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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 …
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. …
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. …
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 …
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. …