**Monday**

In Class

Learning Targets:

*I can explain why Correlation does not imply Causation*

*I can discuss Lurking Variables: Confounding:Common Response situations + the Diagrams*

Finishing up your Spurious Correlation posts in Canvas

**Tuesday**

In Class

Learning Target:

- I can
*use a LSRE for prediction.* *I can find a residual and I know why the Least Squares Regression (LSRE) equation has that name.*- I can explain what extrapolation is and the problems with using a LSRE to make a prediction that is an extrapolation situation.

We’ll do some exploration with this data <link> and this web page <link>

Exit ticket

**Wednesday**

In Class:

Same learning targets

More learning about yesterday’s Targets <link>

Now that you have some background, let’s see an application: <link>

Learning Target: *I can describe what the slope and y-intercept of the LSRE means in the context of the situation.*

Learning Target: *I can find r ^{2} and describe what it means in the context.*

We’ll look at some of those situations in the spreadsheet

**Thursday**

Learning Targets:

I can

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.