7 - Inference for Qualitative Data: Means
7.1Introducing Statistics: Should I Worry About Error?
7.2Constructing a Confidence Interval for a Population Mean
7.3Justifying a Claim About a Population Mean Based on a Confidence Interval
7.4Setting Up a Test for a Population Mean
7.5Carrying Out a Test for a Population Mean
7.6Confidence Intervals for the Difference of Two Means
7.7Justifying a Claim About the Difference of Two Means Based on a Confidence Interval
7.8Setting Up a Test for the Difference of Two Population Means
7.9Carrying Out a Test for the Difference of Two Population Means
✳️ Unit 8 Inference for Categorical Data: Chi-Square
📈 Unit 9 - Inference for Quantitative Data: Slopes
🧐 Multiple Choice Questions (MCQs)
Best Quizlet Decks for AP Statistics
⏱️ 2 min read
October 28, 2020
5-7% of the test
Roughly 2 to 3 multiple choice questions
After covering single-variable statistics, it’s time to increase the complexity a little bit with two variable statistics! We can deal with two variable statistics in two ways. With categorical variables, we can use two way tables to represent the relationship between two different categories of categorical variables. With quantitative variables, we can show the relationship between these using scatterplots. We will also see whether there is a relationship between two variables in both situations. This will link to later units as well. Our study of two-way tables will link to probability (Unit 4) and Chi-squared tests for homogeneity or independence (Unit 8). Our study of scatterplots will link to regression inference as well (Unit 9).
Three of the College Board's mathematical practices for AP Statistics are used in this unit, which will be outlined below.
Selecting Statistical Methods
This is useful when we decide whether we want to use two-variable statistics methods and the type to use or to use inference techniques learned later on.
Using data analysis, we’ll figure out how to figure out different statistics from two-variable data sets and also find ways to model with them and draw conclusions.
In this unit, we will learn to argue about the strength of how much variables are related to each other, and also the most important sentence of this unit: correlation does not imply causation!
Two Way Tables
Joint Relative Frequencies
Marginal Relative Frequencies
Conditional Relative Frequencies
Side-by-side bar graphs
Segmented bar graphs
Unusual features (gaps, clusters, outliers)
Linear Regression (Least Squares Regression)
r, R^2. and s
Transforming Data Sets
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