Dsc 203 lecture notes - chapter 13

DSC 203 Lecture Notes - Chapter 13
Download Document
Showing pages : 1 - 2 of 10
This preview has blurred sections. Sign up to view the full version! View Full Document
1 Introduction to Regression Analysis We wish to study the relationship between 2 or more variables We define: y = the dependent variable x = independent (predictor) variable We wish to predict y on the basis of x . Example: y = sales of a product x = price of the product Assuming a linear (straight line) relationship between y and x, we wish to find a prediction equation Predicted value of y intercept y ˆ = b 0 + b 1 x slope Simple Linear Regression One Straight Line Predictor Relationship Variable We use data concerning both x and y to find numerical values for b 0 and b 1 .
Background image of page 1
Multiple Regression We predict y by using more than one independent (predictor) variable. Example: y = sales x 1 = price x 2 = advertising budget x 3 = type of advertising ( TV, radio, print, etc.) The prediction equation might have the following form: y ˆ = b 0 + b 1 x 1 + b 2 x 2 + b 3 x 3 We find numerical values of b 0 , b 1 , b 2 , and b 3 by using data concerning y , x 1 , x 2 , and x 3 . Alternatively, ( as one possible example) the prediction equation could have the form:
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.