Tagged Questions

83 views

Whether to log transform variable when untransformed variable has positive skew and transformed has negative skew with additional missing data?

I have performed a log transformation on my skewed data, however on my DV it went from positive skew to negative skew after the (log) transformation, further data was missing from my DV after the ...
31 views

Whether to transform non-normal variables prior to performing EM imputation?

I recently received the following email: I have a sample of 100 and approximately 6-7% missing data on each independent variable of interest, and non-normally distributed IVs. I have square root ...
115 views

Whether to transform non-normal independent variables in logistic regression?

I recently received the following email, which I paraphrase below: I want to do binomial logistic regression with the data and I have non-normally distributed IVs. I tried doing square root ...
170 views

Box-Cox transformation for residuals in R

I have residuals for my model. They are simply measured-predicted. However, I notice that they do not follow a normal distribution. I want to make my residuals distribution normal so that I can ...
165 views

How to prepare variables with mild skew for multiple regression?

I am doing some univariate analysis on a variable before doing regression. I think it is very skewed. Three histograms are of (1) the original variable; (2) log10 transformation, and (3) inverse of ...
627 views

I log transformed my dependent variable, can I use GLM normal distribution with LOG link function?

First of all, thank you for the great forum! I have a question concerning Generalized Linear Models (GLM). My dependent variable (DV) is continuous and not normal. So I log transformed it (still not ...
44 views

Transforming data: correlate regardless of distribution

Is there a way to correlate data regardless of distribution? I know the Choleksy transformation is used for normally distributed data, but is there a general method that applies to any case? To ...
305 views

Transforming Percent Change: Lognormal Distribution?

I am dealing with a dependent variable relating to the price of an asset and the percent change in the price of that asset. The problem with this type of analysis, like when stocks are the unit of ...
388 views

Kriging on log transformed rainfall data

I am beginner in R. I had found in the literature that prior to performing kriging on the data, the distribution has to be investigated to check if it is Gaussian. So, in order to check if the data ...
235 views

907 views

Improving transformation of dependent variable and robust regression

In a multiple regression with 16k cases 2 IV (non-normally distributed) and one dependent variable that is also not normally distributed. DV see below: I've tried three ways of transforming the DV ...
4k views

Is a log transformation a valid technique for t-testing non-normal data?

In reviewing a paper, the authors state, "Continuous outcome variables exhibiting a skewed distribution were transformed, using the natural logarithms, before t tests were conducted to satisfy the ...
557 views

Life after the Box-Cox transformation

Suppose, we have a set of measurements of some quantity in some units of measurement. We also have a nice model that heavily relies on the properties of the Gaussian distribution. The model is ...
236 views

Is paired t-test valid for half-normal data?

ok i am not a statistician. I am doing paired comparison for several outcomes of cross validated interpolation results. I have to consider only the absolute value in my analysis and so although ...
241 views

Adjusting for tilt of the earth

I have rewritten the old question (below) to hopefully make things a bit clearer. Basically I think that the temperature of the earth should be normally distributed but is not due to the ‘seasonal ...
139 views

Is it valid to model discrete numerical test scores as coming from a continuous random variable?

I'm working with a sample of test scores which range from 0 to 100. These scores are generated from a set of 100 binary responses (0 or 1), so the higher the resulting sum, the better the performance. ...
72 views

Analyzing historical incident rates and rating future performance

I am analyzing a large of dataset (n>100) of incident rates, with the aim of forming a normal distribution. Then I will know if a future incident rate (x%) is either close to a historical mean or not, ...
Let's say you have a jointly gaussian vector random variable $\mathbf{x}$, with mean $\mathbf{M}$ and covariance $\mathbf{S}$. I now transform each scalar element of $\mathbf{x}$ , say $x_j$, with a ...