{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

qmst notes9 - Building Multiple Regression Models...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Building Multiple Regression Models - Regression models -We are trying to predict unknown y values using known x values. To do so, we ' model' the data, or 'build a model' that gives us the best prediction based on the data we have. Standard Error of the Estimate (s e ) -A measure of how much error is in the regression model. It is preferable to have a small s e . Excel calls it just 'Standard Error' . Need to have a lot of content knowledge of what is being estimated to fully use the s e . Interaction -Occurs when the effects of one treatment ( x -variable) vary according to the levels of treatment of the other effect (used in medical comparisons). Ex : The interaction of temperature and humidity on a manufacturing process. Amount of red meat a family consumes based on religion. Linear regression - Predicting that scores will fall along a straight line. The simple regression and multiple regression examples we've done so far involved linear regression. p-value -Let's us know if the x variable is an accurate predictor of the y variable. R 2 - The proportion of variance of variance in the criterion variable (y) that is attributable to changes in the predictor variables. First order models -When the highest order of any predictor variable is 1 (i.e., x is not squared, cubed, etc.). Simple regression -First order model with one independent variable. Formula: ŷ = β 0 + β 1 x . Multiple Regression
Background image of page 1

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

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}