(Exam 2) SimpleRegression 11

(Exam 2) SimpleRegression 11 - Simple Linear Regression...

Info iconThis preview shows pages 1–23. Sign up to view the full content.

View Full Document Right Arrow Icon
Simple Linear Regression Prediction Using One Predictor
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Prediction
Background image of page 2
Pearson
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Scatterplot: TWO variables Relationship between TWO continuous variables Grades and Studying Age and Memory Height and Weight
Background image of page 4
1 – draw the axes and decide which variable goes on which axis Hours per week on Facebook Quality of Facebook relationships
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 – determine the range of values to use for each variable and mark them on the axes Hours per week on Facebook Quality of Facebook relationships 2 4 6 8 10 14 12 18 16 20 0 1 2 3 4 5
Background image of page 6
3 – Mark a dot for each pair of scores Hours per week on Facebook Quality of Facebook relationships 2 4 6 8 10 14 12 18 16 20 0 1 2 3 4 5 Hrs Rate 10 5 20 4 5 3 2 1
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Scatter Diagram Hours per week on Facebook Quality of Facebook relationships 2 4 6 8 10 14 12 18 16 20 0 1 2 3 4 5 Hrs Rate 10 5 20 4 5 3 2 1
Background image of page 8
Draw a line that best represents the dots Hours per week on Facebook Quality of Facebook relationships 2 4 6 8 10 14 12 18 16 20 0 1 2 3 4 5 Hrs Rate 10 5 20 4 5 3 2 1
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Line graphs Linear (straight line) Curvlinear (U-shaped) Good for predictions
Background image of page 10
Regression (Prediction) Line Slope: how steep the line is and in what direction When using z-scores, slope = correlation Y-intercept: where the line crosses the y- axis; value of Y when X=0
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Vocabulary Predictor (X) Criterion (Y) Regression Coefficients (B and β) Standardized: from z-scores, Betas Unstandardized: from raw scores Proportion of variance accounted for aka Coefficient of determination
Background image of page 12
http://www.youtube.com/watch#!v=MsrMIOlYAPM&feature=related
Background image of page 13

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Prediction Question: How much will the LOST audience enjoy THE EVENT? ?
Background image of page 14
NBC’s Focus Group? To what extent did you enjoy watching LOST? To what extent did you enjoy watching the premiere of THE EVENT? 1 = a little enjoyment 2 = some enjoyment 3 = enjoyment 4 = much enjoyment 5 = a lot of enjoyment
Background image of page 15

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
X = LOST enjoyment Y = THE EVENT enjoyment 1 2 1 3 2 1 3 4 3 5 4 5 4 3 4 2 4 1 5 4
Background image of page 16
X-M -2.1 -2.1 -1.1 -0.1 -0.1 0.9 0.9 0.9 0.9 1.9 Y-M -1 0 -2 1 2 2 0 -1 -2 1 (X-M)(Y-M) 2.1 0 2.2 -0.1 -0.2 1.8 0 -0.9 -1.8 1.9 ∑(X-M x )(Y-M Y ) = 5, n = 10 Covariance = .5
Background image of page 17

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
X-M -2.1 -0.1 -0.1 0.9 0.9 1.9 Y-M -1 1 2 2 -2 1 (X-M)(Y-M) 2.1 1.8 Below the mean on X (negative) Below the mean on Y (negative) Negative times Negative = Positive (a match!) Above the mean on X (positive) Above the mean on Y (positive) Positive times Positive = Positive (a match!)
Background image of page 18
X-M -2.1 -2.1 -1.1 -0.1 0.9 0.9 0.9 1.9 Y-M -1 0 -2 1 0 -1 -2 1 (X-M)(Y-M) 2.1 0 2.2 -0.1 -1.8 1.9 Below the mean on X (negative) Above the mean on Y (positive) Positive times Negative = (NO match!) Above the mean on X (positive) Below the mean on Y (negative) Negative times Positive = (NO match!)
Background image of page 19

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Match or No Match? Match + Neither Match + No Match - No Match - Match + Neither No Match - No Match - Match + Extent of Match large large small small large small large large (X-M)(Y-M) +2.1 0 2.2 -0.1 -0.2 +1.8 0 -0.9 -1.8 +1.9 Taken all together, the matches are larger than the non-matches, so a positive covariance
Background image of page 20
Covariance to Correlation Can’t compare covariance across studies Covariance dependent on variance of X and variance of Y Standardize covariance – put covariance on a common metric Standardize by dividing by standard deviations ( 29 ( 29 ) )( )( ( ] [ Y X Y X s s N M Y M X - -
Background image of page 21

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Covariance to Correlation 272 .
Background image of page 22
Image of page 23
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 05/26/2011 for the course PSCH 343 taught by Professor Victoriaharmon during the Spring '11 term at Ill. Chicago.

Page1 / 67

(Exam 2) SimpleRegression 11 - Simple Linear Regression...

This preview shows document pages 1 - 23. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online