Unit 8: Data Sets, Distributions, and Sampling

Students explore key statistical concepts including dot plots, histograms, measures of center and variability, sampling, and probability.

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Unit

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Concept

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What this unit covers

A Dot Plots and Histograms · 3 lessons

Goals

• Describe a distribution represented by a dot plot, including informal observations about its center and spread.
• Interpret a histogram to answer statistical questions about a data set.

Lessons

1. Representing Data
2. Using Dot Plots to Answer Statistical Questions
3. Interpreting Histograms

B Measures of Center and Variability · 4 lessons

Goals

• Calculate and interpret the mean and mean absolute deviation (MAD) of a data set.
• Calculate and interpret the median and interquartile range (IQR) of a data set.

Lessons

1. The Mean
2. Variability and MAD
3. The Median
4. Box Plots and Interquartile Range

C Sampling · 5 lessons

Goals

• Describe methods to obtain a random sample from a population, and explain why it is representative of the population.
• Explain why samples are necessary and describe a sample and population for a given statistical question.
• Use the mean of a random sample to make inferences about the population.

Lessons

1. Larger Populations
2. What Makes a Good Sample?
3. Sampling in a Fair Way
4. Estimating Population Measures of Center
5. More about Sampling Variability (Optional)

D Probability · 5 lessons

Goals

• Describe a multi-step experiment that could be used to simulate a compound event in a real-world situation, and justify that it represents the situation.
• Interpret or create a list, table, or tree diagram that represents the sample space of a compound event, and use the sample space to write the probability for an event.
• Use the results from a repeated experiment to estimate the probability of an event, and justify the estimate.
• Use the sample space to determine the probability of an event, and express it as a fraction, decimal, or percentage.

Lessons

1. What Are Probabilities?
2. Estimating Probabilities through Repeated Experiments
3. Keeping Track of All Possible Outcomes
4. Multi-step Experiments
5. Designing Simulations