Extended Mathematics 11 Pacing Guide - This pacing guide replaces the previous yearly plan. It has been updated to reflect removed outcomes and provide flexibility for responsive instruction.
DA01 Students will be expected to analyze, interpret, and draw conclusions from two-variable data using numerical, graphical, and algebraic summaries. [C, CN, R, T, V]
DA01.01 Recognize that the analysis of two-variable data involves the relationship between two attributes.
DA01.02 Distinguish between situations that involve one variable and situations that involve more than one variable.
DA01.03 Generate scatter plots of two-variable data, by hand and using technology
DA01.04 Determine, by performing a linear regression using technology, the equation of a line that models a suitable two-variable data set
DA01.05 Determine, using technology, the correlation coefficient, and recognize it as a measure of the fit of the data to a linear model
DA01.06 Determine the fit of an individual data point to the linear model by determining its residual, and recognize how a residual plot can be used to determine if a linear equation is a good model for a two-variable data set.
DA01.07 Make inferences, and make and justify conclusions, from statistical summaries of two-variable data orally and in writing, using convincing arguments
DA02 Students will be expected to critically analyze society’s use of inferential statistics. [C, CN, R, T, V]
DA02.01 Investigate examples of the use of inferential statistics in society
DA02.02 Assess the accuracy, reliability, and relevance of statistical claims in the media by
identifying examples of bias and points of view, including the use and misuse of statistics to promote a certain point of view
identifying and describing the data collection methods, including the characteristics of a good sample, some sampling techniques, and principles of primary data collection
determining if the data is relevant
DA02.03 Recognize and explain why conclusions drawn from statistical studies of the same relationship may differ.
DA02.04 Recognize and explain how the collection and analysis of data has impacted and continues to impact our world.
DA02.05 Create infographics / data visualizations using the design principles of good data visualization.
DA02.06 Identify, discuss, and present multiple sides of the issues with supporting data.
DA03 Students will be expected to analyze data, identify patterns, and extract useful information and meaning from large, professionally collected data sets. [C, CN, R, T, V]
DA03.01 Explore and analyze large sets of open data using technology.
DA03.02 Investigate what data is available and open and why some data is open and other data not.
DA03.03 Pose questions that might be answered or further explored with large open data sets.
DA03.04 Present their findings from an investigation of a big data set.
Additional Resources and Activities for DA01 (two-variable data):
Hula Hoop and Starburst Scatterplots - Two activities for creating scatter plots and lines of best fit.
A Brief History of the Scatter Plot - The scatter plot has been called the most “generally useful invention in the history of statistical graphics.” This article talks about the origins of this scatter plot and how it has been used to allow people to adeptly look at a large number of points on a scale and understanding their relationship.
Candle’s Burning 3-Act Task - Try to predict how long it will take for a candle to burn out. Linear relationships including classifying positive/negative, strong/weak, predicting via interpolation and extrapolation using a line of best fit on a scatter plot.
Are Any of My Students Compatible? - Students “discover” the role of the correlation coefficient r – how it acts as a measure of the strength of the relationship between two quantitative variables in this activity.
Desmos Activity: Charge! (v2) - In this activity, students use linear modeling to predict how long it will take for a smartphone to reach full charge. Students will also interpret the parameters of their equation in context.
MLB Player Batting Stats - This data can be displayed as multiple one variable data as well as two variable data. If a student compared a player and his home runs this would be considered one variable data. If a student compared home runs and batting averages (AVG) then this would be considered two variable data.
Guess the Correlation - the aim of the game is simple. try to guess how correlated the two variables in a scatter plot are. the closer your guess is to the true correlation, the better.
Analyze a Linear Regression Line by Using Residual Analysis - In this lesson students formally fit a least squares regression line to a set of data thought to be linearly associated. After determining the least squares regression line, students find residuals and create a residual plot to assess the linearity of the relationship between two quantitative variables.
Residual Plot Analysis - A regression tool that provides options to calculate the residuals and output the customized residual plots.
EECD 22 Residual Plots Project - An activity shared by EECD to support students in deepening their understanding of outcome DA01.
