Bivariate Regression

Bivariate Regression Analysis is a type of statistical analysis used to analyze and at the reporting stage of quantitative market research. Bivariate Regression Analysis involves the analysis of two variables denoted normally as X and Y where one is treated as an independent variable while the other is a  dependent variable. The equation of the line is the best of fit that shows a positive relationship between the independent and dependent variables and is represented as a y=bx+a where y is the dependent variable, x is the independent variable, a is the point where the line of best fit intersects the y-axis and b is the angle of the line. 

In a bivariate regression, the outcome variables are also called the dependent variable, and the risk factors as the independent variables. In regression analysis .to better understand the working of the dependent and independent variables, when you manipulate the independent variable and measure the outcome in the dependent variable. The intercept/constant) describes the point where the function crosses the y-axis while the slope acts as the heart and soul of the equation because it tells you how much you can suspect Y to change as well X increases.

The regression analysis has multiple uses in the world of business as follows.

1. Predictive Analytics: Regression is useful as it helps businesses to have a forecast of the future opportunities and any impeding risks and therefore the management is made ready to ensure the level of risks does not affect t entire operations. Demand analysis, can for instance predict the number of items a customer would buy and therefore this help to guide on the production levels of those items. Similarly, the insurance companies use RA to assess the creditworthiness of policyholders and the likely claim request at a time

2. Operation Efficiency:

Regression models are very critical in the optimization of business processes. Especially in the factories and industries, data-driven decision making eliminates way any guesswork, hypothesis and possible corporate politics from decision making. As a result, the business performance improves through the highlight of sections with the maximum impact on the firm’s operational efficiency and revenues.

3. Regression analysis supports decisions:

Today’s Businesses are overloaded with data on finances, operations and customer purchases. Increasingly, executives are now bent on data analytics to make informed business decisions that have statistical significance, thus eliminating the intuition and gut feel. Since business currently is turning to the use of data in decision making, RA brings the much needed scientific angle to t5othe overall management of the business operations. ideally, RA tests a hypothesis before a decision to execute it is done.

4. Correcting Errors:

Regression analysis is used to offer quantitative support for the business decision making processes and prevent possible erroneous mistakes arising from the manager’s intuitions.

Bivariate regression can be used to look at the relationship between income and happiness. A researcher could easily predict a participant’s level of happiness (score) if they know their income.