Difference between Correlation and Regression

From ancient times, the role of the economy in nation-building was unmatchable. The economy contributes to the nation's development and creates a skillful labor force. The role of the economy in nation-building is very important as it has several components which enhance the nation's progress. The nation's economic system includes the stock market, export-import, demand-supply, circulation of money, etc. A strong economy is vital for any nation's progress because it forms its backbone. From ancient times, the economy has played a role in changing political powers. The flawed economic system led to the uprising and, ultimately, power transfer.

Initially, people used a barter system to buy goods and services. After the barter system, the currency was developed as coins-several dynasties minted coins to run the economic system. After coins, other inventions took place, ultimately leading to physical money's development. Now governments are moving one step ahead and launching a digital currency. Over time, new forms of currency are developed, but their basic aim is still the smooth running of the economic system. The best way to promote the company's product is to keep records of the product sales and the customer's behavior towards that product. This article will discuss the two important concepts used in the economy: Correlation and Regression. Both form a relationship with the different variables and ultimately lead to the development of outcomes.

What is Correlation?

Correlation is a statistical concept used to represent the strength between two variables. Correlation is comprised of two words: Co, which means together, and relation, which means connection. Together these two words are known as Correlation, and their main aim is to determine the relationship between the two variables x and y. Correlation can be in any form; it may be Positive, Negative, or Zero.

Positive Correlations are those variables that move in the same direction. A negative Correlation is those variables that move in opposite directions. And in Zero Correlation, there is no linear relationship between variables.

Examples:

Positive Correlation: Height and Weight, taller people have heavyweight and vice versa.

Negative Correlation: Price and Demand, with the increasing price, the demand for the product is decreased and vice-versa.

Zero Correlation: There is no relationship between variables, for example, the height of the students and the habit of drinking coffee.

Application of Correlation:

The application of Correlation is very wide; it helps companies progress rapidly. Because it helps to understand the behavior of the consumers. Correlation is used in almost every field. Correlation is important as it forms the backbone of any product's success. It supports the companies and institutions to develop those products in demand. Some of the most notable applications of Correlation are-

1. E-commerce:

Time spent vs Product purchase by a customer helps the e-commerce companies identify those products in demand and those on which customers spend lots of time but have yet to purchase. After getting noted, e-commerce companies offer sales on those products to increase their customer base. Another usage of Correlation in e-commerce is the Number of unique customers vs Sales in a day. This Correlation helps the companies to decide which customers they company has to target and how to increase new customers.

2. Education:

Years of study vs Salary Intake

Correlation in education is very important as it helps governments and other educational institutions frame policies accordingly. Correlation helps the governments frame education policies in such a manner that it helps to increase the education enrollment ratio and simultaneously decreases the unemployment ratio.

3. Real Estate:

Another application of Correlation can be seen in real estate-for example, Income vs Location of Flats or Location of Flats vs Rate of flats. The above comparison helps the real estate contractor to develop flats in those areas where it may increase the selling of flats. Also, it helps real estate companies to decide the market price of the flats and to choose the location for the site and the target customer.

In the above paragraphs, we discussed Correlation and its applications. Now we discussed Regression and its applications.

What is Regression?

Regression is also a statistical technique used to depict the value of those variables that depend on other variables. In other words, it is a technique that estimates the change in the value of the dependent variable due to the change in the independent variable. The outcome in Regression depends on the outcome of other variables, that is, independent variables. To get detailed looks, Regression is used, and it helps the equation to predict and optimize the data in the future.

The best usage of Regression is to determine the strength of predictors, forecast an effect, and trend forecasting. The best thing about Regression is its formula. Lets us now discuss the formula of Regression that simply its calculations. As Regression is a representation of the relationship between the dependent and independent variables, it can be denoted as:

Y = a + bX +c, where

Y: is a Dependent Variable

X: represents independent variable:

a: intercept

B: slope

c: error

Some the Examples of Regression are-

Regression can be used to predict rainfall based on the level of humidity present in that area, the direction of rainfall, the speed of the wind, etc. Other examples where Regression is used are to determine the price of a house depending on the location of the rooms, facilities provided in the room, level of pollution, etc.

Application of Regression:

Regression usage is very wide because many fields are directly or indirectly interlinked with each other. And this linking forms the application for Regression. Observing any study regression is useful because it represents the hidden part of the study. Let us discuss some of the most notable applications of Regression-

1. Epidemiology:

The relationship between smoking and mortality can be best explained with the help of Regression. For instance, if we have to represent these two variables, smoking is independent, while life span is the dependent variable. With the usage of Regression, we find that smoking is injurious to health; it can cause serious health problems and even also lead to cardiovascular diseases, which ultimately lead to death.

2. Environmental Studies:

Another important application of Regression is seen in environmental studies. The environment is a broad concept; everything is directly or indirectly interlinked to each other. Environmentalists use Regression to predict natural phenomena such as tsunamis, thunderstorms, and sandstorms in advance. This helps the governments to frame policies accordingly and save the life and property of the affected ones.

3. Geology:

Another important usage of Regression can be seen in geology. With the help of Regression, companies forecast the reserves of natural resources, most importantly natural gases, in different world sites.

Application of Regression can also be seen in other sectors like archaeology, medicine, finance, and economics.

Key Differences between Correlation and Regression are:

1. In Correlation, the variables used, for example, X and Y, can be interchangeable. But in Regression, the value of X can change the value of Y or vice-versa. And their results are also changed based on swapping these two variables.
2. The variables used in Correlation are random, they can be the same or different, but they must be random. But in Regression, one variable is random, and the other must be fixed. And this helps to predict the result thoroughly.
3. We can say that Correlation is a single statistic. On the other hand, Regression produces an entire equation.
4. Causality doesn't capture by Correlation, whereas Regression depends on causality.
5. A single point represents the Correlation's graphical representation. On the other hand, the line represents linear Regression.
6. The Correlation between the variables used in Correlation is the same. For example, the Correlation between X and Y is the same as the Correlation between Y and X. But the Regression between the variables is different; for example, the Regression of X and Y is completely different from the Regression of Y and X.
7. Sometimes, both Correlation and Regression are interdependent; for example, if the Correlation is negative or positive, then the Regression slope will also be negative or positive.

The Conclusions:

Both Correlation and Regression are necessary as they statistically support our study. In this article, we discussed Correlation and Regression and their applications too.

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