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The plan. Practice – Correlation A straight line A regression equation Practice! A quicker way to compute a correlation. Practice. Interpret the following: 1) The correlation between vocational-interest scores at age 20 and at age 40 was .70. 2) Age and IQ is correlated -.16.

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the plan
The plan
  • Practice – Correlation
  • A straight line
  • A regression equation
  • Practice!
  • A quicker way to compute a correlation
practice
Practice
  • Interpret the following:
  • 1) The correlation between vocational-interest scores at age 20 and at age 40 was .70.
  • 2) Age and IQ is correlated -.16.
  • 3) The correlation between IQ and family size is -.30.
  • 4) The correlation between sexual promiscuity and dominance is .32.
  • 5) In a sample of males happiness and height is correlated .11.
sleeping and happiness
Sleeping and Happiness
  • You are interested in the relationship between hours slept and happiness.
  • 1) Make a scatter plot
  • 2) Guess the correlation
  • 3) Guess and draw the location of the regression line
slide6

.

.

.

.

.

sleeping and happiness1
Sleeping and Happiness
  • 4) Compute the correlation
  • Hours Slept

M = 7.0

SD = 1.4

  • Happiness

M = 6.8

SD = 1.7

blanched formula
Blanched Formula

r =

XY = 247

X = 7.0

Y = 6.8

Sx = 1.4

Sy = 1.7

N = 5

blanched formula1
Blanched Formula

247

r =

XY = 247

X = 7.0

Y = 6.8

Sx = 1.4

Sy = 1.7

N = 5

blanched formula2
Blanched Formula

247

7.0

6.8

r =

XY = 247

X = 7.0

Y = 6.8

Sx = 1.4

Sy = 1.7

N = 5

blanched formula3
Blanched Formula

247

7.0

6.8

5

.76 =

1.4

1.7

XY = 247

X = 7.0

Y = 6.8

Sx = 1.4

Sy = 1.7

N = 5

slide12

.

.

.

.

.

r = .76

remember this statistics needed
Remember this:Statistics Needed
  • Need to find the best place to draw the regression line on a scatter plot
  • Need to quantify the cluster of scores around this regression line (i.e., the correlation coefficient)
straight line
Straight Line

Y = mX + b

Where:

Y and X are variables representing scores

m = slope of the line (constant)

b = intercept of the line with the Y axis (constant)

that s nice but
That’s nice but. . . .
  • How do you figure out the best values to use for m and b ?
  • First lets move into the language of regression
straight line1
Straight Line

Y = mX + b

Where:

Y and X are variables representing scores

m = slope of the line (constant)

b = intercept of the line with the Y axis (constant)

regression equation
Regression Equation

Y = a + bX

Where:

Y = value predicted from a particular X value

a = point at which the regression line intersects the Y axis

b = slope of the regression line

X = X value for which you wish to predict a Y value

practice1
Practice
  • Y = -7 + 2X
  • What is the slope and the Y-intercept?
  • Determine the value of Y for each X:
  • X = 1, X = 3, X = 5, X = 10
practice2
Practice
  • Y = -7 + 2X
  • What is the slope and the Y-intercept?
  • Determine the value of Y for each X:
  • X = 1, X = 3, X = 5, X = 10
  • Y = -5, Y = -1, Y = 3, Y = 13
finding a and b
Finding a and b
  • Uses the least squares method
  • Minimizes Error

Error = Y - Y

 (Y - Y)2 is minimized

slide23

.

.

.

.

.

slide24

Error = Y - Y

 (Y - Y)2 is minimized

.

Error = 1

.

Error = .5

.

.

Error = -1

.

Error = 0

Error = -.5

finding a and b1
Finding a and b
  • Ingredients
  • r value between the two variables
  • Sy and Sx
  • Mean of Y and X
slide26
b

b =

r = correlation between X and Y

SY = standard deviation of Y

SX = standard deviation of X

slide27
a

a = Y - bX

Y = mean of the Y scores

b= regression coefficient computed previously

X = mean of the X scores

mean y 4 6 s y 2 41 r 88 mean x 3 0 s x 1 41 b 1 51
Mean Y = 4.6; SY = 2.41 r = .88Mean X = 3.0; SX = 1.41 b = 1.5

0.1 = 4.6 - (1.50)3.0

regression equation1
Regression Equation

Y = a + bX

Y = 0.1 + (1.5)X

mean y 14 50 s y 4 43 mean x 6 00 s x 2 16 b 1 171
Mean Y = 14.50; Sy = 4.43Mean X = 6.00; Sx= 2.16 b = -1.17

21.52= 14.50 - (-1.17)6.0

regression equation2
Regression Equation

Y = a + bX

Y = 21.52 + (-1.17)X

y 21 52 1 17 x
Y = 21.52 + (-1.17)X

.

