# Koji Yatani's Course Webpage

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hcistats:multilevellinear

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 hcistats:multilevellinear [2014/07/23 07:56]Koji Yatani hcistats:multilevellinear [2014/08/14 05:26] (current)Koji Yatani Both sides previous revision Previous revision 2014/08/14 05:26 Koji Yatani 2014/07/23 07:56 Koji Yatani 2014/07/23 07:55 Koji Yatani created 2014/08/14 05:26 Koji Yatani 2014/07/23 07:56 Koji Yatani 2014/07/23 07:55 Koji Yatani created Line 1: Line 1: ======Multilevel Linear Model====== ======Multilevel Linear Model====== + \\ =====Introduction===== =====Introduction===== [[HCIstats:​LinearRegression|Linear Models]] and [[HCIstats:​GLM|Generalized Linear Models (GLM)]] are a very useful tool to understand the effects of the factor you want to examine. These models are also used for prediction: Predicting the possible outcome if you have new values on your independent variables (and this is why independent variables are also called predictors). [[HCIstats:​LinearRegression|Linear Models]] and [[HCIstats:​GLM|Generalized Linear Models (GLM)]] are a very useful tool to understand the effects of the factor you want to examine. These models are also used for prediction: Predicting the possible outcome if you have new values on your independent variables (and this is why independent variables are also called predictors). Line 253: Line 254: It is probably acceptable that you simply report the direction of each significant effect (positive or negative) if you do not really care about the actual value of the coefficient. But I think you should report at least whether each significant effect contributes to the dependent variable in a positive or negative way. It is probably acceptable that you simply report the direction of each significant effect (positive or negative) if you do not really care about the actual value of the coefficient. But I think you should report at least whether each significant effect contributes to the dependent variable in a positive or negative way. + + \\ + ~~DISCUSSION:​open~~ 