2. INTRODUCTION
• The Karl Pearson coefficient is defined
as a linear correlation that falls in the
numeric range of -1 to +1.
• The Pearson’s correlation coefficient is
usually calculated for two continuous
variables.
• A correlation could be positive, or
negative or zero.
3. TYPES OF COEFFICIENT
POSITIVE CORRELATION
It exists when one variable
tends to decrease as the
other variable decreases, or
one variable tends to
increase when the other
increases.
Ex: Increased price of fuel
and transportation.
NEGATIVE CORRELATION
Negative or inverse
correlation exists when two
variables tends to move in
opposite side and direction,
such that when one increases
the other variable decreases,
and vice-versa.
Ex: height above sea level and
temperature.
ZERO CORRELATION
A zero correlation suggests that
the correlation statistics does
not indicate a linear
relationship between the two
variables.
Ex: The amount of coffee an
individual consumes has zero
correlation with their IQ level.
7. ASSUMPTIONS OF KARL PEARSON’S CORRELATION
COEFFICIENT
The assumption and requirement for calculating Pearson’s correlation
of coefficient are as follows:
oThe data set should approximate to the normal distribution.
oData is homoscedastic if the point lies equally on both sides.
oData satisfy the condition of linearity.
oThe dataset must contain continuous variable.
oThe data points must be in paired observations.
oThere must be no outliner in the data.
8. LIMITATIONS
It cannot distinguish between dependent
and independent variables.
Pearson’s ‘r’ can not indicate the cause and
effect of the variable.
Pearson’s cannot be used to determine the
nonlinear relationship.
The slope must be found by creating a
scatter plot.
