**Math Topics**- Common Core
- Initiatives
- Methodology
- Resources
- Projects
- Manipulatives
- Software

**Learning Support**- Standardized Test Prep
- Technology Integration
- Assisting Readers

**Professionalism**- Associations
- Journals
- News
- Professional Development
- Education Standards
- Education Research
- CT4ME Publications

Select the cluster for resources on this page:

**Standards**:

- ID-A.1. Represent data with plots on the real number line (dot plots, histograms, and box plots).
- ID-A.2. Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
- ID-A.3. Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
- ID-A.4. Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.

**Technology-enhanced investigations:**

Mathwords.com: Math Dictionary. Note: Terms followed by an asterisk are defined at MathsIsFun.com. Also see the Stattrek.com Statistics Dictionary. Key vocabulary for this domain. Use with ID-A, ID-B, ID-C:

Alcula.com:

- Online Box Plot Generator. Use with ID-A.1.
- Online Measures of Central Tendency and Dispersion Generator: Find mean, median, mode, standard deviation, quartiles, range, interquartile range, variance, and mean absolute deviation (MAD. Use with ID-A.2.

College Preparatory Math: Student Tutorials: TI-84 Graphing Calculator: Univariate Data: Videos and screen shots illustrating use of the calculator to set up histograms, box plots, comparing two box plots, and summarizing data numerically. Use with ID-A.1.

LearnZillion:

- Lesson set: Represent data with dot plots, histograms, and box plots: Seven video lessons: Create a dot plot, histogram, box plot; understand the advantages of a dot plot, histogram, box plot; choose the appropriate graphical representation. Aligns with ID-A.1.
- Lesson set: Compare center and spread of two or more data sets: Five video lessons: Describe data by using measures of center and spread, distinguish between mean absolute deviation (MAD) and standard deviation, compare two data sets by using measures of center and spread, use the appropriate measure of center and spread when outliers are present or not present. Aligns with ID-A.2.
- Lesson set: Compare center and spread of two or more data sets: Seven video lessons: Identify a data set’s shape using modes and symmetry, find center using mean and median, calculate and compare the spread of data sets by identifying the interquartile range (IQR), calculate and compare the spread of data sets by finding the standard deviation; compare histograms, box plots, and dot plots using center and spread. Aligns with ID-A.2.
- Lesson set: Interpret differences in shape, center, and spread of data sets in context: Five video lessons: Understand the process of making inferences; draw inferences from a histogram, by analyzing data, from data in a box plot, and from data in a dot plot. Aligns with ID-A.3.
- Lesson set: Compare center and spread of two or more data sets, accounting for outliers: Five video lessons: Identify outliers using knowledge of quartiles, represent outliers in a box plot, understand the effect of outliers on shape, interpreting histograms, compare box plots. Aligns with ID-A.3.
- Lesson set: Use mean and standard deviation to fit a data set to a normal distribution when appropriate; estimate population percentages using tools: Six video lessons: Identify situations that fit a normal distribution, estimate what percentage of a population falls within a certain range by applying the Empirical Rule, find population percentages by extending the Empirical Rule, find z-scores; find population percentages by using z-scores and tables and by using technology. Aligns with ID-A.4.
- Lesson set: Use mean and standard deviation to fit to normal distribution and estimate population percentages when appropriate; use tools to estimate areas under the normal curve: video lessons: Check whether normal model is appropriate for a data set, apply the Empirical Rule to determine if a distribution is normal, model a data set with a normal probability distribution, predict intervals and population percentages using the Empirical Rule, predict population percentages by using a graphing calculator, solve problems involving the normal distribution by using a graphing calculator. Aligns with ID-A.4.

National Library of Virtual Manipulatives: Box Plots and Histograms. Use with ID-A.1.

National Center for Education Statistics: Create a graph

NCTM Illuminations: Advanced Data Grapher: Use this virtual manipulative to analyze data with box plots, bubble graphs, scatterplots, histograms, and stem-and-leaf plots. Use with ID-A.1.

