Lecture-18-Simple Linear Regression

Lecture-18-Simple Linear Regression - LECTURE 18 Simple...

Info iconThis preview shows pages 1–3. Sign up to view the full content.

View Full Document Right Arrow Icon
LECTURE 18 Simple Linear Regression (Part 2): The Coefficient of Determination This lecture covers material on measuring the goodness of fit for the estimated regression equation using the Coefficient of Determination. Microsoft Excel is used for some applications. Read: Chapter 12, Section 12.3. In Lecture 19, we learnt how to compute the estimated linear regression equation from sample data by using the least squares method. We now want to know how good is the fit between this estimated equation values ( ŷ i ) and the actual observation ( y i ). The measure used for determining the goodness of fit is the Coefficient of Determination. To compute the Coefficient of Determination we need to compute three values: 1. Sum of Squares Due to Error (SSE) 2. Total Sum of Squares (SST) 3. Sum of Squares Due to Regression (SSR) ___________________________________________________________ We will use the example in Lecture 18 shown below to illustrate our computations . A company launches a new product and conducts an extensive advertising campaign for four months with advertising expenditure inceasing every month. It wants to develop an equation showing how sales is related to its expenditure on advertising. The data is shown in the table below. Advertising Expenditure ($ 1000s): 1 2 3 4 Product Sales (in 1000 units): 2 4 4 6 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Sum of Squares Due to Error (SSE ) In Lecture 15, we had seen that the Least Squares Method minimizes the sum of the squares of the differences between the observed values of y i and the corresponding estimated values of ŷ i . Therefore the objective of Least Squares method is:
Background image of page 2
Image of page 3
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 09/21/2011 for the course OM 210 taught by Professor Singer during the Summer '08 term at George Mason.

Page1 / 9

Lecture-18-Simple Linear Regression - LECTURE 18 Simple...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online