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

1 vote

Transforming the response on regression

1) Here's a counter-example: x = c(1,2,3,4,5,6,7,1,2,3,3) y = c(1,4,5,4.5,4.5,4,3.8,1,3.5,2.5,2.5) lny = log(y) anova(lm(y~x)) anova(lm(lny~x)) p = 0.05461 for y and p = 0.03907 for log y. 2) Ass …
WavesWashSands's user avatar
4 votes
2 answers
6k views

How are questions of similar meaning in questionnaires dealt with?

Edit 2: In fact, I don't mind if I get an answer about using logistic regression on true-or-false questions or something. … (Assume also that I'm sure simple linear regression is applicable. …
WavesWashSands's user avatar
20 votes
Accepted

Distribution of linear regression coefficients

First, for clarification, you're looking for the distribution of the ordinary least-squares estimates of the regression coefficients, right? … Under frequentist inference, the regression coefficients themselves are fixed and unobservable. …
WavesWashSands's user avatar
0 votes
Accepted

Interaction term with several levels in multi linear regression in R

The result is an ANCOVA model where the regression lines are not parallel (i.e. you're basically fitting a new regression line of your response variable against firm_age for each level of acquired_years …
WavesWashSands's user avatar
1 vote

Multiple Correlation Coefficient with three or more independent variables

One option is to just take the square root of the $R^2$ obtained when you do linear regression. …
WavesWashSands's user avatar