Timeline for LinearModelFit to Indicate Influence on outcome
Current License: CC BY-SA 3.0
8 events
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| May 20, 2019 at 17:44 | comment | added | eyorble |
@Titus Yes, that should work. The documentation has some examples for that use case (under the Scope heading). However, if you have more specific questions about LinearModelFit, please consider asking a separate question here. In general the dependent variable is in the last position.
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| May 20, 2019 at 16:35 | comment | added | Titus | Just a verification on the LinearModelFit syntax, because I am working on a predictive regression: if I have e.g. 2 independent and 1 dependent variable, the dependent variable goes in the third column (position) while the independent in the first and second column (position), with the function looking like LinearModelFit[{x, y, z}, {x, y}, {x,y}] . Is that correct? | |
| Mar 25, 2018 at 23:24 | comment | added | eyorble | @hellohi Generally p-values are the probability that a variable is insignificant, so a p-value of 0.9 generally means that the variable has little to no influence on the resulting model, while 0.01 generally means that it has a relatively significant influence. | |
| Mar 25, 2018 at 19:26 | comment | added | hellohi | @eyeorble Sorry may I ask another question? I'm a little confused about the p-values of the parameter table and I'm finding conflicting information. If the p-value is, say, 0.9 I can assume this has a higher influence on the selected row than a factor with a p-value of 0.01? | |
| Mar 24, 2018 at 16:23 | vote | accept | hellohi | ||
| Mar 24, 2018 at 16:23 | comment | added | eyorble |
@hellohi Yes, this model is fitting the 4 parameters to the last element of each entry in the data table.
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| Mar 24, 2018 at 13:54 | comment | added | hellohi | Amazing, thank you! Just to double check, this model fit is indeed measuring the effects they have on the results in the 6th row? :) | |
| Mar 23, 2018 at 15:23 | history | answered | eyorble | CC BY-SA 3.0 |