Mathematical re-expression, often nonlinear, of data values. Data are often transformed either to meet the assumptions of a statistical model or to make the results of an analysis more interpretable.

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9 views

Predict after using Box Cox Transformation

I am doing a Multiple Linear Regression on a data set where: The response variable is continuous One of the explanatory variables is continuous and the rest are binary(categorical) 1 if it is there 0 ...
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0answers
35 views

Distribution for $Y = \sqrt{X_1^2 + X_2^2}$, when $X_1, X_2$ are dependent and normally distributed with different variance? [duplicate]

Is there a closed form distribution for the transformation given by $Y = \sqrt{X_1^2 + X_2^2}$, when $X_1, X_2$ are jointly normal but dependent random variables with different variance? OBS: I know, ...
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10 views

How to interpret logarithmically transformed coefficients in negative binomial regression?

How can I interpret log-transformed independent variables in terms of percent change in a negative binomial regression?
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24 views

Why am I getting a zigzag when asking the medium of two values [on hold]

Apologies for the code I have made it as readable as I can What I am trying to do is take the first chunk of data from a chart to double its length by stretching it, so instead of each value ...
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0answers
22 views

How to apply transformations to the predictors of a GLM?

This post discusses why we need to transform $Y$ before estimating the predictors exponents in order to reduce the problem to a linear fit. The example builds on $Y$ log-normal. In the case of a GLM, ...
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21 views

Regression excercise

I have been sitting with this regression problem for about four months and can not seem to figure it out. My data show signs of heteroscedasticity and i tried many types of transformations with no ...
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2answers
156 views

Why $Y$ should be transformed before the predictors?

Both answers in these threads, one and two claim that $Y$ should be transformed before applying any other transformation to the predictors. Indeed Weisberg chapter on transformations focus more on DV ...
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0answers
26 views

Variance functions and transformations in linear mixed models, beginner questions

First post here. I'm pretty much a newbie both in statistics and using R, but nevertheless trying to fit a linear mixed model with the package nlme. My question is about transformations/variance ...
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21 views

Covariance Matrix using Delta Method

I am trying to get the variance-covariance matrix of a transformation using the Delta Method. Originally, I have coefficients (let's call them X1, X2, etc...) and their variance-covariance matrix. ...
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0answers
25 views

Confusion about nonlinear transformation of gamma and inverse-gamma distributions

I have a question about the variance of a transformed random variable, illustrated by a particular example. Let $X_1, ..., X_n$ and $Y_1, ..., Y_n$ be independent random variables drawn from an ...
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0answers
25 views

On log-normal distributions

Since my research data seems to follow log-normal distribution, I was curious to learn more about the topic. In addition to very nice answers here on Cross Validated (In linear regression, when is it ...
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0answers
17 views

Transforming data with a limited range (0,1,2) for parametric testing (ANOVA) [duplicate]

I've collected data on accuracy of recognition of images. Accuracy is a score out of 2 with points 0,1,2.. participants can score a 0. I am aiming to use a parametric test (ANOVA mixed design) to ...
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1answer
26 views

Transforming data with a limited range (0,1,2) for parametric testing (ANOVA)

I've collected data on accuracy of recognition of images. Accuracy is a score out of 2 with points 0,1,2.. participants can score a 0. I am aiming to use a parametric test (ANOVA mixed design) to ...
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0answers
25 views

UnReSolved Mean Adjust DataSet to achieve .5 Mean [duplicate]

Update So I've done some of my own work on transformational methods, and the best I can get is what I call an s transform as detailed in this workbook; however, various attempts at trying to mean ...
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2answers
74 views

Log transformation not making data normal

I have a data set with positive skewness when I log tranform it tends to be negatively skewed. Is there any other transformation that I can use or any statistical method works? Thanks!!!
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1answer
15 views

Comparing treatment means with lots of zeros

I am trying to compare the means of two treatments on a continuous variable with a lot of zeros in it. I've tried a log(n+1) transformation but that did not get me to a normal distribution. Any ...
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0answers
8 views

How can I code in SAS a tree to bin continuos variables?