Ohio Resource Center on YouTube: Tutorials for High School Mathematics. Select the following:

- Data Display. Organizing information into tables and graphs with titles, legends, correct units, error bars, and fitting functions.
- Measures of Center and Dispersion

Purple Math:

- Box-and-Whisker Plots: Quartiles, boxes, and whiskers; five number summary of numbers needed to construct the plot; interquartile ranges and outliers.
- Mean, Median, Mode, Range
- Stem-and-Leaf Plots

Shodor Interactivate: Statistics and Probability: Interpreting Categorical and Quantitative Data: Summarize, represent, and interpret data on a single count or measurement variable. A series of nine lessons and 14 activities with virtual manipulatives to investigate concepts such as box plots, histograms, stem-and-leaf plots, measures of center and spread, the Bell curve, univariate and bivariate data, normal distributions, skewed distributions. Aligns with ID-A.

Stat Trek: Tutorials:

- What is a dot plot?; Bar charts and histograms; and Boxplots (aka, Box and Whisker Plots): Each tutorial also includes a video.
- The Mean and the Median: Measures of Central Tendency; and How to Measure Variability in Statistics: This latter tutorial includes range, interquartile range, variance, and standard deviation. Both tutorials include video.
- How to Compare Data Sets: The tutorial also includes a video and addresses center, shape, spread, and unusual features (e.g., outliers).

Thinking Mathematics: Standard Deviation Formula Explained: A YouTube video. Aligns with ID-A.2 and ID-A.4.

Academo.org: Standard Deviation Calculator. After entering the numbers, this tool shows the number of numbers, the mean, variance, and standard deviation. A brief explanation of the standard deviation formula is included. Aligns with ID-A.2 and ID-A.4.

Teaching Channel Video: Statistical Analysis to Rank Baseball Players: This video's lesson objective: Rank the greatest NY Yankee homerun hitters using statistical analysis. Questions for learners to consider are included. Use to address ID-A.1, ID-A.2, and ID-A.3.

McDougal Littell ClassZone, Algebra 2, 2011: Select resources in Ch. 11 Data Analysis and Statistics for interactive practice: Animations (Virtual manipulatives), Games/Vocabulary Flashcards, and PowerPoint lesson examples, and interactive problems within the eWorkbook. See multiple choice below for related sections to use within the eWorkbook. Use with ID-A.

MIT BLOSSOMS: Video lesson with additional teacher and learner resources. Description is from the video summary. Flaws of Averages: "This learning video presents an introduction to the Flaws of Averages using three exciting examples: the “crossing of the river” example, the “cookie” example, and the “dance class” example. Averages are often worthwhile representations of a set of data by a single descriptive number. The objective of this module, however, is to simply point out a few pitfalls that could arise if one is not attentive to details when calculating and interpreting averages." Aligns with ID-A.2.

Wolfram Demonstrations Project: Download the free Wolfram CDF player to interact with the following manipulatives. Note: Within the Wolfram Demonstration Project are 14 manipulatives addressing ID-A.1, 5 manipulatives for ID-A.2, 9 manipulatives for ID-A.3, 12 manipulatives for ID-A.4. Among those:

- Stylized pie and bar charts for fast food nutrition: Illustrates styling options. Aligns with ID-A.1.
- Exploring skewness in box plots: Aligns with ID-A.1.
- Bin width and histogram shape: This histogram presents data regarding the time between eruptions of Old Faithful in Yellowstone. Aligns with ID-A.1.
- Line plots, histograms, and stem-and-leaf plots: Various ways to display data. Aligns with ID-A.1, ID-A.2.
- Descriptions of univariate data: Aligns with ID-A.2.
- Mean, median, mode: Aligns with ID-A.2.
- Mean, median, and standard deviation for random values: Aligns with ID-A.2.
- The 2001 CSO Mortality Tables: The 2001 CSO Mortality Tables represent the most widely used approximations as to the expected rates of death in the United States as a function of age. The demonstration manipulates the data to show three types of plots. Aligns with ID-A.1, ID-A.3, MD-A.4, MD-B.6.
- Area of a normal distribution: Aligns with ID-A.4.
- Matching temperature data to a normal distribution: Aligns with ID-A.4.
- Standard normal distribution areas: Aligns with ID-A.4.
- Impact of sample size on approximating the normal distribution: Aligns with ID-A.4.
- Bell curves: Aligns with ID-A.4.
- Mean and standard deviation of a distribution: Aligns with ID-A.4.