22

20

.

18

16

.

14

.

12

10

y 21 52 1 17 x1
Y = 21.52 + (-1.17)X

.

.

22

20

.

18

16

.

14

.

12

10

y 21 52 1 17 x2
Y = 21.52 + (-1.17)X

.

.

22

20

.

18

16

.

14

.

.

12

10

y 21 52 1 17 x3
Y = 21.52 + (-1.17)X

.

.

22

20

.

18

16

.

14

.

.

12

10

practice4
Practice
  • How much money would likely be earned if an advertiser spent $2,000 (i.e., x = 2)?
  • How much money would likely be earned if an advertiser spent $10,000 (i.e., x = 10)?
blanched formula4
Blanched Formula

r =

XY = 3360

X = 4.29

Y = 108.57

Sx = 1.03

Sy = 20.30

N = 7

blanched formula5
Blanched Formula

3360

14.5

(4.29) (108.57)

7

.68 =

2.16

4.43

(1.03) (20.30)

XY = 3360

X = 4.29

Y = 108.57

Sx = 1.03

Sy = 20.30

N = 7

mean y 108 57 s y 20 30 mean x 4 29 s x 1 03 b 13 401
Mean Y = 108.57; Sy = 20.30Mean X = 4.29; Sx= 1.03 b = 13.40

51.08 = 108.57 - (13.40)4.29

regression equation3
Regression Equation

Y = a + bX

Y = 51.08 + (13.40)X

y 51 08 13 40 x
Y = 51.08 + (13.40)X
  • How much money would likely be earned if an advertiser spent $2,000?
  • How much money would likely be earned if an advertiser spent $10,000?
y 51 08 13 40 x1
Y = 51.08 + (13.40)X
  • How much money would likely be earned if an advertiser spent $2,000?
  • 77.88 = 51.08 + (13.40)2
  • $77, 880
y 51 08 13 40 x2
Y = 51.08 + (13.40)X
  • How much money would likely be earned if an advertiser spent $10,000?
  • 185.08 = 51.08 + (13.40)10
  • $185,080
blanched formula6
Blanched Formula
  • Good way to calculate r if the means and standard deviations are already provided.
  • It is very time consuming to calculate these statistics if they are not already provided
  • If means and standard deviations are not given, use the raw-score formula
step 6 plug in values
Step 6: Plug in values

r =

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 6 plug in values1
Step 6: Plug in values

15

r =

15

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 6 plug in values2
Step 6: Plug in values

15

23

r =

15

23

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 6 plug in values3
Step 6: Plug in values

84

15

23

r =

15

23

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 6 plug in values4
Step 6: Plug in values

84

15

23

r =

55

15

23

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 6 plug in values5
Step 6: Plug in values

84

15

23

r =

55

135

15

23

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 6 plug in values6
Step 6: Plug in values

(5)

84

15

23

r =

(5)

55

(5)

135

15

23

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve
Step 7: Solve!

(5)

84

15

23

r =

(5)

55

(5)

135

15

23

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve1
Step 7: Solve!

(5)

84

15

23

r =

(5)

55

(5)

135

23

225

529

225

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve2
Step 7: Solve!

420

345

23

23

15

r =

275

23

225

675

529

225

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve3
Step 7: Solve!

420

345

23

23

15

r =

50

23

146

225

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve4
Step 7: Solve!

75

23

23

15

r =

146

23

7300

50

225

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve5
Step 7: Solve!

75

23

23

15

r =

85.44

146

23

7300

50

225

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

step 7 solve6
Step 7: Solve!

75

23

23

15

.88 =

85.44

146

23

7300

50

225

Y = 23 Y2 = 135

X = 15 X2 = 55

XY = 84 N = 5

practice8
Practice

(4)

72

20

19

r =

(4)

120

(4)

123

20

19

X = 20 X2 = 120

Y = 19 Y2 = 123

XY = 72 N = 4

practice9
Practice

-92

20

19

r =

(4)

120

(4)

123

20

19

X = 20 X2 = 120

Y = 19 Y2 = 123

XY = 72 N = 4

practice10
Practice

-92

20

19

r =

80

131

X = 20 X2 = 120

Y = 19 Y2 = 123

XY = 72 N = 4

practice11
Practice

-92

20

19

-.90 =

102.37

80

131

X = 20 X2 = 120

Y = 19 Y2 = 123

XY = 72 N = 4

practice12
Practice
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