Unit 7
INVESTIGATING DATA
UNIT DESCRIPTION
In this unit, students will engage in the 4-step statistical problem-solving process to collect, analyze, and interpret univariate quantitative data to answer statistical investigative questions that compare groups to solve real-life problems. Students will represent bivariate data on a scatter plot and fit a function to the data to answer statistical questions and solve real-life problems. When engaging with the content included in this unit, students should solve problems using the four-step statistical problem-solving process. The Framework for Statistical Reasoning should be used in all learning plans to guide learners through the sense-making process, ultimately leading to the goal of statistical literacy in all grade levels and courses. Reasoning with statistics provides a context that necessitates the learning and application of a variety of mathematical concepts.
This unit builds upon students’ prior experiences with data, providing students with opportunities to analyze and interpret real-world situations and experiences by using various mathematical representations. Distinctions will be made between univariate and bivariate variables as students identify the data needed to answer statistical investigative questions. Students will revisit and learn techniques for representing and analyzing quantitative data by hand and using technology. Students will use the shapes of distributions to determine appropriate summary statistics and to compare groups of interest. Students will explore patterns of association between two quantitative variables and model data with linear functions where appropriate. An emphasis on making predictions and interpreting model parameters will allow students to connect data to real-world situations.
This unit builds upon students’ prior experiences with data, providing students with opportunities to analyze and interpret real-world situations and experiences by using various mathematical representations. Distinctions will be made between univariate and bivariate variables as students identify the data needed to answer statistical investigative questions. Students will revisit and learn techniques for representing and analyzing quantitative data by hand and using technology. Students will use the shapes of distributions to determine appropriate summary statistics and to compare groups of interest. Students will explore patterns of association between two quantitative variables and model data with linear functions where appropriate. An emphasis on making predictions and interpreting model parameters will allow students to connect data to real-world situations.
LEARNING TARGETS
- I can calculate and interpret measures of center (mean, median, and mode).
- I can calculate and interpret measures of spread (range, IQR, and SD).
- I can interpret differences in shape center and variability.
- I can calculate and determine the outliers and analyze their effects on the data set (1.5 IQR rule).
- I can describe the direction, strength, and form (linear, non-linear) of the association between two quantitative variables.
- I can identify a scatter plot as linear, quadratic, or exponential.
- I can interpret the slope and intercept a linear model.
- I can calculate the line of best fit and correlation coefficient using technology.
- I can distinguish between correlation and causation.
TEXTBOOK CONNECTION
Module 12
Lesson 1- Measures of Center: Students represent sets of data using measures of center and percentiles.
Lesson 4- Measures of Spread: Students represent sets of data using measures of spread.
Lesson 5- Distributions of Data: Students analyze the shapes of distributions to determine appropriate statistics and identify extreme data points.
Lesson 6- Comparing Sets of Data: Students use statistics appropriate to the shapes of the distributions to compare the measures of center and spread of two data sets.
Module 5
Lesson 3- Scatter Plots and Lines of Fit: Students use scatter plots to make and evaluate predictions.
Lesson 4- Correlation and Causation: Students determine whether a situation illustrates correlation or causation.
Lesson 5- Linear Regression: Students use best-fit lines and correlation coefficients to determine how well linear functions fit sets of data
Lesson 1- Measures of Center: Students represent sets of data using measures of center and percentiles.
Lesson 4- Measures of Spread: Students represent sets of data using measures of spread.
Lesson 5- Distributions of Data: Students analyze the shapes of distributions to determine appropriate statistics and identify extreme data points.
Lesson 6- Comparing Sets of Data: Students use statistics appropriate to the shapes of the distributions to compare the measures of center and spread of two data sets.
Module 5
Lesson 3- Scatter Plots and Lines of Fit: Students use scatter plots to make and evaluate predictions.
Lesson 4- Correlation and Causation: Students determine whether a situation illustrates correlation or causation.
Lesson 5- Linear Regression: Students use best-fit lines and correlation coefficients to determine how well linear functions fit sets of data
IXL SKILLS
Measures of Center:
- MM.2
- MM.3
- MM.4, MM.5, MM.9
- MM.8, MM.10
- MM.11-15
- MM.16
- MM.6