I don't want to use SAS-Miner, because I want to create the code to do the optimal binning, even if it's only using entrophy criteria. Is there a code to do so? Thanks.
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35 views

Finding transformation function for a distribution that looks like exponential

Suppose that we have two data sets, R and P. R is larger than or equal to ...
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0answers
23 views

How do I relate the variance of a Normally distributed variable to the variance of the exponentiated transform

If I have a Normally distributed variable with a Mean of zero and a given variance, how will this variance relate to the variance of the exponentiated form?
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1answer
23 views

Data transformation

I was writing with a question regarding a time-varying state space model of the form: \begin{align} y(t) &= \mu_1(t) + A(t)x(t) + v(t); &v(t) &\sim (0, R(t)) \\ x(t) &= ...
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0answers
51 views

Transform long tail data set to bell curve [closed]

I'm analyzing a data set that has an extremely long tail, and I'm looking for a way to transfer the data into a bell curve so I can apply statistical analysis to it. Hope this makes sense, any help ...
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3answers
57 views

Transforming time series to compensate for change in variance

I have a time series (shown below) that comes from a sensor whose calibration was changed in the middle of last year. As part of this change, the sensor's reading of the variance (or volatility) of ...
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0answers
24 views

how to handle the imbalanced data in regression analysis

The problem here is very similar to the problem asked by someben in 2012 (link:Sampling for Imbalanced Data in Regression). It involves the linear regression analysis using an unbalanced dataset. Say, ...
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0answers
16 views

Untransforming unbiased estimates

Suppose I have some measured experimental data and I want to fit it to a power law of the form $y=ax^b$. Suppose I transform the data to log-log space and then I fit a straight line of the form ...
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0answers
18 views

How to eliminate dependent inputs?

There are a lot of statistical methods that rely on the assumption of input independence. For example, Naive Bayes text classifiers operate under the assumption that occurrences of different words are ...
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3answers
59 views

Ratio of explanatory variables in multiple regression

I wonder if anyone has any links or advice on specifying a ratio of two explanatory variables in a linear regression? That is, specifying the two independent variables plus their ratio. We have data ...
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0answers
28 views

Order of preprocessing steps in a binary classification problem

I have these stages (ordered) for preprocessing in my binary classification problem. Dividing data based on criteria (class1 and class2 databases) Outlier ...
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1answer
65 views

Spread-Level Plot versus Power Transformation Functions in R

I'm having trouble interpreting the results from the Spread-Level Plot function in R (car package). The documentation says: PowerTransformation spread-stabilizing power transformation, ...
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1answer
100 views

Regression where the dependent variable is the difference between two correlated variables — bias and other issues to consider

I am interested in estimating a regression that looks like this: $(x_{1,i} - y_{i} )_{i} = x’_{i}*\beta + \epsilon_{i}$ (1) However, I am not sure if doing this—in this form—is appropriate. ...
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28 views

Plotting data and polynomial equation when x-values have been transformed

I hope I can make my question clear, if not I will be happy to clarify. I want to present a simple regression plot with the polynomial equation line linked to it. Those data have been tested in a ...
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26 views

Have Narrow Distribution - Need Standard Distribution

I am building a model in R using GLM based on this predictor variable. As you can see, the data is concentrated in the center of the distribution, and then falls off sharply. Is there a ...
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0answers
24 views

Derivation of normalizing transform for GLMs

How is the $A(\cdot) = \int\frac{du}{V^{1/3}(\mu)}$ normalizing transform for the exponential family derived? More specifically: I tried to follow the Taylor expansion sketch on page 3, slide 1 of ...
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22 views

Transformation for zeros

I have been working on seed germination experiments with some dormancy breaking treatments and only for some treatments only I got germination. I need to analyze the parameters like days for initial ...
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1answer
32 views

