Question

pic posted

Answer 1

Hint: the residual for a perfect correlation would be zero at all the data points.

Answer 2

so A is answer?

Answer 3

any others?

Answer 4

@mathmate

Answer 5

@pgpilot326 help pls?

Answer 6

In general, a residual is the difference between the actual value of a dependent variable (DV) and the value of variable that was predicted by a statistical model. In the context of a regression, a residual is how far a predicted value (as determined by the predictors in the model) is from the actual value of the dependent variable. This is also called an "error term" (because it represents how much your regression model was in error, in terms of its ability to predict the value of the DV).

In any statistical model, a residual can be thought of in various contexts. For example, each person will have their own "residual" score (which is simply the difference between the actual and predicted value of the DV), while the model as a whole also has a residual score (which represents how much variability in the DV remained 'unexplained' by the predictors in the model). A residual score serves several purposes, including: 1) determining the accuracy of your model (how much variability is explained by the model) and 2) being used to test the assumptions inherent in the regression analysis (such as the assumption that the residuals are normally distributed OR that their variance is equal across all levels of the predicted value of the DV).

from http://www.statsmakemecry.com/stats-questions/what-are-residuals-in-regression-how-we-can-find-residuals-r.html

Answer 7

so A is an answer for sure

Answer 8

read, don't jump to conclusions

Answer 9

but

Answer 10

so what answers would u say

Answer 11

anyone know this?

Answer 12

The answer is explicitly stated in @pgpilot326 's text.
If you read through the whole text, you cannot miss it.

Answer 13

ok.

Answer 14

thank you @mathmate but I'm afraid the student might not want to actually learn

Answer 15

I think he/she is coming along. Wait and see! :)

Answer 16

ok so C is one

Answer 17

i hope so

Answer 18

Did you find the relevant definition?

Answer 19

yes its option C

Answer 20

there may be more than one correct choice

Answer 21

yea

Answer 22

could it be D?

Answer 23

Just for your information, yhat represents the values predicted by the regression line, and y represents the observed values.
Does this help you make up your mind?

Answer 24

oh the mean?

Answer 25

is it the mean?

Answer 26

Hope you see better with this.

Answer 27

thts like the points... or the values that are on the regression line right?

Answer 28

yHat represents the predicted value
y represents the observed value
What can you then say about y-yHat?

Answer 29

observed value minus predicted value

Answer 30

and The predicted values are calculated from the estimated regression equation

Answer 31

residuals are calculated as the observed minus the predicted value

Answer 32

Keep going!

Answer 33

so then that means option D is also an answer

Answer 34

because it describes a residual

Answer 35

There you go! Good job!

Answer 36

so it would be C & D

Answer 37

thank u

Answer 38

yw!