Distance Learning Update: 1st official Blackboard Collaborate session TODAY

Hopefully you have been receiving the Blackboard annoucements via email. For now, I will also post them to OpenLab:

Hi all,

I will plan to have Blackboard Collaborate Ultra sessions during our regularly scheduled class times. So our first one will be today (Monday) from 12p-1:40p. You should be able to see the session scheduled under Course Tools->Blackboard Collaborate Ultra in Blackboard. You can join the session there, or via the guest link:


Attendance (i.e., logging in) for class sessions is not required, but I do strongly recommend it, assuming you have access to a device and a reliable wifi or data connection. As some of you saw last week, you can even join and view these sessions via a phone (but I do recommend a computer or tablet, so that you have a bigger screen to view pdfs, the whiteboard and other content that I will share in the sessions).

Tomorrow we can go over any remaining HW5 questions, and discuss conditional probability (using the Class 12 outline pdf I uploaded to Openlab on March 11):

For those of you that can’t join, I will post a summary and followup instructions on OpenLab this afternoon after the Blackboard session.

Hope to see all of you on Blackboard!

Quiz #2 / “HW4-Paired Data”

We will have a quiz (Quiz #2) tomorrow (Wednesday, Feb 26). The quiz will be a simple exercise involving generating a scatterplot and calculating the correlation coefficient (using the spreadsheet command =correl) for a  given paired data set.

To prepare for the quiz, review the class outline on those topics and also review the exercises from “HW4-Paired Data” on scatterplots and the correlation coefficient (exercises #6, 9, 10, 13, 14, 19, 22):

  • you can use the built-in spreadsheet function =correl to calculate the correlation coefficient for #6, 19, and 22
  • #19 and #22 ask for additional statistics related to linear regression–those won’t be covered on tomorrow’s quiz

Here are additional notes and hints on “HW4-Paired Data” (which is due Mon March 2)

  • #1-2, 5 (review of equations of lines, independent/dependent variables)
    • recall that if we have y given as a function of x, we call x the independent variable, and y the dependent variable
    • especially in the context of linear regression, where we get a linear function (or “linear model”)  y = α + βx that seeks to explain the y-variable in terms of the x-variable, then x is sometimes called the explanatory or input variable, and y is called the response or output variable
  • #3, 4, 20, 21, 22 (linear regression)
    • for #3 and 22, use the built-in spreadsheet functions =slope(y_data, x_data) and =intercept(y_data, x_data) to find the “least squares line” (i.e., the linear regression line y = α + βx, where α is the y-intercept and β is the slope
  • #7, 8, 17, 19 ask about the “coefficient of determination”