Zweigmedia.com: Tutorials and examples using fill-in or multiple choice to test understanding. Learners can also choose to do game versions of some topics. Use with ID-A:

- Measures of central tendency: Mean, median, mode, expected value of a random variable
- Measures of dispersion: Part A: Variance and Standard Deviation of a Set of Scores; Part B: Variance and Standard Deviation of a Random Variable

**Multiple Choice:**

MathBitsNotebook: Algebra 1: Statistics: Data Distributions includes lessons, then practice problems on categorizing data, box plots, outliers, measures of center and shapes of distributions, standard deviation, and interpreting graphs. Use with ID-A.1, ID-A.2, and ID-A.3.

MathsIsFun.com:

- Quartiles: How to calculate quartiles and interquartile range and their association to a box and whisker plot. Following the explanation are ten multiple choice exercises. Use with ID-A.1 and ID-A.2.
- Outliers: Contains a concise explanation of the role outliers play in data analysis involving mean, median, and mode. Following the explanation are eight multiple choice exercises. Use with ID-A.3.
- Standard deviation and variance: After explanation and examples, there are 10 multiple choice exercises. Use with ID-A.4.
- Normal Distribution: Contains a concise explanation of the normal distributions, standard deviation (how to compute it, and accompanied by a standard deviation calculator), and z-scores with visuals and worked examples. Following the explanation are ten multiple choice exercises. Use with ID-A.4.
- Standard Normal Distribution Table: includes a manipulative to work with z-scores on a normal distribution and a table of values associated with percent of population with worked example. Following the explanation are ten multiple choice exercises. Use with ID-A.4.

McDougal Littell ClassZone, Algebra 2, 2011:

- Ch. 11.1 Quiz: Find Measures of Central Tendency and Dispersion
- Ch. 11.2 Quiz: Apply Transformations to Data
- Ch. 11.3 Quiz: Use Normal Distributions

OpenEd:

- Representing data in plots: 6 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-A.1.
- Comparing data sets: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-A.2.
- Difference in data sets and outliers: 5 multiple choice questions. Includes related review resources. Aligns with ID-A.3.
- Normal distributions: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-A.4.

**Constructed-response:**

Khan Academy: Practice questions with videos.

- Creating box and whisker plots: Aligns with ID-A.1.
- Mean, median, and mode: Aligns with ID-A.2.
- Calculating z-scores: Aligns with ID-A.4.
- Standard deviation of a population: Aligns with ID-A.2.
- Comparing data distributions: Aligns with ID-A.1, ID-A.2, ID-A.3.
- Empirical rule: Aligns with ID-A.4.
- Calculating z-scores: Find the z-score given the mean and standard deviation. Aligns with ID-A.4.
- Normal distribution: Area above or below a point: Aligns with ID-A.4.
- Normal distribution: Area between two points: Aligns with ID-A.4.

Statistics: Power from Data:

- Ch. 9: Graph Types: Explanations of graph types. A tool is presented for learners to create graphs. Practice exercises are included.
- Ch. 11: Measures of Central Tendency: Explanations on mean, median, mode with exercise problems and answers.
- Ch. 12: Measures of Spread: Explanations on range and quartiles, variance and standard deviation, box and whisker plots with exercise problems and answers.

Wisc-Online, Learning Objects: Use to address standard ID-A.4:

- The Normal Distribution: Learners read a definition of normal distribution. In this interactive exercise, they enter values for the mean and the standard deviation of normally distributred data and observe the resulting changes in the shape of the normal curve.
- The Normal Distribution and the Empirical Rule: Students use the Empirical Rule to calculate the percentages of data between two data points. They also calculate the values corresponding to the given percentages of the data. Practice problems are included throughout. Note: The Empirical Rule states that in a normal distribution data set, 68% of data values will fall between the mean plus one standard deviation and the mean minus one standard deviation.
- The Area Under the Standard Normal Distribution: The learner identifies and calculates the area under the normal curve specified by given z-scores. Note: the area under the curve is used as a method to find the percent of data values between two given values. A z-score tells us how many standard deviations above or below the mean a score is. See Statistics Help for Students: What are Z-Scores?

