Transcript Summary Statistics When analysing practical sets of data

Regression
[Example 3.] As a motivating example, suppose we are modelling sales data over time.
SALES
3
5
4
5
6
7
TIME 1990
1991
1992
1993
1994
1995
Y=mX+c
We seek the straight line “Y = m X + c” that best
Y
Yi
approximates the data. By “best” in this case, we
mean the line which minimizes the sum of squares
of vertical deviations of points from the line:
m Xi + c
SS = S ( Yi - [ mXi + c ] ) 2.
Setting the partial derivatives of SS with respect to m
and c to zero leads to the “normal equations”
0
X
S Y = m S X + n .c
, where n = # points
Xi
S X .Y= m S X2 + c S X .
Let 1990 correspond to Year 0.
X.X
0
1
4
9
16
25
X
0
1
2
3
4
5
X.Y
0
5
8
15
24
35
Y
3
5
4
5
6
7
Y.Y
9
25
16
25
36
49
55 15
87
30
160
Sales
10
5
Time
0
5
Example 3 - Workings.
The normal equations are:
30 = 15 m + 6 c =>
87 = 55 m + 15 c
=>
24 = 35 m
=>
150 = 75 m + 30 c
174 = 110 m + 30 c
30 = 15 (24 / 35) + 6 c
=>
c = 23/7
Thus the regression line of Y on X is
Y = (24/35) X + (23/7)
and to plot the line we need two points, so
X = 0 => Y = 23/7
and X = 5 => Y = (24/35) 5 + 23/7 = 47/7.
It is easy to see that ( X, Y ) satisfies the normal equations, so that the regression line of Y on
X passes through the “Center of Gravity” of the data. By expanding terms, we also get
S ( Yi - Y ) 2 = S ( Yi - [ m Xi + c ] ) 2
Total Sum
of Squares
SST
=
ErrorSum
of Squares
SSE
+
+
S ( [ m Xi + c ] - Y ) 2
Y
Regression Sum
of Squares
SSR
In regression, we refer to the X variable as the independent
variable and Y as the dependent variable.
Yi
mXi +C
Y
Y
X
X
Correlation
The coefficient of determination r2 ( which takes values in the range 0 to 1) is a measure of
the proportion of the total variation that is associated with the regression process:
r2
=
SSR/ SST
=
1 - SSE / SST.
The coefficient of correlation r ( which takes values in the range -1 to +1 ) is more
commonly used as a measure of the degree to which a mathematical relationship exists
between X and Y. It can be calculated from the formula:
r =
S(X-X)(Y-Y)
S
=
( X - X )2 ( Y - Y ) 2
nSXY-S XSY
 { n S X 2 - ( S X )2 } { n S Y2 - ( S Y)2 }
Example. In our case r = {6(87) - (15)(30)}/  { 6(55) - (15)2 } { 6(160) - (30)2 } = 0.907.
r=-1
r=0
r=+1
Collinearity
If the value of the correlation coefficient is greater than 0.9 or less than - 0.9, we would take
this to mean that there is a mathematical relationship between the variables. This does not
imply that a cause-and-effect relationship exists.
Consider a country with a slowly changing population size, where a certain political party
retains a relatively stable percentage of the poll in elections. Let
X = Number of people that vote for the party in an election
Y = Number of people that die due to a given disease in a year
Z = Population size.
Then, the correlation coefficient between X and Y is likely to be close to 1, indicating that
there is a mathematical relationship between them (i.e.) X is a function of Z and Y is a
function of Z also. It would clearly be silly to suggest that the incidence of the disease is
caused by the number of people that vote for the given political party. This is known as the
problem of collinearity.
Spotting hidden dependencies between distributions can be difficult. Statistical
experimentation can only be used to disprove hypotheses, or to lend evidence to support the
view that reputed relationships between variables may be valid. Thus, the fact that we
observe a high correlation coefficient between deaths due to heart failure in a given year
with the number of cigarettes consumed twenty years earlier does not establish a cause-andeffect relationship. However, this result may be of value in directing biological research in a
particular direction.