Questions tagged [standardization]

Usually refers to "z-standardization" which is shifting and rescaling data to assure they have zero mean and unit variance. Other "standardizations" are possible, too.

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How to standardize multiple time series for GAN

I have a set of 150 multivariate time series, each containing 10 variables measured at 50 time points. The goal is to generate artificial time series which are similar to the ones I have, so I trained ...
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Variable Importance sorting by absolute value of x or fully standardized coefficient?

I am looking at the output of a linear regression model and would like to sort the IVs by feature importance. In this case I want to use the absolute value of the standardized coefficients since my ...
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Getting singular fit error on lmer model after standardizing the response variable

I'm running a mixed model with the lmerfunction in R, and am running into an issue with singular fits. My dataset is comprised of 48,538 observations of sleep ...
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1answer
36 views

PCA; variance, interpretability, and scaling

I've been going through threads about PCA and the predictive power of various axes, but since I cannot comment, I am opening a new question. There is a lot of discussion whether PCA components with ...
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Do I need to standardize my data?

I have the results of mass spectrometry, there are 2500 peaks and in each peak there is the recorded intensity for the samples. My data looks like this: ...
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42 views

Mean-centering changes the values of regression coefficients with interaction terms [duplicate]

To my knowledge, mean-centering does affect the values of regression coefficient for variables involved with interaction terms. But it does reduce the standard errors of the coefficient estimates for ...
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26 views

Multicollinearity - mean centering does not reduce the confidence interval of interaction terms?

I generated synthetic high-collinearity 10,000 datasets (each of sample size 1,000) using high-covariance matrix with multivariate normal distribution. I fitted 10,000 different linear models and ...
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1answer
71 views

What to report when p-values of Standardized and Unstandardized Beta disagree?

I am testing the following models. Volumes were purposely log10 transformed to examine brain allometry. ...
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How to interpret standardized coefficients for an interaction effect between continuous and categorical variables?

It is conventional, in some circles, to standardize all continuous variables before conducting OLS regression. It is argued that this actions makes it possible to rank the effects observed, and hence ...
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Standardizing regression coefficients in the case of Total Least Squares Regression

So I know that there exits a method to standardize regression coefficients by simply multiplying them with the ratio of the standard deviations of the two variables. I'm wondering if this holds true ...
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33 views

Look ahead bias induced by standardization of a time series?

Let's say I'm using some machine learning model to predict future values of a time series (e.g. stock price, air temperature, etc). In my model, I'm using some autoregressive features such as lagged ...
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20 views

Can one use weights from PCA on non-standardised data?

I want to extract the first principal component from a set of data which contains 12 variables. The data, ie all 12 variables, all have the same units of measurement (that is percentage point ...
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1answer
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Can the output of a probit regression with a Z-score predictor be interpreted as a standardised effect size?

I want to obtain a standardised effect size for a regression with a binary outcome. If I standardise the (continuous) predictor and run a probit regression, the resulting coefficient can be ...
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1answer
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Using Lasso Regression coefficients for prediction and reporting

I am running a Lasso regression for a model with one target and several predictors. I have standardized the predictors (but not the target) before running the regression. The results I am getting ...
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45 views

Using standardization with small noisy values

Let's say we have a dataset of how often people say certain words that looks like ...
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Which mean and standard error should be used when standardizing a variable coming from a different data set? [closed]

I'm leveraging data from the Census Bureau's ACS 5 year summary data, which includes point estimates and margin of error for variables at the census tract level. For example, the median income for ...
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What is best practice when standardizing a truncated numeric variable with lots of zeroes?

What is best practice when standardizing truncated numeric variables with lots of zeroes (like 80% of the obs.)? To provide an example, I have a variable counting number of days per year several ...
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Standardize by min, max, and arbitrary chosen middle to range from -1 through 0 in the middle to +1 in max

How to standardize data by the following example: ...
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Why is R-squared equal to the sum of standardized coefficients times the correlation?

Reading about standardized coefficients I came across the following formula: $$R^2=\sum\beta_ir_{yi}$$ Where $\beta$ is the standardized coefficient for the independent variable $i$ and $r_{yi}$ is ...
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Accounting for different factors during construction of hypotheses

I am a beginner in statistics (we are using statistics in a Data Science introduction course) We are learning about hypothesis testing and we have been asked to write Python code to perform ...
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Normalization of training and test set with data leakage

I have a time series data set for actual number of airport passengers. Within 15 years (2004 ~ 2019), just like having a trend, number of the passengers is increasing over time as the country is ...
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19 views

Regarding mean centering, for subsequent linear and exponential modeling

I'm centering my age variable using R's center() function (for reference). I am modeling the linear, quadratic, and cubic effects of age. I am wondering if I should either: Mean center Age, then ^2 ...
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Standardize data before plotting learning curve

I have implemented cross validation function with hyper parameter tuning. Basically, doing the following: Split the data into 80% training, 20% testing apply cross validation with hyper parameter ...
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Standardization breaks my Lasso regression model [closed]

I'm trying to apply a Lasso regression model on some data (the ultimate goal is to perform feature selection) using sklearn. When the data features are not standardized, the resulting test score is ...
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1answer
79 views

If simple linear regression model is valid model for a dataset, the plot of the standardized residuals versus the fitted values's approximately linear

I have to decide if the sentence is true or false and then explain why. ''If the simple linear regression model is a valid model for a dataset, the plot of the standardized residuals versus the ...
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Covariance and Correlation