**Performance tasks:**

Illustrative Mathematics: Statistics and Probability:

- Speed Trap: This task aligns with ID-A.1, ID-A.2, and ID-A.3.
- Haircut Costs: This task aligns with ID-A.1, ID-A.2, and ID-A.3.
- Should we send out a certificate?: This task aligns with ID-A.4

Mathematics Assessment Project: Standards: High School: Statistics & Probability: https://www.map.mathshell.org/stds.php?standardid=1233

- Task 404: Representing Data 1: Using Frequency Graphs
- Task 423: Representing Data 2: Using Box Plots

Mathematics Vision Project, Secondary 1 Student Edition:

- Module 8: Modeling Data: This module contains 8 classroom tasks. Module 7 addresses standards ID-A.1, ID-A.2, ID-A.3 and ID-B.5, ID-B.6 and ID-C.7, ID-C.8. Task 1: Texting by the Numbers; and Task 2: Data Distributions align to standards ID-A.1, ID-A.2, and ID-A.3.

Mathematics Vision Project, Secondary 3 Student Edition:

- Module 8: Statistics: This module contains 8 classroom tasks. Tasks 1-4 align with standard ID-A.4.

NCTM's Reasoning and Sense Making Task Library: Eruptions: Old Faithful includes the task overview, teacher notes for its use in the classroom, and student activity sheet. Aligns with IC-A.1, ID-A.1, and mathematical practice standards 1, 3, and 5.

Statistics in Schools from the U.S. Census Bureau: Activities: Math: 9-12:

- Commuting to Work: Box Plots, Central Tendency, Outliers. Students "calculate various measures of central tendency using data on the number of people who bike to work in select states. Students will then create a box plot to represent the data set and answer conceptual questions about the impact of the data set’s outlier." Aligns with ID-A.1.
- Differences in Earnings Across Sex and Educational Attainment: Comparing Box Plots. Students "interpret box plots that represent the national median earnings of men and women aged 25 and older whose highest levels of educational attainment are either a high school diploma (or equivalent) or a bachelor’s degree. Students will use the box plots to identify each data set’s median, maximum, minimum, first quartile, third quartile, range, interquartile range, and outliers. They will also compare the box plots to draw conclusions about differences in earnings between the sexes and between levels of educational attainment." Aligns with ID-A.1, ID-A.2, ID-A.3.
- Describing and Comparing Data Distributions. Students "use data on the organization, spending, and populations of governments at different levels (city or town, county, and state) to compare and contrast the distributions of these variables in graphs [box plots and histograms], analyzing the shape, center, and spread of each." Aligns with ID-A.2, ID-A.3.
- Census in Countries: Describing and Comparing Histograms to Understand American Life. Students "analyze a variety of county-level census data, including on employment, technology, and transportation, in histograms to compare and contrast the shapes of their distributions and to interpret measures of center and spread in context." Aligns with ID-A.2, ID-A.3.
- The New Normal. Students "explore distributions of various census data sets to determine whether it can be reasonably assumed that those data follow a normal distribution, based on students’ analysis of either a histogram or a normal probability plot for each data set. They will then discuss their findings with a partner who analyzed the other type of graph for each data set." Aligns with ID-A.3.
- Over the Hill: Aging on a Normal Curve. Students "use census data from a sample of 136 U.S. counties and other sample data to make estimates about the U.S. population that is 65 or older in all other counties and about other variables, using normal distribution models." Aligns with ID-A.4.

**Standards**:

- ID-B.5. Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
- ID-B.6. Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
- ID-B.6.a. Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.
- ID-B.6.b. Informally assess the fit of a function by plotting and analyzing residuals.
- ID-B.6.c. Fit a linear function for a scatter plot that suggests a linear association.

