Hello everyone, I am Surbhi and I welcome you all in my channel Key Differences. Today in this video, we will talk about the difference between correlation and regression. So friends, without further ado, let's move on to our video.
Correlation Suppose you are studying two quantitative variables at the same time and you get to know that the change in one variable is reciprocated by an equivalent change in another variable, directly or indirectly, then these variables are said to be correlated. Further, if there is no such association found between them, then they are said to be uncorrelated. So, correlation refers to a statistical measure that determines the linear relationship between two variables. Basically, It shows the degree to which the two variables are correlated, i.e. fluctuate together. Further, the scientific study of how variables are correlated to one another is called as correlation analysis.
Correlation coefficient Correlation coefficient refers to a statistical measure which determines how strongly the pair of variables are correlated or connected to one another. It is denoted by R and it ranges from minus one to plus one. Now correlation can be positive correlation, negative correlation and no correlation.
So here we are going to discuss them in detail. Positive correlation. Positive correlation determines the degree to which two variables increase or decrease in tandem.
That is, In a positive correlation, the variables under study move in the same direction. For example, height and weight, profit and investment, study time and marks obtained, yield and rainfall, electricity bill and temperature. Negative correlation Negative correlation represents the degree to which one variable increases when there is a decrease in another variable to form an inverse relationship.
Hence, In the case of negative correlation, variables move in opposite direction. For example, price and demand, speed and travel time, age and eye vision. Lastly, no correlation.
Two variables are said to be uncorrelated when the change in one variable does not lead to any change in another variable in a certain direction. For example, age and intelligence, weight and energy, etc. Regression is a statistical tool used to identify the nature of relationship existing between a dependent variable and a set of independent variables. Here dependent variable means the factor which the researcher attempts to understand or predict.
It is also called as explained variable. On the other hand, independent variable refers to the variable which the researcher assumes and have an impact on the dependent variable. It is also called as predictor variable.
Furthermore, regression analysis is a set of processes which identifies the variable that influences the topic of interest. Hence, one can ascertain which factors matter most, which are not to be considered and how these factors influence each other. Regression Line The regression line of Y on X is the line of best fit for your data, that is a line that represents the data points in the best way, derived by least squares method.
It is used to predict the value of dependent variable y for the known value of independent variable x. In case of simple regression model, when y depend on x, then the regression line of y on x is represented by y is equals to a plus bx, where a and b are constants commonly known as regression parameters. Moreover, b is also called as regression coefficient of y on x. Now we are going to discuss the differences between correlation and regression. Correlation, as the name suggests, is a statistical measure that ascertains the correlationship between two variables.
Correlation allows the experimenter to identify the association or absence of association between the variables under study. If the variables are found correlated, it helps in measuring the strength of association between the variables. On the other hand, Regression refers to a group of statistical processes that estimates the relationship between dependent variable and one or more independent variable.
Basically, it represents how an independent variable X is numerically related to dependent variable Y. Regression analysis helps to identify the functional relationship between the variables so as to estimate the unknown variable with the help of known variable and make future projections. The main objective of correlation is to obtain the numerical value that depicts the relationship between variables. On the other hand, the main objective of regression analysis is to determine the value of random variable depending on the values of fixed variable. Correlation represents the degree to which two variables move together as against.
Regression represents the effect of one unit change in the known variable x on the estimated variable y. Next, correlation coefficient can range from minus 1 to plus 1. Whereas if regression coefficient of y on x is greater than 1 then regression coefficient of x on y is less than 1. Coefficient Correlation coefficient is independent of any change of scale or shift in origin. Conversely regression coefficient is dependent on the change of scale however it is independent of shift in origin. When it comes to nature of coefficient. correlation coefficient is mutual and symmetrical.
This means that correlation between x and y is same as y and x. In contrast, regression coefficient is not symmetrical, meaning that regression of y on x is not same as x on y. Lastly, when it comes to usage, correlation shows the linear relationship between two variables.
As against, regression gives the line of best fit and Estimates one variable on the basis of another variable. So friends this brings me to the end of this lesson. I hope you find the lesson very useful and all your doubts regarding the difference between correlation and regression has been clear now. However if you want to study the topic further in detail you can visit our official website that is key-references.com. Here you can find a detailed comparison of correlation and regression along with their definitions.
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