I'm new to Statistics. How do we calculate the correlation coefficient from covariance, by Standardization or Normalization? I understand, that for better interpretation, we calculate correlation ...
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How would I calculate the standardized value of y given the standardized value of x and

If the correlation between two variables, $x$ and $y$, is $-1/2$, and the standardized value of $x$ is $-4$, how would I predict the standardized value of $y$ with just those two pieces of information?...
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Standardise components of an additive model output

I've got a sales forecasting model using the fbprophet library. The model is additive: calculates a base trend and then adds ...
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Using standardization methods to exclude erroneous outlier values

As my data has a lot of outliers, using mean to standardize data doesn't seem to be optimal. I'm experimenting with using median to classify outliers and stumbled upon robStandardize function from ...
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57 views

Standardizing data produces negative values

I am working on a basic house price prediction problem with traditional ML algorithms, not NN since the size of data is small comparing to the number of features. The issue I am having is that many ...
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63 views

Small Sample Size and NOT normally distributed

Say $n_1=4$ and $n_2 = 5$. If I want to compare the mean, should I: Standardize the data (i.e., using the scale() function in R), then perform t-test. This is because t-test required the data to be ...
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When comparing the ACT and SAT by converting both measurements into z scores, why is it that the person with the smaller z score has the better test?

I was reading this article: https://medium.com/omarelgabrys-blog/statistics-probability-probability-distribution-35818d301cf4#fbef and, in one section of the article, the author compares two students....
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1answer
37 views

RMSE with and without standardizing the output variable

I have a time series data that I would like to be able to forecast. I was trying to standardize the data as my columns are all of different ranges. I have standardized the input variables, but was ...
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2answers
35 views

Transformed continuous variable Z-Score to Percentile

I had a non-normal distribution of my variable of interest which required log10-transformation to reduce outliers. This was then standardized, to a mean of 0 and sd of 1 (z-score?). I now want to ...
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De-standardizing covariance matrices after applying PCA?

If I decide to use PCA to estimate a high-dimensional asset portfolio covariance matrix using reduced dimensions, I can use the following procedure to transform the low-dimensional matrix back to the ...
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1answer
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Why standardization of design matrix $X$ with factor $\frac{1}{n}$ instead of $\frac{1}{n-1}$ in lasso/glmnet?

I'm a little bit puzzled by the default standardization of the lasso/elastic net/ridge regression algorithms implemented in the (great!) glmnet package. In most other applications, people would ...
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What type of Normalisation technique is best for image data before applying any CNN deep learning model on type of it? [duplicate]

How to decide upon the normalisation that need to be used for image classification or cv problems , Is there any standard on what to choose when in particularly with Image data ?
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Feature Scaling/Standardization or Change Point Score?

I've different data sets that have the feature Volume. This feature represents the absolute number of events. Each observation represents a period (a fixed period such as 15 minutes). You can imagine ...
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1answer
37 views

CFA: scaling of measured variables/indicators

I'm running a latent variable analysis with: 166 observations 21 continuous variables using the R package lavaan A simple run of ...
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1answer
87 views

Two questions about standardization and overfitting [closed]

Question 1 Why neural networks (or more generally, any machine learning models) tend to overfit smaller datasets? The "default" reason is that the information associated with the smaller dataset is ...
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27 views

Effect of learning rate (and standardization) on NN with ReLu layers [duplicate]

I'm trying to understand the effect of the learning rate on a 10x10x10x10x4 sequential NN. Where each hidden layer is ReLu and the output layer being Softmax. I know the theory: low rate -> slow ...
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1answer
35 views

Significance for regression or standardized regression coef

I'm calculating multiple regression with R and trying to decide which predictors to keep and which to drop. I realized that when I use the lm.beta function I'm not ...
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30 views

Why would you subtract the mean of your variance from your variance? [duplicate]

i recently stumbled over the following codeline: variancedm<-variance-mean(variance) Is this a common way to normalize/standardize variance ? Why would you ...
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2answers
87 views

Do we need to standardize when our data is univariate?

In this question: What algorithms need feature scaling, beside from SVM? it is said that we need to standardize so that all features are weighted equally. But what if we only have as features: time ...
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1answer
32 views

Interpretation of a regression coefficient

How should I interpret my regression result as my independent variable is in log format and my dependent variable is standardized to a mean of zero and standard deviation of one. More specifically, is ...
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1answer
246 views

Should you standardize your variables before or after removing outliers?

Barring the question of how to operationalize outliers, or the utility of doing so, and assuming dependent variables and independent variables are all scaled in the main regression specification (...
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transform different measures for correlation analysis

I inherited data from different companies which used different tools to measure customer preference. Now I would like to correlate customer preference with another variable. The problem is that ...
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Which Comes First: Standardization or Transformation?

I have a data frame that contains a few variables where the skew is larger than 1. Also most of the variables have vastly different scales. I am looking to scale the data using R's scale() function (...
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1answer
55 views

Help needed to Interpret ln(y) = a +b (Standardized X)

I am analysing server data and I have a scenario where I need to get the % by which Y is changed because of a unit change in X: EDIT: I am doing a Linear Regression in Python (and its other forms ...
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66 views

Difference between standardizing variables and using Mahalanobis distance

I am wondering how and/or why the Mahalanobis distance is different from using the Euclidean distance on standardized variables?