Writing sas code linear regression
The test statistic (F* = 36.96) is found under F Value. Overall F-test To test whether all of the variables taken together significantly predict the outcome variable (FVC), use the overall F-test.Because the model includes more than one predictor variable, you may want to consider using the adjusted R 2 (Adj R-Sq) value instead of the R-Square for interpreting amount of variance explained by the independent variables. Interpreting Output The multiple regression equation is: Yhat = -6.67 + 0.18(height) 0.03(age) The R-Square value is interpreted the same as with simple linear regression: 67% of the variance in FVC is explained by height and age in the model.Create a multiple linear regression model using both height and age to predict FVC: PROC REG DATA = air MODEL fvc = height age RUN QUIT It appears a linear relationship is justified between FVC and height, although it is unclear whether a linear relationship exists between FVC and age.age: PROC GPLOT DATA = air PLOT fvc * height PLOT fvc * age RUN In order to explore this before analyzing the data, create two plots: one of FVC vs. Exploring the Data We are interested in what factors may predict FVC.Input the file air.txt into SAS with the following code (adjusting the location of the file as necessary): DATA air INFILE C:\air.txt' dlm = ' ' firstobs = 2 INPUT sex age height weight fvc fev1 height_age = height*age RUN Height_age creates a new variable which represents the interaction between height and age.(Dunn and Clark (1987), Applied Statistics: Analysis of Variance and Regression, p.354.)
FEV1 is the volume of air expelled during the first second when an individual has been told to breath in deeply and then expel as much air as possible.
FVC is the total volume of air in liters which an individual can expel regardless of how long it takes. The variables measured were age, gender, height, weight, forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV1). Consider the following situation: The file air.txt contains a subsample of data from a study of the effect of air pollution on lung function.It is also often important to investigate a possible interaction between two or more independent variables. In this case, your model will contain more than one independent variable. Introduction Often in linear regression, you want to investigate the relationship between more than one predictor variable and some outcome.Multiple Linear Regression Linear regression with two or more predictor variables.