Univariate
Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data and find patterns that exist within it.
Example: height of students
|
Height (in cm) |
|
164 |
|
167.3 |
|
170 |
|
174.2 |
|
178 |
|
180 |
The patterns can be studied by drawing conclusions using mean, median, mode, dispersion or range, minimum, maximum, etc.
Bivariate
Bivariate data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to determine the relationship between the two variables.
Example: temperature and ice cream sales in the summer season
|
Temperature (in Celcius) |
Sales |
|
20 |
2,000 |
|
25 |
2,100 |
|
26 |
2,300 |
|
28 |
2,400 |
|
30 |
2,600 |
|
36 |
3,100 |
Here, the relationship is visible from the table that temperature and sales are directly proportional to each other. The hotter the temperature, the better the sales.
Multivariate
Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a bivariate but contains more than one dependent variable.
Example: data for house price prediction
|
No. of rooms |
Floors |
Area (sq ft) |
Price |
|
2 |
0 |
900 |
$4000,00 |
|
3 |
2 |
1,100 |
$600,000 |
|
3.5 |
5 |
1,500 |
$900,000 |
|
4 |
3 |
2,100 |
$1,200,000 |
The patterns can be studied by drawing conclusions using mean, median, and mode, dispersion or range, minimum, maximum, etc. You can start describing the data and using it to guess what the price of the house will be.
