Class Notes (905,598)
CA (538,456)
UTSG (45,721)
POL242Y1 (17)
Lecture

All of September`s lecture notes

4 Pages
125 Views

Department
Political Science
Course Code
POL242Y1
Professor
Joseph Fletcher

This preview shows page 1. Sign up to view the full 4 pages of the document.
Tuesday January 11, 2011
Regression equation: y = a + bx
y = the predicted value, or the dependent variable
and x = the independent variable
a = intercept (y when x = 0 )
b = the slope of a line
we're going to discuss this more next week
regression and correlation fit together in a way, since correlation is defined by a regression
equation
people think interval data is more powerful than nominal or ordinal....
bivariate analysis:
if we want to predict y from x the scores from x must be at least as efficient as the scores of y
as the mean of y itself.
So we have two axes and the idea is that there's one independent and one dependent
variable. These variables have ranks that some things are higher than others, and that
there's a constant interval between each of the units. It's measured in some set of units i.e. x
= years of education. Now the idea here is that we can have a number of individual cases
that are ranked on x or ranked on y. the best predictor of y (just y, alone) would be whatever
the mean score of y is. and the reason that it' s the best predictor is that y would give us the
smallest average deviation of the mean of y. so the mean of the sample becomes the standard
of which we compare any other variable.
so the square deviation of the mean score will let us decide if x is useful.
pg. 309 in brians (only just touched upon on that page).
there's more than one kind of variance or variation. in fact there's 3 we need to worry about...
Types of Variation
1) Total Variance
- It equals the sum of the square differences from the mean on any variable
- The average squared deviation = the total variation of the y variable
- "The least squares line"
2) Unexplained Variance
- Everything that's left over (that doesn't touch the regression "slope" aka the
equation
"y = a + bx")
3) Explained Variance
- Total variation of y that can be attributed to the influence of x
- Pearson's correlation = the square root of the variance.
- So r = the square root of explained variance.
www.notesolution.com

Loved by over 2.2 million students

Over 90% improved by at least one letter grade.

Leah — University of Toronto

OneClass has been such a huge help in my studies at UofT especially since I am a transfer student. OneClass is the study buddy I never had before and definitely gives me the extra push to get from a B to an A!

Leah — University of Toronto
Saarim — University of Michigan

Balancing social life With academics can be difficult, that is why I'm so glad that OneClass is out there where I can find the top notes for all of my classes. Now I can be the all-star student I want to be.

Saarim — University of Michigan
Jenna — University of Wisconsin

As a college student living on a college budget, I love how easy it is to earn gift cards just by submitting my notes.

Jenna — University of Wisconsin
Anne — University of California

OneClass has allowed me to catch up with my most difficult course! #lifesaver

Anne — University of California
Description
Tuesday January 11, 2011 Regression equation: y = a + bx y = the predicted value, or the dependent variable and x = the independent variable a = intercept (y when x = 0 ) b = the slope of a line were going to discuss this more next week regression and correlation fit together in a way, since correlation is defined by a regression equation people think interval data is more powerful than nominal or ordinal.... bivariate analysis: if we want to predict y from x the scores from x must be at least as efficient as the scores of y as the mean of y itself. So we have two axes and the idea is that theres one independent and one dependent variable. These variables have ranks that some things are higher than others, and that theres a constant interval between each of the units. Its measured in some set of units i.e. x = years of education. Now the idea here is that we can have a number of individual cases that are ranked on x or ranked on y. the best predictor of y (just y, alone) would be whatever the mean score of y is. and the reason that it s the best predictor is that y would give us the smallest average deviation of the mean of y. so the mean of the sample becomes the standard of which we compare any other variable. so the square deviation of the mean score will let us decide if x is useful. pg. 309 in brians (only just touched upon on that page). theres more than one kind of variance or variation. in fact theres 3 we need to worry about... Types of Variation 1) Total Variance - It equals the sum of the square differences from the mean on any variable - The average squared deviation = the total variation of the y variable - The least squares line 2) Unexplained Variance - Everything thats left over (that doesnt touch the regression slope aka the equation y = a + bx) 3) Explained Variance - Total variation of y that can be attributed to the influence of x - Pearsons correlation = the square root of the variance. - So r = the square root of explained variance. www.notesolution.com
More Less
Unlock Document


Only page 1 are available for preview. Some parts have been intentionally blurred.

Unlock Document
You're Reading a Preview

Unlock to view full version

Unlock Document

Log In


OR

Don't have an account?

Join OneClass

Access over 10 million pages of study
documents for 1.3 million courses.

Sign up

Join to view


OR

By registering, I agree to the Terms and Privacy Policies
Already have an account?
Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.


Submit