**Technology-enhanced investigations:**

Alcula.com:

- Online Scatter Plot Generator. Use with ID-B.6.
- Online Linear Regression Calculator: Compute the equation for the line of best fit from a set of bivariate data. Use with ID-B.6.

CK-12: Algebra: Concepts, video, and practice problems for learners.

- Application of Function Models: Learn how to fit data to a model; Draw a scatter plot, find and draw the function that best fits the data, and make predictions from that information.
- Use Graphs and Technology to Solve Quadratic Equations: Find intercepts, axis of symmetry, and vertex using technology.

LearnZillion:

- Lesson set: Summarize categorical data for two categories in two-way frequency tables, interpret relative frequencies, and recognize trends: Three video lessons: Interpret a two-way frequency table, compute conditional relative frequencies, determine associations by comparing conditional relative frequencies. Aligns with ID-B.5.
- Lesson set (additional): Summarize categorical data for two categories in two-way frequency tables, interpret relative frequencies, and recognize trends: Three additional video lessons: Set up a two-way frequency table, use two-way frequency tables by determining if the data is a frequency count or a relative frequency, calculate probabilities by using the data in a two-way frequency table. Aligns with ID-B.5.
- Lesson set: Represent and describe data on two quantitative variables on a scatter plot: video lessons: Describe the relationship between two quantitative variables by looking at a scatter plot, describe a relationship in terms of strength and direction, make predictions using a line of best fit, draw and calculate residuals, understand the least squares regression line and find its equation using technology. Aligns with ID-B.6.

McDougal Littell ClassZone, Algebra 2, 2011: Select resources in Ch. 2 Linear Equations and Functions for interactive practice: Animations (Virtual manipulatives, including The Correlation of x and y), Games/Vocabulary Flashcards, and PowerPoint lesson examples, and interactive problems within the eWorkbook. See multiple choice below for related sections to use within the eWorkbook (Do section 2.6). Use with ID-B.6.

MIT BLOSSOMS: Video lesson with additional teacher and learner resources. Description is from the video summary. Flu Math Games: "This video lesson shows students that math can play a role in understanding how an infectious disease spreads and how it can be controlled." Additional simulations are included. Aligns with Algebra standards SSE-B.3.c and REI-A.1; Function standards IF-C.8.b, BF-B.4.a, and LE-A-1.(a, c); and Statistics and Probability standards ID-B.6.a, IC-A.1, IC-B.4, CP-A.2, and MD-A.1.

NCTM Illuminations:

- Line of Best Fit: This virtual manipulative allows the user to enter a set of data, plot the data on a coordinate grid, and determine the line of best fit.
- Linear Regression I extends this activity. Learners plot points on a coordinate grid in a relatively straight line to create a scatter plot, show the regression line (line of best fit), and then experiment with adding adding additional points (e.g., outliers) to the grid and viewing the resulting change in the line of best fit.
- Correlation and the Regression Line: “Interactive computer-based tools provide students with the opportunity to easily investigate the relationship between a set of data points and a curve used to fit the data points. As students work with bivariate data in grades 9-12, they will be able to investigate relationships between the variables using linear, exponential, power, logarithmic, and other functions for curve fitting.”
- Determining Functions Using Regression: “This unit guides students though activities that ask students to collect data. Then, they use technology to find functions that best describe a data collected. After analyzing the data, the student should be able to determine a best type of function to describe the trend.”

Ohio Resource Center on YouTube: Tutorials for High School Mathematics: Lines of Fit: Defining and finding lines of fit using real data.

Saltire Software: Common Core Nuggets: There are five applets addressing residual plots and least squares, which align with ID-B.6.

Shodor Interactivate:

- Linear Regression and Correlation: Lesson introducing correlations between two variables and line of best fit.
- Univariate and Bivariate Data: Lesson introducing the difference between these two types and how to determine the best graph to use to display the data.
- Regression: A virtual manipulative to plot a bivariate data set, determine the line of best fit for the data, and then check the accuracy of a line of best fit.

