Statistics 101: Simple Linear Regression Concept 2022

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Statistics 101: Simple Linear Regression, The Very Basics
Link: https://www.youtube.com/watch?v=ZkjP5RJLQF4
Summary
To clarify the concept behind simple linear regression a case study of hotel service is
used. In this article, the hotel owner need to estimate the tip expected from any given bill
amount. To do the estimate the tip amount values which are the dependent variable are collected.
Using only the dependent variable, the hotel owner have to come up with a model that best
predicts the next expected tip amount. Using this single variable with no other additional
information, the best estimate for the tip at any given bill amount will be given by the mean. The
dependent variable which in this case is the tip amount is the variable that is to be predicted.
The difference between the value predicted using the model and the actual recorded
value is known as the residual. The sum of all the residual values do add up to zero. The
residuals are squared to emphasize on the larger deviations. The sum of the squared residuals is
what is known as SSE (Sum of the Squared Errors). The objective of the simple linear regression
is to develop a linear model that minimizes the SSE. This is what is defined as the best fit line.
When the independent variable is introduced, a significant regression model is expected to
reduce the SSE that was obtained when only the dependent variable data was used. The
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