Bivariate Analysis: An Easy Guide For Beginners In 2021

Introduction

Bivariate analysis lets you study the relationship that exists between two variables. This has a lot of use in real life. It helps to find out if there is an association between the variables and if yes then what is the strength of association.

The bivariate analysis helps to test the hypothesis of casualty and association. It helps to predict the value of a variable that is dependent based on changes that happen to an independent variable.

1) What does bivariate analysis mean?

Bivariate analysis means the analysis of the bivariate data. This is a single statistical analysis that is used to find out the relationship that exists between two value sets. The variables that are involved are X and Y.

• Univariate analysis is when only one variable is analyzed.
• Bivariate data analysis is when exactly two variables are analyzed.
• Multivariate analysis is when more than two variable get analyzed.

The results that are obtained from the bivariate analysis are stored in a data table that has two columns. Bivariate analysis should not be confused with two sample data analysis where the x and y variables are not related directly.

2) How do you conduct a bivariate analysis?

Here is how the bivariate analysis is carried out.

• Scatter plots – This gives an idea of the patterns that can be formed using the two variables
• Regression Analysis – This uses a wide range of tools to determine how the data post could be related. The post may follow an exponential curve. The regression analysis gives the equation for a line or curve. It also helps to find the correlation coefficient.
• Correlation Coefficients –The coefficient lets you know if the data in question are related. When the correlation coefficient is zero then this means that the variables are not related. If the correlation coefficient is a positive or a negative 1 then this means that the variables are perfectly correlated.

3) How many types of bivariate correlations are there?

The kind of bivariate analysis is dependent on the kind of attributes and variables that is used to analyze the data. The variables may be ordinal, categorical, or numeric. The independent variable is categorical like a brand of a pen. In this case, probit regression or logit regression is used. If the dependent and the independent variables are both ordinal which means that they have a ranking or position then the rank correlation coefficient is measured.

In case the dependent attribute is ordinal then the ordered probit or the ordered logit is used. It is possible that the dependent attribute could be internal or a ratio like the scale of temperature. This is where regression is measured. Here is how we mention the kinds of bivariate data correlation.

• Numerical and Numerical

In this kind of variable both the variables of the bivariate data which includes the dependent and the independent variable have a numerical value.

• Categorical and Categorical

When both the variables in the bivariate data are in the static form then the data is interpreted and statements and predictions are made about it. During the research, the analysis will help to determine the cause and impact to conclude that the given variable is categorical.

• Numerical and Categorical

This is when one of the variables is numerical and the other is categorical.

Bivariate analysis is a kind of statistical analysis when two variables are observed against each other. One of the variables will be dependent and the other is independent. The variables are denoted by X and Y. The changes are analyzed between the two variables to understand to what extent the change has occurred.

4) Bivariate data examples

Bivariate analysis is the analysis of any concurrent relation between either two-variable or attributes. The study will explore the relationship that is there between the two variables as well as the depth of the relationship. It helps to find out if there are any discrepancies between the variable and what the causes of the differences are.

The bivariate analysis examples are used is to study the relationship between two variables. Let us understand the example of studying the relationship between systolic blood pressure and age. Here you take a sample of people in a particular age group. Say you take the sample of 10 workers.

The first column will have the age of the worker and the second column records their systolic blood pressure.

The table then needs to be displayed in a graphical format to make some conclusion from it. The bivariate data is usually displayed through a scatter plot. Here the plots are made on a grid paper y-axis against the x-axis and this helps to find out the relationship between the data sets that are given.

A Scatter plot helps to form a relationship between the variables and tries to explain the relationship between the two. Once you apply the age on the y-axis and the systolic blood pressure on the x-axis you will notice possibly a linear relationship between them.

How to understand the relationship

The graph will show that there is a strong relationship between age and blood pressure and that the relationship is positive. This is because the graph has a positive correlation. So the older is one’s age the higher is the systolic blood pressure. The line of best fit also helps to understand the strength of the correlation. If there is little space between the points then the correlation is strong.

The correlation coefficient or R is a numerical value that ranges between -1 to 1. This indicates the strength of the linear relationship between two variables. To describe a linear regression the coefficient is called Pearson’s correlation coefficient. When the correlation coefficient is close to 1 then it highlights a strong positive correlation. When the correlation coefficient is close to -1 then this shows a strong negative correlation. When the correlation coefficient is equal to 0 then this shows no relationship at all.

Conclusion

The above example lets you understand what is bivariate analysis. Analyzing two variables is a common study used in inferential statistics and calculations. Many of the scientific and business investigations work on understanding the relationship between two continuous variables. The main question that the bivariate analysis answers are if there is a correlation between the two variables, if the relationship is negative or positive and what is the degree or strength of the correlation.

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