Stat-Trek: Tutorials:

- What is a scatterplot?: The tutorial also includes a video.
- What is linear regression?: The tutorial also includes a video.

Thinking Mathematics: Line of Best Fit: Least Squares Method: Video with explanations on why we calculate the line of best fit the way we do, and the mathematics behind the formula for the best line fitting data on a scatter plot.

College Preparatory Math: Student Tutorials: TI-84 Graphing Calculator: Bivariate Data: Videos and screen shots illustrating use of the calculator for setting up a scatter plot, non-linear regressions, least squares regression line, the correlation coefficient, residuals & residual plots.

Wolfram Demonstrations Project: Download the free Wolfram CDF player to interact with the following manipulatives. Note: Within the Wolfram Demonstration Project are 1 manipulative addressing ID-B.5, 20 manipulatives for ID-B.6. Among those:

- Clean tile problem: Aligns with ID-B.5.
- Interactive curve fitting: Aligns with ID-B.6.
- Simple best-fit line: Aligns with ID-B.6.
- Linear and quadratic curve fitting practice: Aligns with ID-B.6.
- Curve fitting: For polynomials. Aligns with ID-B.6.
- Ordinary regression and orthogonal regression in the plane: Determine line of best-fit methods. Aligns with ID-B.6.
- Correlation and regression explorer: Aligns with ID-B.6, ID-C.8.

Zweigmedia.com: Tutorials and examples using fill-in or multiple choice to test understanding. Learners can also choose to do game versions of some topics. Use with ID-B.6: Linear regression

**Multiple Choice:**

MathsIsFun.com: Scatter Plots: Contains a concise explanation of the scatter plot and its relation to line of fit and correlation. Following the explanation are nine multiple choice exercises. Use with ID-B.6.

Khan Academy: Practice questions with videos.

- Interpreting scatter plots: Aligns with ID-B.6.

MathBitsNotebook: Algebra 1: Statistics: Bivariate Data includes lessons, then practice problems on two-way frequency tables, fitting functions to data, residuals, linear regression, correlation and correlation coefficients, slopes and intercepts of linear models. Use with ID-B.5, ID-B.6, ID-C.7, ID-C.8, and ID-C.9.

McDougal Littell ClassZone, Algebra 2, 2011: Ch. 2.6 Quiz: Draw Scatter Plots and Best-Fitting Lines: Use with ID-B.6.

OpenEd:

- Two-way frequency tables: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-B.5.
- Scatter plots: 5 multiple choice questions. Includes related review resources. Aligns with ID-B.6.
- Fitting functions to data: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-B.6.a.
- Residuals: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-B.6.b.
- Line of best fit: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-B.6.c.

**Constructed-response:**

Khan Academy: Practice questions with videos.

- Trends in categorical data: Aligns with ID-B.5.
- Frequencies of bivariate data: Aligns with ID-B.5. (mix of fill-in and multiple choice).
- Estimating the line of best fit: Aligns with ID-B.6.
- Linear models of bivariate data: Aligns with ID-B.6, ID-C.7.

**Performance tasks:**

Illustrative Mathematics: Statistics and Probability:

- Musical Preferences: Aligns with ID-B.5.
- Coffee and Crime: Aligns with ID-B.6, ID-C.7, ID-C.8 and ID-C.9.
- Olympic Men's 100 Meter Dash: Aligns with ID-B.6.a and ID-C.7.

Inside Mathematics: MARS Tasks: The following align with ID-B.6:

- Population: Students work with a scatter plot to identify specific information on it, describe main features of a scatter plot and make sense of trends in order to graph a line to represent average density and calculate density relationships in the given situation.
- Snakes: Students work with two scatter plots to make sense of data displayed. They make sense of a table and look for trends including correlations and lines of best fit, and they make inferences based on data and conclusions about a situation being modeled.

