The output shows the parameters of β 0 and β 1 respectively, i.e, Linear regression assumes that the dependent variable (e.g, Y) is linearly depending on the independent variable (x), i.e., Y= β 0 + β 1(X) + random error, where β 0 is the intercept and β 1 is the slope. Interpreting the result of the linear regression Variable DF Estimate Error t Value Pr > |t| ![]() Reading the output of the linear regression Weight (continuous) ~ Height (continuous) In the demo example, if one wants to see impact of height on weight, or predict weight according to a certain given value of height. The general linear regression model can be used. If the question is to investigate the impact of one variable on the other, or to predict the value of one variable based on the other, Infile "H:\sas\data\measurement.csv" dlm=',' firstobs=2 Īnalyzing the impact of one variable on the other Suppose in a health screening, seven people take measurement on Gender, Height, Weight and Age. On the other hand, if a linear model is used to fit relationship between X and Y, the stronger X and YĪre linearly associated, the better fit the model for the date, and the corresponding test on strength of lindear association is also known as a test on linear correlation. The slope the linear function indicates the strength of impact, and the corresponding test on slopes is also known as a test on linear influence. If the relationship between two variables X and Y can be presented with a linear function, Linear regression model is a method for analyzing the relationship between two quantitative variables, X and Y.
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