A way of re-expressing data to make their values lie between 0 and 1 (or 0% and 100%).

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18 views

Normalize time series data - Wikipedia article counts

I have: 3 wikipedia article access counts (weekly) (A-B-C) Ground truth data (weekly) Total wikipedia english article traffic counts (weekly) My purpose is, build a multiple linear regression ...
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1answer
26 views

Should I normalize the data (capital stock series) after deflating it with whole price index? [on hold]

I am working on across industries. I want to know that after deflation capital stock of large scale industries with whole sale price index, Is there need of normalizing the data series?
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2answers
89 views

What are the real benefits of normalization (scaling values between 0 and 1) in statistics?

I am working on student data set in which I want to normalize the range of percentage to 0,1. But I am not clear with the actual benefits of normalizing a range.
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26 views

Weighted Least Squares Normalization for Parameter Uncertainty

I want to fit a function $f(x_1,x_2..)$ to (noisy) data with unknown variance. For each datapoint, I have a weight $w_i$ which is proportional to the reliability of that particular datapoint. The real ...
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17 views

Data normalization of different data ranges in R with reduced data loss

I have a data.frame with columns that contains data in different ranges. For example, below is the max and min values for these columns: ...
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41 views

Using variance in place of standard deviation for z-normalization

I'm implementing a 1-nearest neighbor (with dynamic time warping as the distance measure) classification algorithm on a severely constrained embedded platform with no FPU, so we're doing fixed point ...
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0answers
17 views

Normalization of scale in cluster analysis

I have 16 variables which are scaled 1-5, 5 variable scaled 1-4 and 1 variable scaled 1-10. I suppose I will need to do normalization before applying cluster analysis. Variable response is in likert ...
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1answer
32 views

Normalizing a Continuous Variable for Appropriate Use Alongside Binary Variables

I am fitting a model where I estimate my Dependent Variable based on about 20 Binary Variables (0/1), and one continuous variable. I've read about the importance of normalizing that continuous ...
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10 views

Data transformation: normalizing the rows or the columns of a data matrix?

I am a bit confused on the kind of data transformation I have to use for analyzing my data. I have a matrix $X$, where rows are genes, columns are individuals, and entry $X_{i,j}$ is a value of ...
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43 views

Normality Problems [duplicate]

I'm doing a regression analysis which involves 4 independent variables (IV). I performed a Shapiro-Wilk test to test the normality of of each of the IVs and it turned out that the the test showed a ...
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1answer
27 views

How to remove normalization function on data? [closed]

I currently have my data scaled between the range of 0 and 1 using the following normalization function in R: ...
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1answer
29 views

Will normalizing training and testing data separately cause under/overfitting?

Suppose I have training and testing data and I want to train a classifier (e.g. SVM). Typically, features are normalized before classification to ensure some features aren't weighted more heavily than ...
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2answers
60 views

Kernel of a Normal Distribution

From Wikipedia , The kernel of a probability density function (pdf) or probability mass function (pmf) is the form of the pdf or pmf in which any factors that are not functions of any of the ...
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2answers
88 views

Is cosine similarity identical to l2-normalized euclidean distance?

Identical meaning, that it will produce identical results for a similarity ranking between a vector u and a set of vectors V. I have a vector space model which has distance measure (euclidean ...
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0answers
14 views

Why is the Multivariate RMSD “normalised” differently to the NMB?

For the multivariate case in regression, and also in other model predictions, the Root Mean Squared Deviation (RMSD or RMSE) is normalised by $n-p-1$, giving, $$RMSD = \sqrt{\frac{\sum_{i=1}^n ...
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24 views

Repeated measures ANCOVA

I have a data set with training effects and I measured the outcome variable before, during and after training. This is a repeated measures design and I hope to find an effect of training on the ...
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24 views

Normalized data and regression

Suppose I have eight subjects and measured performance in a time series (outcome measure is a distance measure). I assess learning effects across these time points by expressing the increase in ...
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0answers
19 views

Normalize(Scale) data before sampling or after sampling in binary classification?

I have a binary classification database with imbalance outputs (1 labeled data: 1400 samples, 0 labeled data: 200 samples). I balance data based on a criteria to (200 - 200). Where should I normalize ...
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1answer
53 views

Data normalization choice [duplicate]

What are the main advantages and disadvantages of normalization between 0 and 1 or the other zero mean variance one algorithm? If we want to preprocess the data, how to select either of these two ...
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1answer
23 views

Normalizing data before estimation of Granger causality?

I want to estimate granger causality between two series. Visual inspection indicates it might be useful to normalize data first (i.e. (X-mean(x))/ (sample stdev(x)) ) Are there some caveats ...
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9 views

Combining and normalizing gridded parameters of different distributions with the same units

I have a grid that has two parameters for each cell. One parameter, let's call it K, is the same for all grid cells. The other parameter, let's call it M, has values of M, 2M, and 3.5M, with each ...
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35 views

Standardization (z-score) across the “Samples” or across the “variables”?

I found in literature that one of the most common way of standardization data is to compute z-scores (mean subtraction and division by standard deviation). Can anybody tell me if it is ok to compute ...
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30 views

normalizing data for neural network

I'm working on a neural network with back propagation for indoor localization. The input of the neural network is Received Signal Strengths (RSSs) and the output is a coordinate (x,y). I have ...
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1answer
24 views

RMSE normalisation, what method prefer?