Mathematics Assessment Project: Standards: High School: Statistics & Probability: https://www.map.mathshell.org/stds.php?standardid=1233

- Task 217: Interpreting Statistics: A Case of Muddying the Waters
- Task 420: Devising a Measure for Correlation

Mathematics Vision Project, Secondary 1 Student Edition: Module 8: Modeling Data: This module contains eight classroom tasks. Module 8 addresses standards ID-A.1, ID-A.2, ID-A.3 and ID-B.5, ID-B.6 and ID-C.7, ID-C.8. Task 3: After School Activity; and Task 4: Relative Frequency align to standard ID-B.5. Task 7: Getting Schooled; and Task 8: Rocking the Residuals align to standard ID-B.6.

Statistics in Schools from the U.S. Census Bureau: Activities: Math: 9-12:

- Linear Models – Analyzing Relationships: Marriage, Divorce, and Linear Regression. Students "create a scatter plot, find a line of best fit, and analyze the relationship between the two variables (i.e., sex and marriage/ divorce rates). They will also use a residual plot, explain the meaning of the slope and of the y-intercept of the line of best fit, and investigate the effect of outliers on this line." Aligns with ID-B.6, ID-C.7.
- Applying Correlation Coefficients: Educational Attainment and Unemployment. Students "use state and regional unemployment data for various education levels to create scatter plots and calculate correlation coefficients. Students will then compare scatter plots with different strengths of linear relationships and will determine the impact of any influential points on the correlation coefficient. Aligns with ID-B.6, ID-C.8.

**Standards**:

- ID-C.7. Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
- ID-C.8. Compute (using technology) and interpret the correlation coefficient of a linear fit.
- ID-C.9. Distinguish between correlation and causation.

**Technology-enhanced investigations:**

Khan Academy: Correlation and Causality video explanation of the difference. Aligns with ID-C.9.

LearnZillion:

- Lesson set: Interpret the slope and intercept of a linear function in context: Three video lessons: Distinguish between scatterplots and lines; interpret intercepts and slope using a line of best fit. Aligns with ID-C.7.
- Lesson set: Interpret the slope and the intercept of a linear model using data: Four video lessons: Interpret the parts of a linear model, find the slope of a linear function by using two points, find the slope and y-intercept of a line by using the slope formula and analyzing linear models, solve real-world problems using slope and y-intercept, determine type of slope by analyzing linear function word problems. Aligns with ID-C.7.
- Lesson set: Find correlation coefficient of a linear fit: Three video lessons: Understand correlation in terms of the strength of a relationship, find the correlation coefficient by using technology, understand and interpret the slope of a regression line. Aligns with ID-C.8.
- Lesson set: Compute and interpret the correlation coefficient of a linear fit: Four video lessons: Use the correlation coefficient to assess the strength of a linear fit, assess the appropriateness of a linear correlation model, calculate the correlation coefficient by using a graphing calculator, solve problems using linear regression. Aligns with ID-C.8.
- Lesson set: Distinguish between correlation and causation: Three video lessons: Understand causation; distinguishing correlation and causation using the reverse causation example and by examining a common causation example. Aligns with ID-C.9.
- Lesson set: Distinguish between correlation and causation and assess causation: Three video lessons: Differentiate between correlation and causation, evaluate language that confuses correlation and causation, establish causation through experimental design. Aligns with ID-C.9.

NCTM Illuminations:
Least Squares Regression: “This Unit Plan consists of lessons in
which students interpret the slope and *y*-intercept of least
squares regression lines in the context of real-life data. Students
use an e-example applet to plot the data and calculate the
correlation coefficient and equation of the least squares regression
line.”

Stat Trek: Tutorial: Linear Correlation Coefficient: The tutorial includes a video, and explanation on how to interpret and calculate the correlation coefficient. Use with ID-C.8.

Social Science Statistics: Pearson Correlation Coefficient Calculator. Free online calculator. Use with ID-C.8.

College Preparatory Math: Student Tutorials: TI-84 Graphing Calculator: Bivariate Data: Videos and screen shots illustrating use of the calculator for setting up a scatter plot, non-linear regressions, least squares regression line, the correlation coefficient, residuals & residual plots.

