1. Simple (Linear) correlation
2. Partial correlation (more than 2 variables)
3. Multiple correlation (more than 2 variables)

**TYPES OF CORRELATION**

**1. Simple (Linear)
correlation **(2 variables only) : The correlation between the given two
variables.

**2. Partial correlation (more than 2
variables): **The

**3. Multiple
correlation (more than 2 variables) : **The correlation between a group of variables and

In this chapter, we study simple correlation only, multiple
correlation and partial correlation involving three or more variables will be
studied in higher classes .

Here, we are dealing with data involving two related variables and
generally we assign a symbol ‘ *x* ’ to scores of one variable and symbol
‘*y* ’ to scores of the other variable. There are five types in simple
correlation. They are

1) Positive correlation (Direct correlation)

2) Negative correlation (Inverse correlation)

3) Uncorrelated

4) Perfect positive correlation

5) Perfect negative correlation

The variables are said to be positively correlated if larger
values of x are associated with larger values of y and smaller values of x are associated with smaller values of *y*. In other words, if both the variables are varying in the *same direction *then the correlation is said to be positive.

In other words, if one variable
increases, the other variable (on an average) also increases or if one variable decreases, the other (on an average)variable also
decreases.

For example,

i) Income and savings

ii) Marks in Mathematics and Marks in Statistics. (*i.e.,*Direct
relationship pattern exists).

The variables are said to be negatively correlated if smaller
values of *x* are associated with larger values of *y* or larger
values *x* are associated with smaller values of *y*. That is the
variables varying in the ** opposite directions** is said to be
negatively correlated. In other words, if one variable increases the other
variable decreases and vice versa.

For example,

i) Price and demand

ii) Unemployment and purchasing power

The variables are said to be uncorrelated if smaller values of *x*
are associated with smaller or larger values of *y* and larger values of *x*
are associated with larger or smaller values of *y*. If the two variables
do not associate linearly, they are said to be uncorrelated. Here *r* = 0.

** Important note: **Uncorrelated does not imply independence. This
means “do not interpret as the two variables are independent instead interpret
as there is no specific linear pattern exists but there may be non linear
relationship”.

If the values of *x* and *y* increase or decrease ** proportionately**
then they are said to have perfect positive correlation.

If *x* increases and *y* decreases ** proportionately**
or if

The purpose of correlation analysis is to find the existence of
linear relationship between the variables. However, the method of calculating
correlation coefficient depends on the types of measurement scale, namely,
ratio scale or ordinal scale or nominal scale.

**Statistical tool selection**

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail

12th Statistics : Chapter 4 : Correlation Analysis : Types of Correlation |

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