The Datasaurus Dozen - 13 datasets (the Datasaurus, plus 12 others) each have the same summary statistics to two decimal places, while being drastically different in appearance. This shows why it is important to visualize data and not just rely on a statistical summary.
Video - Regression and Residuals Using Google Docs
Causation and Lurking Variables - PDF of a PowerPoint Slide Deck identifying examples of causation and lurking variables.
Desmos Activity: Are People Waiting to Get Married? - Students explore the relation between median age at first marriage and time (number of years since 1960) for men and women. They make predictions, write equations, and reflect on the behavior—and contextual meaning—of graphs and parameters.
Additional Resources and Activities for DA02 (society’s use of statistics ):
What is Data Science - Online article identifying "8 Skills That Will Get You Hired In Data".
Data Skills - Online article exploring "What Are The Most - Wanted Data Science Skills for 2016"
Our World in Data - Explore the ongoing history of human civilization at the broadest level, through research and data visualization.
EECD 23 Using Stats to Support a Point of View - A debate activity shared by EECD to support students in deepening their understanding of outcome DA02.
Sampling Slide Show - Provides an overview of the different types of probability and non-probability sampling.
How to Spot a Misleading Graph video- When they’re used well, graphs can help us intuitively grasp complex data. But as visual software has enabled more usage of graphs throughout all media, it has also made them easier to use in a careless or dishonest way — and as it turns out, there are plenty of ways graphs can mislead and outright manipulate. Lea Gaslowitz shares some things to look out for in this short 4 minute TED-Ed video.
Data Viz Project - The Data Viz Project has a collection of data visualizations, searchable and sortable by shape, input, function, and family, each with real-world examples.
EECD 24 Infographics Project - An activity shared by EECD to support students in deepening their understanding of outcome DA02.
FAANGG' Is Catchy, But Not Fully Accurate from Seeking Alpha - This article includes an anecdote from Jordan Ellenberg's How Not to Be Wrong: The Power of Mathematical Thinking that helps to illustrate the erroneous nature of using positive and negative numbers in statistics. Ellenberg calls this the "More pie than plate" fallacy.
16 Useless Infographics - If it's an image that displays and explains information quickly and clearly, it's an infographic This is an exciting gallery of infographics that tell you nothing.
Weekly Example of Media Bias - A weekly example of biased news reporting. Also included on each page are questions about the excerpt and definitions of the types of media bias.
Canva How - To - Canva is available in the GNSPES Launchpad, can be used to create Infographics.
Robots and Jobs from Different Perspectives - students could use to create infographics.
Additional Resources and Activities for DA03 (big data):
Analytics for Beginners - A video that provides an introduction to data analytics, with examples (start at 2:08).
EECD 25 Data Analytics Lessons - A series of lessons shared by EECD to support students in deepening their understanding of outcome DA03.
DA03 YouTube Video Playlist - A series of instructional videos was created by Darcy Benoit to support instruction of DA03. These all relate to the Rio Olympics Data set.
EECD 26 Open Data Project - A project shared by EECD to support students in deepening their understanding of outcome DA03.
Online Sources For Data (Note: Teachers should be aware that some of these open data set may contain information that could cause conflict in the classroom, such as divorce records for the Province of Nova Scotia, etc. Care should be taken when choosing data sets to be used in the classroom.)
StatsCan - An excellent source for data
Government of Canada Open Data Portal - This site contains data about Government of Canada services, financials, and national demographic information.
Province of Nova Scotia Open Data Portal - This site contains data from the province of Nova Scotia.
Olympian Data Base - The Olympian Database holds all Olympic medals since the first Olympic Games in Athens 1896.
Gapminder World
A teacher guide explains how you can use Gapminder World to lecture about global development from 1800 until today.
A 5 minute video clip to show the power of Gapminder
An interactive graph whose default shows the relationship between GDP per capita, and Life Expectance, for countries around the world. County points are color coded based on continent, and vary in size to reflect population. Default setting can be changed to reflect a variety of different data relationships, show changes over time, and are reflective of a variety of historical events (ie: Ireland's life expectance dips around 1845 and recovers around 852, reflective of the Irish Potato Famine).