Basics of Bivariate Regression
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What are the different types of variable analysis?
All statistical analysis revolves around the variables. If there is only variable in the analysis, it is called as univariate analysis. With the introduction of a second variable, the same analysis is termed as Bivariate analysis. When the number of variables is more than two, then it is a Multivariate analysis.
What is Regression Analysis?
A regression analysis is a statistical method that determines the relationship between two variables. It involves ascertaining the change in a dependent variable in comparison to a change in one or more independent variables. In the recent years, regression analysis has become very popular. Many companies use regression analysis for their decision-making process. For instances, companies use this tool for market research by comparing different factors. Decisions regarding releasing new products, pricing of products are all based on regression analysis of market forces.
What is a Bivariate regression?
A Bivariate regression is focused on establishing the relationship between two sets of variables. Some prominent examples would be statistical observations of Sales of a company during different time periods, or test scores of students in comparison to their age. In these cases, the relationship is between the two terms is determined. Bivariate analysis is the most forms of quantitative analysis between the two terms (x,y). This article is aimed at providing the basic understand for bivariate regression assignment help and bivariate regression homework help.
Types of Bivariate Regression
There are two types of Bivariate Regression
- Where one variable is dependent, and the other is independent
In the first case, the value of one of the variable relies on or is dependent on the value of the other variable. For instance, if an analysis is conducted for sales of an umbrella company during different months. There is a connection between the sales and the months. The sales will be higher during the months where there is a higher rainfall. Hence the sales are dependent and the month is an independent variable. Another example will be marks of students in Quants and students who receive bivariate regression assignment help or bivariate regression homework help. Hence the marks secured in quants is dependent on the access to Bivariate regression assignment help or bivariate regression homework help
- Both the two terms are independent
In this case, both the variables are independent of each other. The values of one variable have no impact on the value of the second variable. For instance, the rate of interest offered by a bank and the rainfall during the month. There variables totally unrelated and hence they are independent variables.
Steps for performing a Bivariate Regression Analysis
The below sector lists down the steps for performing a Bivariate regression analysis.
Before starting the analysis, it is important to define the relationship between the variable. As discussed above the variables can be dependent or independent.
Once the relationship is defined, it should be quantified based on the type or direction of the relationship. This allows segregation of the data sets. The levels of measurement are Nominal, Ordinal, Ratio analysis and Correlation Coefficient.
Determination of statistical significance deals with establishing a connection with the results. This is done to ensure that the results are unbiased and the same results can be obtained with a similar study population.
After establishing a statistical significance, the strength of the relationship should be identified.
Bivariate regression equation
A bivariate regression equation can be given by y=a+bx + e. In this equation
y is the dependent variable
x is the independent variable
b denotes the coefficient of x with respect to y
e represents the margin of error.
The bivariate regression aims to display the relationship between the two variables with the help of a straight line. This is done so as to determine the coefficient of the line. This process of determining the coefficient is also called as ‘fitting a regression line’.
Based on the above equation we can observe the value of Y and estimate the coefficient between the two variables. The bivariate equation is useful for building financial modelling and economical research.
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