Simple Linear Regression Solution

STEP 0: Pre-Calculation Summary
Formula Used
Dependent Variable = Regression Line Slope*Independent Variable+Y Intercept
Y = B*X+A
This formula uses 4 Variables
Variables Used
Dependent Variable - Dependent Variable refers to the forecasted number i.e. number of deals closed.
Regression Line Slope - A Regression Line Slope is a line which is used to describe the behavior of a set of data. In other words, it gives the best trend of the given data.
Independent Variable - Independent Variable refers to the number of deals closed.
Y Intercept - Y Intercept is the point of interception, or what y equals when x is zero.
STEP 1: Convert Input(s) to Base Unit
Regression Line Slope: 2 --> No Conversion Required
Independent Variable: 20 --> No Conversion Required
Y Intercept: 4 --> No Conversion Required
STEP 2: Evaluate Formula
Substituting Input Values in Formula
Y = B*X+A --> 2*20+4
Evaluating ... ...
Y = 44
STEP 3: Convert Result to Output's Unit
44 --> No Conversion Required
FINAL ANSWER
44 <-- Dependent Variable
(Calculation completed in 00.020 seconds)

Credits

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Created by Kashish Arora
Satyawati College (DU), New Delhi
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Verified by Vishnu K
BMS College of Engineering (BMSCE), Bangalore
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Simple Linear Regression Formula

​LaTeX ​Go
Dependent Variable = Regression Line Slope*Independent Variable+Y Intercept
Y = B*X+A

What is Simple Linear Regression?

Simple Linear Regression is a method of sales forecasting that is focused on studying the relationships between two quantitative variables such as sales values and sales volumes. It can measure how strong the relationship is by determining the value of one variable at the certain value of another variable, for example impact of advertising spending on sales revenue.

One variable X on the x-axis is independent, also called predictor or explanatory. And, the other variable Y on the y-axis is dependent on the first one, also called outcome or response. The response variable Y is the amount of sales revenue and the predictor variable X is the amount of money the business spent on advertising. In short, Y depends on X.

How to Calculate Simple Linear Regression?

Simple Linear Regression calculator uses Dependent Variable = Regression Line Slope*Independent Variable+Y Intercept to calculate the Dependent Variable, The Simple Linear Regression formula is defined as regression analysis used to understand the relationship between a dependent variable and an independent variable. Dependent Variable is denoted by Y symbol.

How to calculate Simple Linear Regression using this online calculator? To use this online calculator for Simple Linear Regression, enter Regression Line Slope (B), Independent Variable (X) & Y Intercept (A) and hit the calculate button. Here is how the Simple Linear Regression calculation can be explained with given input values -> 45 = 2*20+4.

FAQ

What is Simple Linear Regression?
The Simple Linear Regression formula is defined as regression analysis used to understand the relationship between a dependent variable and an independent variable and is represented as Y = B*X+A or Dependent Variable = Regression Line Slope*Independent Variable+Y Intercept. A Regression Line Slope is a line which is used to describe the behavior of a set of data. In other words, it gives the best trend of the given data, Independent Variable refers to the number of deals closed & Y Intercept is the point of interception, or what y equals when x is zero.
How to calculate Simple Linear Regression?
The Simple Linear Regression formula is defined as regression analysis used to understand the relationship between a dependent variable and an independent variable is calculated using Dependent Variable = Regression Line Slope*Independent Variable+Y Intercept. To calculate Simple Linear Regression, you need Regression Line Slope (B), Independent Variable (X) & Y Intercept (A). With our tool, you need to enter the respective value for Regression Line Slope, Independent Variable & Y Intercept and hit the calculate button. You can also select the units (if any) for Input(s) and the Output as well.
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