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Discussion section 227
09/21/10
Regression
•
Regression is a statistical tool that allows us to predict
scores of one variable
based on the other variable.
•
We are assuming that the relationship between the two variables can best be
described by a
straight line
•
The line that passes through our data points with the minimum amount of error
(distance between actual points and the line) is called
line of best fit
The regression line equation
The equation describing a straight line is Y = a + b(x) where b is the slope and a is the Y
intercept. We can use Z scores to compute this equation
c
Intercept:
predicted value of
Y
when
X
is equal to zero
c
Slope: the amount that
Y
is predicted to increase for an increase of 1 in
X
Example 1:
Student
# of absences (X)
Statistics test scores (Y)
1
2
85
2
1
90
3
4
78
4
0
95
5
3
80
M
X
= 2.0
M
Y
= 85.6
SD
X
= 1.58
SD
Y
= 7.02
r = 0.63
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View Full Document Computing the regressio
1.
Find the Y intercept –
I.
Convert raw sco
II.
Find Z(Y hat)
III.
Convert to raw s
0.80*7.02+85.6
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This note was uploaded on 02/21/2012 for the course PYSC 227 taught by Professor Fairchild during the Fall '10 term at South Carolina.
 Fall '10
 Fairchild

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