According to this article on wikipedia http://en.wikipedia.org/wiki/Root-mean-square_deviation, two methods are widely used to normalise the RMSE. The first is dividing by the range: $$NRMSE = ...
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29 views

Z-score across the “Samples” or across the “variables”?

I found in literature that one of the most common way of standardization data is to compute z-scores (mean subtraction and division by standard deviation). Can anybody tell me if it is ok to compute ...
1
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1answer
49 views

De Normalize data

How would I de normalize the values which where normalized by the min max normalization below ?
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2answers
30 views

Data normalization for RBF kernel

I have a matrix of values where rows are individuals and columns are attributes. I want to extract a similarity value for every pair of individuals, and I use an rbf kernel: $$k(x_i,x_j) = ...
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1answer
43 views

Some issues with standardized variable in a regression analysis

I'm solving this multiple choice question on the properties of a standardized variable. Two of the possible options (which are wrong but look right to me) are 1. It is always normally distributed and ...
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1answer
45 views

What's the best approach for results of a running race?

I am a student in a good statistics program, but I'm not always the best at picking the tools/process to apply to a problem. To be clear, this is NOT homework, I am asking for a project that I have in ...
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17 views

normalization of data points in order to make them adhere to a specified probability distribution (e.g. Normal)

I am doing some preprocessing for a computer vision task. My target is to select a few elements (pixels) containing highest scores according to a metric that I am developing. The values of this metric ...
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26 views

Normalizing interview scores

Our school debate team is interviewing potential new members. There are a lot of applicants, and so we have decided to only have six of us present at any given interview. We are judging each of the ...
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23 views

Does centering or mean normalizaiton alone every help in feature scaling?

In feature scaling, one way is to subtract the mean (centering) and then divide by the standard deviation for all data points. Suppose we just centered the data and didn't divide by the standard ...
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1answer
61 views

Feature normalization in Text Classification

I'm doing Text Classification in R, and my initial features are just word frequency inside a Document. For example: ...
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32 views

How do I do RLM normalization in R?

I have a combined dataset of 123 samples and 9,482 features (expression levels of antibodies from Invitrogen's ProtoArray v5.0). Based on ...
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12 views

Normalizing Concepts in an Ontology

Background I would like to develop a simple ontology (2-3 levels) for example: ...
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1answer
217 views

Normalizations: dividing by mean

I'm reading various papers and I don't understand the meaning of three types of normalizations used. Let's say I have the number of calls $X_i(t)$ in region $i$ at time $t$. I see it normalized with: ...
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18 views

Creating A weighted Scorecard using Stats

Im new to forum and have tried reading some articles to solve my problem but had trouble understanding some of it and others might not have been completely relevant. Below I will outline my problem ...
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1answer
28 views

NLP: How to do feature normalisation for gender classificiation?

NOTE Before I begin, this F-measure is not related to precision and recall, and its title and definition is taken from this paper. I have a feature known as the F-measure, which is used to measure ...
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33 views

Baseline normalization

Let's assume that I have data that is a 10x90 matrix (10 variables, 90 samples). 90 samples are divided into three groups: 30 - baseline 30 - treatment-1 30 - treatment-2 Only treatment samples ...
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1answer
113 views

How to handle data normalization in kNN when new test data is received

I had a discussion with my colleagues about the following problem: Lets say we have 100 points of labeled data and we are using $k$-nearest neighbor method for prediction. So our data looks like ...
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11 views

Mediation using normalized variables

If I normalize (range 0-1) variables x, m, y prior to conduct a mediation (OLS), do I report them as "fully normalized coefficients". How do I interpret the results? I am used to report unstandardized ...
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54 views

Normalizing bimodal distribution

I have two sets of student test data (Version 1 and Version 2). These tests were given to over 500 students. Both tests appear to have a bimodal distribution, and one test appears to be harder than ...
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1answer
73 views

Weekly data normalization - Python

I have a weekly dataset and I have to normalize this data. Data is something like this : ...
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25 views

(LDA) Topic Modeling: eliminate Junktopics through normalization

Question: Is it reasonable to normalize topics to eliminate junk-topics and get a better distinction of document-relations? I used the MALLET-LDA Java-library to estimate a ParallelTopicModel with ...
4
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1answer
88 views

normalization effect on polynomial regression

I need to know why input normalization has no effect on polynomial regression. Here there is a good explanation proving that column normalization does not affect linear regression. But I need to know ...
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1answer
55 views

Tsallis and Renyi Normalized Entropy

I'm working with Shannon, Tsallis and Renyi entropies. I need to normalized these entropies for comparison purposes. In Shannon's entropy you need only to divide by the log of the number of bins. ...
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63 views

High variability in data set, how to normalize? [closed]

For my research I'm doing FACS analysis on neutrophils isolated from venous blood. The blood is taken from different individuals each day so there is high variation in the results, resulting in a high ...
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0answers
28 views

removing batch effect when combing patient's data into a large cohort

I have some clinical data quantifying severity of disease for patients from 3 different hospitals. Basically, the patient severity vector for each hospital looks like below: ...
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0answers
38 views

How to normalize scoring to eliminate bias?

Background: I'm writing an app to allow multiple people to rate an object on a scale of 1-10. There are a few hundred objects to be rated, so I'd like to allow the reviewers to review what they can, ...
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19 views

what's your process for normalizing data?

I have a time series data set that I want to put through a simple linear regression. Besides guessing, what stat tests or processes can I use to normalize outliers?