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

2
votes
Starting from your first approach: If you treat the data as if it had only 7 observations, you need to weight them with the size of the group. Refer to this question for how to do regression with …
answered Jan 31 '14 by mzuba
0
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You would probably want to test the hypothesis that the outcome is different in these two groups. Since the scale of the dependent variable is ordinal, you want to perform a Wilcoxon-Mann Whitney U- …
answered Mar 4 by mzuba
3
votes
3answers
are around 6–8. OLS regression seems to be a poor choice to me, as it might produce predicted values outside the 1–10 interval. My colleagues have suggested that I might take a look at truncated … /censored analysis, such as tobit regression. However, I do not believe that I have data which is censored in the way tobit regression would assume, which would be the case if only part of the real …
asked May 3 '12 by mzuba
2
votes
Multinomial logistic regression works like a series of logistic regressions, each one comparing two levels of your dependant variable. Here, category 1 is the reference category. For example … , consider the case where you only have values where category is 1 or 5. (Recode that to 0 and 1, so that you can perform logistic regression.) The coefficients of line 5 of your output represent the variable coefficients such a logistic regression would yield. …
answered Dec 3 '12 by mzuba
1
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Insignificant at the 10% level means that the 90%-confidence interval overlaps with zero. A significant difference at the 1% level means that the (larger!) 99% confidence intervals do not overlap. Thi …
answered Mar 26 '12 by mzuba
4
votes
2answers
others that I might include in later stages). Suppose my regression model is $y_t = β_0 + β_1 age + β_2 sex + β_3y_{t-1} +u $. Since R provides me with coefficients for the betas, it is easy to …
asked Aug 17 '11 by mzuba
1
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In short: There is no statistical a-priori method to identify causality in non-time-series data, and there cannot be. Regression analysis always only measures some sort of coincidence, that is a …
answered Jan 31 '14 by mzuba
11
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What you need is a postestimation test, which tests for significance of difference between two regression models, one of which is nested, i.e., it results from the first regression model plus some … interaction is then constructed with a dummy variable that identifies the group which might feature a different coefficient in the regression model. I do not believe that an interaction term between price and attitude could help to answer your question. …
answered Sep 6 '12 by mzuba
1
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1 – non-normal residuals might indicate poor model fit. Panel-GMM models are quite tricky, something might be wrong with you specification. 2 – Have you tried the robust standard error calculation m …
answered Jan 31 '14 by mzuba
0
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You could fit a simple logistic regression model and include time as a covariate, this would imply a linear time trend. Note that in the regression, the time trend is negative and insignificant …
answered Jun 13 '17 by mzuba
1
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Take a look at the predicted values and check if they have roughly the same distribution as the original Ys. If this is the case, linear regression is probably fine. and you will gain little by improving your model. …
answered Mar 4 by mzuba