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Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

8
votes
If you use the sum of predictors to model their interaction, your equation would be: $$ \begin{eqnarray} Y &=& \beta_0 + \beta_1X + \beta_2M + \beta_3(X + M) + e\\ &=& \beta_0 + \beta_1X + \beta_2M …
answered Jul 21 '16 by Milos
0
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0answers
Among other parameters, I examined the effect of the factor $Q$ on the response variable $Y$. I found a linear model with $R^2$ and adjusted $R^2$ being around $0.95$. The $F$ statistic of the overall …
asked Oct 27 '16 by Milos
1
vote
Here are my answers to your questions: This depends on how you define discrete levels. I would opt for regression followed by discretization if there are strict and crisp differences between new …
answered Jul 24 '16 by Milos
0
votes
1answer
I have a response variable, $Z$, for which I'm trying to make a linear model. Here are some of the fit diagnostics plots: From the fan-like shape of the residual-vs-predicted value plots, I concl …
asked May 21 '16 by Milos
0
votes
1answer
I used a Definitive Screening Design plan to examine which of the parameters $A_1, A_2, \ldots, A_6$ have significant influence on the response $Y$. The design plan consists of $13$ treatments. A trea …
asked Oct 20 '16 by Milos