**Monday**: No School

**Tuesday**

In Class

Some warm up review

Learning Target:

I can *use a LSRE for prediction.*

*I can explain what LSRE means*

We will watch this video together <link>

Then we will complete this activity <link> You’ll need an “approval” before you can begin. Sign in with Google so you can earn these formative points.

**Wednesday**

In Class:

Learning target: *I can determine the appropriateness of a linear regression model for use with a set of data using r*^{2 }

More learning about yesterday’s Targets <link> and we will use this web page <link>

If you want the grade, in Canvas>Week 7: try the MOM assignment Oxbow Correlation/Regression. Try them all, get at least a 70% and I’ll write int full credit in IC

**Thursday/Friday**

Learning Targets:

I can explain what the slope and y-intercept of a LSRE means in the context of the situation.

We will look at the situation in the data from yesterday.

Then here is the last big grade in this marking period. I’ll allow some time next week also to complete this project.

Here is the assessment:

Regression Analysis:

Part 1

Choose two quantitative variables that you think may be associated. You can collect some data yourself or find lists on the Internet.

Before you conduct any analysis, What association ( Shape, Strength, Direction) do you expect to see between the two variables? Which variable is the explanatory variable and which variable is the response variable?

Part 2

I’ll be looking to see if the following is present and correct.

- Table of Your two quantitative variables.
- Well labeled scatterplot (Axes with units)
- Correlation coefficient (r)
- Coefficient of Determination (r
^{2}) expressed either as a proportion/percentage and you explain what it means in the context of this situation. - Least Square Regression Equation for predicting your Response Variable from your Explanatory Variable.
- An analysis of your results. You must relate your results back to part 1. This must include a discussion about r and r
^{2} - You use your model to make a prediction for two values that are not part of the original data. One of these values must be an example of extrapolation. Identify this prediction.
- Discuss what the slope and y-intercept of your model means in the context of the situation.
- You suggest possible sources of confounding influences.

You can attach a publicly share link to a google doc/sheet.

If you are going to collect some data, this long weekend would be a good time to do it.