✍️ 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 4, 2020
A normal model is unimodal (one mound, bell-shaped) and symmetric. The median and mean are equal to each other.
When asked to describe a normal model, talk about the center, shape, and spread.
Furthermore, a model (such as a sampling distribution) is approximately normal if the following conditions are met for the following types of data:
The area underneath any density curve (including the normal curve) is equal to 1, or 100% of the data. There is 50% of the data on each size of the mean.
68% of the data is within 1 standard deviation of the mean. (34% on each side)
95% of the data is within 2 standard deviations of the mean (an additional 13.5% on each side)
99.7% of the data is within 3 standard deviations of the mean (an additional 2.35% on each side)
🎥Watch: AP Stats - Normal Distributions
A z-score indicates how many standard deviations above or below the mean a piece of data is. If a z-score is -2, it is 2 standard deviations below the mean. If a z-score is 1, it is 1 standard deviation above the mean. When asked to interpret a z-score, it is imperative that you indicate the direction (positive or negative). The magnitude of the z-scores is also important, especially when making inferences.
Z-scores are often used to compare scores regarding two sets of data, like SAT and ACT scores. The z-scores are resistant to units. Therefore the units do not matter and the z-scores remain the same if we convert the units. Think of z-scores as a way of comparing apples 🍎 to oranges 🍊.
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