Multlinear regression: analysis of residual of transformed response and predictor variables

In the first step of modeling a regression equation I came up with the following model: $T_c = 26.73 + 0.042{\rm Sc} + 0.247{\rm Lc} - 14.709{\rm Lf} + 1.41{\rm Lu} - 0.214{\rm Fc} + 0.041{\rm Ad} - ...
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40 views

How can i make a fraud detection dataset (I have the data ready but unordered)

I'm a little confused with the creation of the dataset for a fraud detection predictive model. Here i put a link with a sample of the dataset that I made. (the real dataset have ~950.000 clients). ...
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2answers
42 views

transformation of percentages (and eventual display)

I have some percentage data derived from the analysis of grain presence and absence in ears of wheat that have undergone two treatments (control and heat stress). For example, data might be like 10%, ...
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0answers
14 views

How to interpret log of independent variable in Poisson regression? [duplicate]

How to interpret log of independent variable in Poisson regression? Can we compare two independent variable one with Log and one without log in the model equation? For e.g. Y = b1*X1+b2*log(X2) Once ...
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30 views

Is this poor transformation advice for predictive modeling?

I have gotten some advice from a PhD statistician on doing predictive modeling on large datasets (lots of variables AND lots of observations) that I should perform transformations to eliminate ...
2
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1answer
57 views

Linear Discriminant Analysis and non-normal distributed data

If I understand correctly, a Linear Discriminant Analysis (LDA) assumes normal distributed data, independent features, and identical covariances for every class for the optimality criterion. Since ...
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0answers
88 views

Data preparation for Poisson regression: use of individual data

Most texts I have read about Poisson-regression assumes that the data is available in an already grouped form, i.e. counts are given for each unique covariate combination. For instance, we have (in R) ...
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1answer
68 views

Working with residuals of regression

So the background is that the I collected yield data for past 5-6 decades and location from where I collected yield data had high yielding varieties introduced over time. I am looking at the ...
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1answer
67 views

Normal distribution to triangular distribution

I would like to know if it is possible to convert a normal distribution into a triangular distribution. If it is, how it can be done? I know the mean and the coefficient of variation of the normal ...
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0answers
65 views

nMDS in vegan for soil data

I am working with abiotic soil data such as bulk density, moisture levels and soil chemistry as response data (some quantitative some as percentages) and a mix of abiotic and biotic data as ...
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0answers
5 views

Design a feature with time and presence information

Context: I am working on a decision tree classifier, trying to classify businesses as to whether they are likely to have an event occur (default) in the next 90 days. One input I get is whether, and ...
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16 views

strange coeficient estimates in GLS with ranked variables

Somebody could explain me why the estimated coefficients of a multiple regression through GLS seem not to pass through the majority of observations? Here is a example: ...
4
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1answer
98 views

Model with non-linear transformation

I don't understand this concept well and need help. I was choosing whether to use a linear model or apply a non-linear transformation in my model formula. To do a diagnostic, I quickly plotted my ...
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0answers
14 views

Mixing Categorical and Continuous variables where cardinality of categorical can surpass data points

Suppose we have a dataset of people that can be described with a mix of some continuous variables (eg height, age) some ordinal (eg social status) and some categorical (eg city, car brand, favourite ...
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1answer
89 views

Empirical logit transformation on percentage data

I have already used the logit transform on my outcome variables (which are displayed in percentages). However, this obviously gives me -INF values and since my data includes a lot of zeros in some ...
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0answers
20 views

clustering for histogram shapes

I am trying to get a start on a clustering problem. The sample data is trade volume at a particular price. Some notes about the data: number of bins vary from sample to sample (larger price range ...
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1answer
28 views

Transforming data for canonical correlation analysis

Okay, I'm a stats newbie so I'll try to be as specific and clear as possible. I have a set of predictor variables (2 predictor variables) and a set of response variables (7 response variables). I am ...