In this article, we are going to learn about regression analysis, why it is such an important concept in the subject of statistics. It is among the most powerful methods in this subject that are used to determine the connection or link between different variables. Then these links are used to forecast observations of the future.
In this article, we will learn more about this method, how different companies use this method, what are the various types of regression analysis, and much more about this type of data analysis.
What is Regression Analysis?
When we define this analysis, we say it is a method that is used to estimate the relationship between one or more independent variables and a dependent variable. These independent variables can be defined as an assumption or driver that is altered to evaluate its influence on a dependent variable which is the result or the outcome.
In simple terms, regression analysis is a mathematical method of sorting out which independent variables have an impact on the outcome. This method answers various questions including:
Which of these factors matter the most?
Which factors don’t matter much and can be ignored/ discarded?
How do these factors relate and how do they interact with one another?
One Regression Analysis Example that can be Given is:
Imagine you are a manager that is trying to forecast the subsequent month’s numbers. Knowing that countless factors can affect the final numbers at the month, you try to think about all the various options. Some of the factors you know are the weather, competition, and much more. Some in your company agree and conclude that ‘the more rain there is, the higher the numbers will be’, etc.
In this example, the dependent variable would be the final numbers of the month and the independent ones are weather, competitors, etc. Using such information, you can create a regression analysis PDF so you can use the data later on when you need it for other work.
What are the Different Types of Regression Analysis?
There are three types which are:
Linear regression forecast Y responses from an X variable. It creates the relationship between two variables with the help of a straight line. This method uses one independent variable to forecast the result of the dependent variable which is Y.
Multiple linear regression is also known as multiple regression analysis. It is very rare for a dependent variable to be affected by only one variable. This can be linear or non-linear and it is grounded on the assumptions that there is a linked connection between the two sorts of variables. This type also assumes that there isn’t any major correlation between the independent variables which are used.
Y = the variable that you trying to predict (dependent variable).
X = the variable that you using to predict (independent variable).
a = the intercept.
b = the slope.
u = the regression residual
Nonlinear regression analysis is the type in which the data is fit to a model and then that data is articulated as a mathematical function. It relates the 2 variables in a nonlinear relationship which is a curve. The main goal of this is to make the summation of the squares as minor as possible. This sum of squares is a measure that keeps track of how far the Y observations vary from the curved function which is used to forecast the Y. in simple terms, it is a curved function of variable X and is used to forecast variable Y. They can show an estimate of population growth, for example.
These are the 3 main types of regression analysis that are very important and need to be revised thoroughly.
In this article, we learned quite a bit about regression analysis and much more about how everything works.
Did you know that this method is not only used for looking for trends it is also a very useful hack for finding the nth term in a quadratic sequence?
Did you know that Francis Galton coined the term "regression" in the nineteenth century to describe a biological phenomenon?
Did you know regression analysis is one of the most reliable methods of identifying the impact of variables on a topic of interest?
Did you know regression analysis is mainly used to find the cause and effect relationship between variables, forecasting, and time series modeling?