✍️ 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
June 3, 2020
Focus on the big picture in terms of what context the variables exist in.
A random variable takes numerical values that describe the outcomes of a chance process. The probability distribution of a random variable gives its possible values and their probabilities. To assign labels to random variables, we use capital letters (X or Y).
A continuous random variable can take any value in an interval on the number line so this includes whole numbers and decimals for value of X. Generally, you use a density curve to find the probability of a continuous variable and the probability usually applies to an interval rather than individual values.
A discrete random variable X takes a fixed set of possible values with gaps between them (cannot include decimals so, whole numbers only). *For a probability distribution to be valid, each probability must be between 0 and 1, inclusive. Also, the sum of the probabilities must add to 1.
When calculating probability for discrete random variables, always think about whether you should include the boundary value in your calculations. Make sure you understand how to calculate the probability of a discrete random variable given P(Xn) and P(X=n). The wording is usually confusing so draw yourself a mini probability distribution chart to figure out whether you should/shouldn’t include the boundary value. This will help you when there are phrases like at least, no more than, greater than, etc.
You need to know how to represent a discrete random variable as a histogram or in a table. For the histogram, use the discrete random variable as the x axis values and the probabilities for the y axis.
When describing the shape of a discrete random variable, talk about whether the graph is roughly symmetric, double/single peaked, and right/left skewed. Don’t forget to mention the center (mean) and measure of variability (standard deviation). These interpretations will allow you to make conclusions.
🎥Watch: AP Stats - Probability: Random Variables, Binomial/Geometric Distributions
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