Outcome #8
Data
Embedded Files
Description
This outcome covers the following:
mean, median, mode, range, quartiles and box plots
creating scatterplots using technology, identifying correlations and making predictions
using regression models to find a lines and curves of best fit
analysing one and two variable data
Curriculum Expectations: D1.1, D1.2, D1.3, D2.1, D2.2, D2.3, D2.4, D2.5
Notes:
The intent is not for students to be creating graphs by hand. This is an opportunity to be making good use of technology.
Emerging
Emerging
I can display single variable data in various ways (ie. bar graphs, histograms, dot plots)
I can perform single data variable calculations.
measures of central tendency (ie. mean, median, mode, range)
measures of spread (ie. quartiles, Q1, Q2, Q3, IQR)
Notes/Examples:
Quartiles will lead to making box plots
Mean, Median, Mode, Range offers a good opportunity to practice numeracy skills
Developing
Developing
I can use and compare box plots.
I can construct scatter plots with technology, identify correlations (ie. strong/weak, positive/negative), and make predictions when appropriate (ie. graphically).
I can identify the information needed to answer a question of interest requiring the collection of data.
Notes/Examples:
Box plots should first be done by hand but can be explored using technology as well
Graphs should be created using technology (excel, google sheets, Desmos)
Provide examples of questions of interest and the data that could be collected to answer those questions (building skills for summative data task)
Proficient
Proficient
I can analyse a variety of single and two variable data sets.
I can perform linear regression using technology and interpret the correlation coefficient and then make predictions when appropriate. (ie. interpolation, extrapolation)
I can pose a question of interest, create a plan to collect the necessary data and carry out the plan effectively.
Notes/Examples:
Use Desmos or Google sheets to perform linear regression.
Feedback given on Summative Data Assessment (Report); What question is being posed, what data is being collected and how (ie. primary or secondary sources)
Exceeding
Exceeding
I can test different regression models using technology and can use the line or curve of best fit to interpret and make predictions.
I can display and analyse data I collected in order to explore an original question of interest.
Notes/Examples:
Desmos works well for regression models. Focus on linear regressions vs non-linear regressions (quadratic in standard form and exponential models for non-linear)
Opportunity for feedback on Summative Data Assessment (Report) is given before final report is submitted
Extending
Extending
I can thoroughly report how a model can be used to answer a question of interest which requires the planning and analysing of data , how well the model fits the context, potential limitations of the model, and what predictions can be made based on the model.
Notes/Examples:
A final version of their Summative Data Assessment (Report), which has been built throughout the outcome, is submitted with previous opportunities for revisions and additions given
Sample Assessments
(Approaching Standard)
(Approaching Standard)
(Reaching Standard)
(Reaching Standard)
(Approaching Standard -> Extending Understanding)
(Approaching Standard -> Extending Understanding)
Lesson Ideas
Intro to Data
Intro to Data
This a framework of what the first two lessons of the Data outcome could look like. The word doc included could be used to as a student page for lesson 1.
This Thinking Classroom Task has students exploring data sets of class marks. Students will develop an understanding of mean, median, mode, range and Box Plots.
This task was originally shared by Jamie Mitchell on twitter: @realJ-Mitchell
Linear Regression
Linear Regression
The following is a smartboard lesson to introduce linear regression by first examining scatterplots and drawing lines of best fit by hand. It then leads into performing linear regression using Desmos. Google docs are also included below with student support pages and practice questions with data sets.
Linear Regression (and Nonlinear Regression) Lesson Notebook file
This fun data collecting activity created by Sarah Carter compares hand span to the number of candies you can pick up with one hand.
Use this hands-on activity in your classroom to collect and analyze data. Students use different pull back distances to determine how far their car will travel. Use graphing software to determine a relationship and make predictions to complete challenges.
Coding Activities
This lesson leads the class through creating a program that will calculate the average of a set of numbers. It starts with three numbers, then extends to a larger set.
Follow-up activities are included to challenge students at various comfort levels. Each "spiciness level" includes a program already started that needs completing, and then a program students will have to write on their own.
Follow-up Activities
Desmos Classroom Activities
This activity helps emphasize the key parts that make up a box and whisker plot (max, min, range, IQR, Q1, Q2, Q3)
Students learn about lines of best fit by "eyeballing" a line to data involving the amount of profit a business earns based upon advertising dollars spent.
Students use their equation to calculate the profit based upon various advertising budgets and vice versa.
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