Bivariate Analysis (07)
Table of contents
What is BA?
Bivariate analysis is a statistical technique that involves the analysis of two variables to determine the relationship between them. It is used to understand how changes in one variable are related to changes in another variable.
Types of BA
Under Bivariate analysis, we’ve three kinds of analysis
Numerical-Numerical Analysis
Numerical-Categorical Analysis
Categorical-Categorical Analysis
Thus, Let’s understand one by one all these things in detail.
From the title, it’s clear that Numerical-Numerical Analysis states that when both variables are continuous.
Under this, we’ve two important concepts that are Correlation and Causation
Correlation vs Causation
Correlation: Correlation refers to a statistical relationship between two variables. When two variables are correlated, it means that a change in one variable is associated with a change in the other variable.
A Few Examples
For example, let's say we are interested in examining the relationship between a person's age and income. If we find that as a person's age increases, their income also tends to increase, then we would say that age and income are positively correlated.
On the other hand, if we find that as a person's age increases, their income tends to decrease, then we would say that age and income are negatively correlated.
There are different ways to measure the strength and direction of correlation, but one common method is to use a correlation coefficient, which is a numerical value that ranges from -1 to +1. A correlation coefficient of +1 indicates a perfect positive correlation, a correlation coefficient of -1 indicates a perfect negative correlation, and a correlation coefficient of 0 indicates no correlation.
Here's another example: Let's say we are interested in examining the relationship between a student's attendance and their grades. If we find that students who attend more classes tend to have higher grades, then we would say that attendance and grades are positively correlated. If we find that students who attend fewer classes tend to have higher grades, then we would say that attendance and grades are negatively correlated.
In summary, correlation is a statistical relationship between two variables that helps us to understand how changes in one variable are associated with changes in another variable.
Causation: Causation refers to a relationship between two variables in which one variable directly causes the other variable to change. Unlike correlation, which only shows an association between two variables, causation involves a cause-and-effect relationship.
For example, let's say we are interested in examining the relationship between smoking and lung cancer. If we find that people who smoke are more likely to develop lung cancer than people who do not smoke, then we would say that smoking causes lung cancer.
However, establishing causation requires more than just observing a correlation between two variables. It also requires ruling out other potential factors that could be influencing the relationship. For example, in smoking and lung cancer example, we would need to rule out other factors that could be causing lung cancer, such as exposure to air pollution or genetic predisposition.
To establish causation, researchers often use experimental designs in which they manipulate one variable and observe the effect on another variable while controlling for other factors. For example, in a randomized controlled trial, researchers might randomly assign participants to either a smoking or non-smoking group and then observe the incidence of lung cancer over time.
In summary, causation refers to a direct cause-and-effect relationship between two variables. Establishing causation requires ruling out other potential factors and often involves experimental designs.
In the next Blog, I'll explain to you all the same Bivariate Analysis Through python codes to get a thorough understanding of the same!
That's the end of the article readers!
Will be explaining more in my following blogs!
"In God we trust, all others must bring data." - W. Edwards Deming
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