In this unit, students will be introduced to the basic principles of descriptive statistics. They will expand their knowledge of representing data to include the use of boxplots and histograms. Students will identify and write statistical questions by understanding that a statistical question anticipates variability in its answers. They will describe numeric datasets using measures of center and variability so that they can later analyze and interpret datasets.
Key Concepts:
 A statistical question anticipates variability in the data related to the question and accounts for it in the answers.
 Displaying data using a boxplot or histogram helps to describe the "shape" of the data and give a sense of center and spread.
 The set of numerical values in a data set can be summarized by a measure of center (median and arithmetic mean).
 The variation (or spread) in a data set can be described by the mean absolute deviation, which describes how far data points are from the arithmetic mean.
Prior Knowledge Needed:
 Represent and interpret data; Grade 4, Unit 9; 4.MD.B.4
 Solving problems with fractional quantities, Grade 5, Unit 12, 5.MD.B.2
Units on the Horizon:
 Analyzing data (Grade 6, Unit 13)
 Sampling, inferences, and comparing populations (Grade 7, 10)
Lessons

Lesson objective: Understand that a statistical question anticipates variability in the data related to the question and accounts for it in the answers. Students bring prior knowledge of data displays from 4.MD.B.4. This prior knowledge is extended to u...

Lesson objective: Understand that a graph can help us describe the center, shape, and spread of a dataset. Students bring prior knowledge of representing and interpreting data from 4.MD.B.4. This prior knowledge is extended to summarizing data displays ...

Lesson objective: Find the center, spread, and shape of a graph. This lesson helps to build fluency with describing the center, spread, and shape of a dataset. Dot plots are used here because they allow students to visualize a dataset. This work develop...

Lesson objective: Understand that the arithmetic mean is a balance point of all the data. Students bring prior knowledge of bar graphs from 4.MD.B.4. This prior knowledge is extended to calculating measures of central tendency as students use a bar grap...

Lesson objective: Compute the mean, median, and mode of a dataset. This lesson helps to build procedural skill with calculating measures of center. Numeric datasets are used here because they support student focus on computation. This work develops stud...

Lesson objective: Calculate the mean, median, and mode using dot plots and bar charts. This lesson provides an opportunity for students to apply their knowledge and understanding of measures of central tendency to a reallife situation. Students are ask...

Lesson objective: Understand that the mean absolute deviation is the mean distance from each data point in a given set to the mean of the data set. Students bring prior knowledge of calculating the mean from 6.SP.3. This prior knowledge is extended to m...

Lesson objective: Compute the mean absolute deviation of a dataset. This lesson helps to build procedural skill with calculating mean absolute deviation. A table is used here because it supports organization of computations. This work develops students'...

Lesson objective: Compute the five number summary, range, and interquartile range. This lesson helps to build procedural skill with calculating measures of variability. A numeric dataset is used here because it supports calculating the five number summa...

Lesson objective: Use the Mean Absolute Deviation to compare two datasets. This lesson provides an opportunity for students to apply their knowledge and understanding of mean absolute deviation to a reallife situation. Students are asked to determine i...