Wolfram Alpha: Wolfram Demonstrations Project: Download the free Wolfram CDF player to interact with the following manipulatives. Note: Within the Wolfram Demonstration Project are 3 manipulatives for ID-C.8. Among those:

- Correlation and regression explorer: Aligns with ID-B.6, ID-C.8.
- Anscombe Quartet: The Anscombe Quartet is comprised of four scatterplots that have nearly identical correlations, as well as means and standard deviations, but disparate shapes. These graphs show the crucial role that data visualization plays in developing a sensible statistical model. Aligns with ID-C.8.

**Multiple Choice:**

Khan Academy: Practice questions with videos.

- Identifying slope of a line: Aligns with ID-C.7.
- Linear models of bivariate data: Aligns with ID-B.6, ID-C.7. (mix of fill-in and multiple choice).
- Types of statistical studies: Aligns with ID-C.9.

MathBitsNotebook: Algebra 1: Statistics: Bivariate Data includes lessons, then practice problems on two-way frequency tables, fitting functions to data, residuals, linear regression, correlation and correlation coefficients, slopes and intercepts of linear models. Use with ID-B.5, ID-B.6, ID-C.7, ID-C.8, and ID-C.9.

MathsIsFun.com: Correlation: Contains a concise explanation of the meaning of correlation, correlation does not mean causation, and how to calculate the correlation coefficient. Worked examples are provided. Following the explanation are four multiple choice exercises. Use with ID-C.8 and ID-C.9.

OpenEd:

- Linear modelling: 5 questions combine multiple choice and free response. Includes related review resources. Aligns with ID-C.7.
- Correlation coefficient: 5 multiple choice questions. Includes related review resources. Aligns with ID-C.8.
- Causation: 5 multiple choice questions. Includes related review resources. Aligns with ID-C.9.

**Constructed-response:**

**Performance tasks:**

Illustrative Mathematics: Statistics and Probability:

- Coffee and Crime: This task aligns with ID-B.6, ID-C.7, ID-C.8 and ID-C.9.
- Olympic Men's 100 Meter Dash: Aligns with ID-B.6.a and ID-C.7.
- Texting and Grades II: Aligns with ID-C.7.
- Golf and Divorce: Aligns with ID-C.9.

Mathematics Assessment Project: Standards: High School: Statistics & Probability: https://www.map.mathshell.org/stds.php?standardid=1233 Task 217: Interpreting Statistics: A Case of Muddying the Waters

Mathematics Vision Project, Secondary 1 Student Edition: Module 8: Modeling Data: This module contains eight classroom tasks. Module 8 addresses standards ID-A.1, ID-A.2, ID-A.3 and ID-B.5, ID-B.6 and ID-C.7, ID-C.8. Task 5: Connect the Dots aligns to standard ID-C.8. Task 6: Making More $; and Task 7: Getting Schooled align to both standards ID-C.7, ID-C.8.

Statistics in Schools from the U.S. Census Bureau: Activities: Math: 9-12:

- Linear Models – Analyzing Relationships: Marriage, Divorce, and Linear Regression. Students "create a scatter plot, find a line of best fit, and analyze the relationship between the two variables (i.e., sex and marriage/ divorce rates). They will also use a residual plot, explain the meaning of the slope and of the y-intercept of the line of best fit, and investigate the effect of outliers on this line." Aligns with ID-B.6, ID-C.7.
- Applying Correlation Coefficients: Educational Attainment and Unemployment. Students "use state and regional unemployment data for various education levels to create scatter plots and calculate correlation coefficients. Students will then compare scatter plots with different strengths of linear relationships and will determine the impact of any influential points on the correlation coefficient. Aligns with ID-B.6, ID-C.8.
- Educational Attainment and Marriage: Testing a Correlation Coefficient's Significance. Students "develop, justify, and evaluate conjectures about the relationship between two quantitative variables over time in the United States: the median age (in years) when women first marry and the percentage of women aged 25–34 with a bachelor’s degree or higher. Students will write a regression equation for the data, interpret in context the linear model’s slope and y-intercept, and find the correlation coefficient (r), assessing the strength of the linear relationship and whether a significant relationship exists between the variables. Students will then summarize their conclusions and consider whether correlation implies causation." Aligns with ID-C.8, ID-C.9, and IC-A.1.

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