Regression analysis is a statistical technique used to understand the relationship between variables and predict outcomes. There are two main types: linear regression uses one independent variable to predict a dependent variable, while multiple regression uses two or more independent variables. Regression finds the linear relationship between variables that best fits the data points. It can be applied in business to measure how factors like price, interest rates, or industries influence asset prices, and to estimate how sensitive sales or profits are to changes in other variables like advertising or interest rates. Regression allows businesses to forecast demand and is often implemented using specialized software.

1. Uttara University
Assignment on:
“ How regression analysis can be applied in business?”
Subject:Business Statistics 2
Submitted By:
Tanvir Sakib Hasan
ID:M21431113004
Batch: 35th (A)
Submitted To:
Dr. Matiur Rahman,Ph.D
Uttara University
Date of Submission:27-12-2015

2. Regression
A statistical measure that attempts to determine the strength of the relationship between one
dependent variable (usually denoted by Y) and a series of other changing variables (known as
independent variables).
BREAKING DOWN 'Regression'
The two basic types of regression are linear regression and multiple regression. Linear regression
uses one independent variable to explain and/or predict the outcome of Y, while multiple
regression uses two or more independent variables to predict the outcome. The general form of
each type of regression is:
Linear Regression: Y = a + bX + u
Multiple Regression: Y = a + b1X1
+ b2X2 + B3X3 + ... + BtXt + u
Where:
Y= the variable that we are trying to predict
X= the variable that we are using to predict Y
a= the intercept
b= the slope
u= the regression residual.
In multiple regression the separate variables are differentiated by using subscripted numbers.
Regression takes a group of random variables, thought to be predicting Y, and tries to find a
mathematical relationship between them. This relationship is typically in the form of a straight
line (linear regression) that best approximates all the individual data points. Regression is often
used to determine how much specific factors such as the price of a commodity,interest
rates,particular industries or sectors influence the price movement of an asset.

3. Regression Analysis Applied in Business:
Regression analysis is a statistical tool used for the investigation of relationships between
variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon
another — the effect of a price increase upon demand, for example, or the effect of changes in
the money supply upon the inflation rate.
Regression analysis is used to estimate the strength and the direction of the relationship between
two linearly related variables: X and Y. X is the "independent" variable and Y is the "dependent"
variable.
The two basic types of regression analysis are:
 Simple regression analysis: Used to estimate the relationship between a dependent
variable and a single independent variable; for example, the relationship between crop
yields and rainfall.
 Multiple regression analysis: Used to estimate the relationship between a dependent
variable and two or more independent variables; for example, the relationship between
the salaries of employees and their experience and education.
Multiple regression analysis introduces several additional complexities but may produce
more realistic results than simple regression analysis.
Regression analysis is based on several strong assumptions about the variables that are being
estimated. Several key tests are used to ensure that the results are valid, including hypothesis
tests. These tests are used to ensure that the regression results are not simply due to random
chance but indicate an actual relationship between two or more variables.
An estimated regression equation may be used for a wide variety of business applications, such
as:
 Measuring the impact on a corporation's profits of an increase in profits
 Understanding how sensitive a corporation's sales are to changes in advertising
expenditures
 Seeing how a stock price is affected by changes in interest rates
Regression analysis may also be used for forecasting purposes; for example, a regression
equation may be used to forecast the future demand for a company's products.
Due to the extreme complexity of regression analysis, it is often implemented through the use of
specialized calculators or spreadsheet programs.