Correlation and Regression
Correlation
A quantitative relationship between two interval or ratio
level variables
Explanatory
(Independent) Variable
x
Response
(Dependent) Variable
y
Hours of Training
Number of Accidents
Shoe Size
Cigarettes smoked per day
Score on SAT
Height
Height
Lung Capacity
Grade Point Average
IQ
What type of relationship exists between the two
variables and is the correlation significant?
Correlation
 measures and describes the strength and direction of
the relationship
 Bivariate techniques requires two variable scores
from the same individuals (dependent and
independent variables)
 Multivariate when more than two independent
variables (e.g effect of advertising and prices on
sales)
 Variables must be ratio or interval scale
Scatter Plots and Types of Correlation
x = hours of training (horizontal axis)
y = number of accidents (vertical axis)
Accidents
60
50
40
30
20
10
0
0
2
4
6
8
10 12 14 16 18
20
Hours of Training
Negative Correlation–as x increases, y decreases
Scatter Plots and Types of Correlation
GPA
x = SAT score
y = GPA
4.00
3.75
3.50
3.25
3.00
2.75
2.50
2.25
2.00
1.75
1.50
300 350 400 450 500 550 600 650 700 750 800
Math SAT
Positive Correlation–as x increases, y increases
Scatter Plots and Types of Correlation
IQ
x = height
y = IQ
160
150
140
130
120
110
100
90
80
60
64
68
72
Height
No linear correlation
76
80
Scatter Plots and Types of Correlation
Strong, negative
relationship
but non-linear!
Correlation Coefficient
A measure of the strength and direction of a linear
relationship between two variables
The range of r is from –1 to 1.
–1
If r is close to –1
there is a strong
negative
correlation.
0
If r is close to
0 there is no
linear
correlation.
1
If r is close to
1 there is a
strong
positive
correlation.
Outliers.....
Outliers are dangerous
Here we have a spurious
correlation of r=0.68
without IBM, r=0.48
without IBM & GE,
r=0.21
Application
Final Grade
Final
Absences Grade
95
90
85
80
75
70
65
60
55
50
45
40
0
2
4
6
8
10
Absences
X
12
14
16
x
8
2
5
12
15
9
6
y
78
92
90
58
43
74
81
Computation of r
1
2
3
4
5
6
7
x
y
8
2
5
12
15
9
6
78
92
90
58
43
74
81
xy
624
184
450
696
645
666
486
x2
64
4
25
144
225
81
36
y2
6084
8464
8100
3364
1849
5476
6561
57 516
3751
579
39898
Hypothesis Test for Significance
r is the correlation coefficient for the sample. The
correlation coefficient for the population is
(rho).
For a two tail test for significance:
(The correlation is not significant)
(The correlation is significant)
The sampling distribution for r is a t-distribution
with n – 2 d.f.
Standardized test
statistic
Test of Significance
The correlation between the number of times absent and a
final grade r = –0.975. There were seven pairs of data.Test the
significance of this correlation. Use
= 0.01.
1. Write the null and alternative hypothesis.
(The correlation is not significant)
(The correlation is significant)
2. State the level of significance.
= 0.01
3. Identify the sampling distribution.
A t-distribution with 5 degrees of freedom
Rejection Regions
Critical Values ± t0
t
–4.032
0
4.032
df\p
0.40
0.25
0.10
0.05
0.025
0.01
0.005
0.0005
1
0.324920
1.000000
3.077684
6.313752
12.70620
31.82052
63.65674
636.6192
2
0.288675
0.816497
1.885618
2.919986
4.30265
6.96456
9.92484
31.5991
5. Find the rejection region. 3
0.276671
0.764892
1.637744
2.353363
3.18245
4.54070
5.84091
12.9240
4
0.270722
0.740697
1.533206
2.131847
2.77645
3.74695
4.60409
8.6103
5
0.267181
0.726687
1.475884
2.015048
2.57058
3.36493
4.03214
6.8688
4. Find the critical value.
6. Find the test statistic.
t
–4.032
0
–4.032
7. Make your decision.
t = –9.811 falls in the rejection region. Reject the null hypothesis.
8. Interpret your decision.
There is a significant negative correlation between
the number of times absent and final grades.
The Line of Regression
Regression indicates the degree to which the variation in one
variable X, is related to or can be explained by the variation
in another variable Y
Once you know there is a significant linear correlation, you
can write an equation describing the relationship between the
x and y variables. This equation is called the line of regression
or least squares line.
The equation of a line may be written as y = mx + b
where m is the slope of the line and b is the yintercept.
The line of regression is:
The slope m is:
The y-intercept is:
(xi,yi)
= a data point
= a point on the line with the same x-value
= a residual
Best fitting straight line
260
revenue
250
240
230
220
210
200
190
180
1.5
2.0
Ad $
2.5
3.0
1
2
3
4
5
6
7
x
8
2
5
12
15
9
6
xy
y
78
92
90
58
43
74
81
624
184
450
696
645
666
486
57 516
3751
x2
64
4
25
144
225
81
36
y2
6084
8464
8100
3364
1849
5476
6561
579 39898
The line of regression is:
Write the equation of the
line of regression with
x = number of absences
and y = final grade.
Calculate m and b.
= –3.924x + 105.667
The Line of Regression
Final Grade
m = –3.924 and b = 105.667
The line of regression is:
95
90
85
80
75
70
65
60
55
50
45
40
0
2
4
6
8
10
12
14
16
Absences
Note that the point
= (8.143, 73.714) is on the line.
Predicting y Values
The regression line can be used to predict values of y
for values of x falling within the range of the data.
The regression equation for number of times absent and final grade is:
= –3.924x + 105.667
Use this equation to predict the expected grade for a student with
(a) 3 absences
(b) 12 absences
(a)
= –3.924(3) + 105.667 = 93.895
(b)
= –3.924(12) + 105.667 = 58.579
Strength of the Association
The coefficient of determination, r2, measures the strength of the
association and is the ratio of explained variation in y to the
total variation in y.
The correlation coefficient of number of times absent and
final grade is r = –0.975. The coefficient of determination is
r2 = (–0.975)2 = 0.9506.
Interpretation: About 95% of the variation in final grades can
be explained by the number of times a student is absent. The
other 5% is unexplained and can be due to sampling error or
other variables such as intelligence, amount of time studied, etc.