✍️ 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)
Is AP Statistics Hard? Is AP Statistics Worth Taking?
Best Quizlet Decks for AP Statistics
⏱️ 2 min read
June 3, 2020
This section describes how we organize and display quantitative data. The frequency table is a bit complicated for quantitative data, especially if we deal with vast amounts of data. The good news is that AP doesn’t require you to make one, so we will skip this one. Just know that many computer programs, including our mighty Excel, and your TI series calculator can make it in seconds. The main displays we will discuss are histograms, polygons, ogive, stem-and-leaf plots, and dot-plot.
The histogram displays the data by using vertical bars or bins. The bins are of equal width, and unlike the bar graphs, there is no space between them. If there is a space, then that indicates an actual gap in data with no values. The height of bins represents the frequencies of the classes. Remember, always check the quantitative data assumption to verify the right graph or display.
The frequency polygon displays the distribution of quantitative data by using lines and connecting points at the midpoints of the classes for each bin.
The Ogive uses cumulative frequencies for the classes to display the distribution of quantitative variables. The cumulative frequency adds the frequencies by each class. Ogives help us determine the position of data to see how many values are below or above a certain value.
Source: Allan G. Bluman. Elementary Statistics. A Step By Step Approach. McGraw Hill. Eighth Edition. 2009
These graphs are very common in research and in the corporate world.
The Stem-and-leaf plots are similar to histograms, but they reveal the individual values in the display. This is the advantage of stemplots compared with other displays. Since histogram uses grouped data, we miss the individuals in the bins. Like histograms, stemplots can help us analyze data. Whenever you make a stemplot, don’t forget to provide the key to help the reader how to read it.
Dot-Plots are more similar to stemplots. Moreover, if you forget how to write the numbers 😉 then this is the best display for you. It is simple, use dots instead of digits to construct it. Dot-plots are the first choice when we deal with a small set of data.
Tips. Turn the stem-and-leaf plot on its side to see any unusual things that data will have for you to be aware of it.
🎥Watch: AP Stats - Displaying Quantitative Data with Graphs
Relative Frequency Polygon
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