Questions tagged [univariate]
Pertaining to a single variable. Univariate statistics deal with only one variable - e.g. the mean, standard deviation, range etc. Univariate distributions involve only one variable e.g. the univariate normal, uniform etc. distributions.
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Bonferroni correction necessary? regression to predict ONE outcome
I am working on a project with a small sample size where I have multiple predictors at baseline and one IV. I am trying to see if any of the DVs are good predictors for the score on the IV (continuous)...
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Why can't fit I fit a multivariate regression (OPLS) model when my variables are univariately (Pearson/Spearman) correlated?
I have a dataset of 950 lipids (X) and want to see if any are correlated with cognitive function (Y). When I try to fit an opls regression model, it errors and says that "No model was built ...
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Potential evaluation based on the coherence of predicted value with actual data
I have the following data over time:
that means data collected for a single variable like CPU usage in lowest, highest, and average mode over time every 5 mins (data granularity = 5mins) like the ...
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Getting an equivalent of R-squared for simple univariate regression done with structural equation modeling
I need to calculate a very simple regression model outcome ~ predictor. To treat missings, I have to use FIML and also I need bootstrapping. Since the ...
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Weighted summing time associated data for univariate model?
I have a situation where I have several mice A, B, C, etc., each of which has several thousand datapoints, all associated with a timepoint of 1 year, in addition to a single datapoint associated with ...
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Evaluate upper bound prediction results using classic error calculation instead of PI metrics
I have the following data over time:
that means data collected for a single variable like CPU usage in lowest, highest, and average mode over time every 5 mins (data granularity = 5mins) like the ...
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Beginner - Univariate Analysis
I am trying to perform an analysis based on a survey response 'yes' vs 'no'. The response has multiple categories. For example, I am looking at a variable 'mechanism of injury' that has 4 categories, ...
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Multiple Univariate regression (lm) using a for loop but problem with missing values. (Using R studio) [closed]
I want to execute this code that work well when there is no missing values :
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Correcting p-values when accounting for age and sex in a study where the main objective is to find differences between disease/control
So I analyzed my metabolomics data using Mann Whitney U tests and OPLS-DA. My data is super small, with the control group only having 5 samples and the other groups having 10 and 14 samples ...
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Anomaly Detection in Multivariate and Univariate timeseries
I just started exploring Anomaly detection in timeseries for Univariate, Multivariate timeseries.
I read few articles about it, few research papers as well. But every article/research paper has ...
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Univariate vs. Multivariate Standardization
There are several common methods for scaling input features to machine learning models prior to training the model. The most popular methods seem to be standardization (centering by the mean and ...
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How to classify univariate time series data in real-time? [closed]
I have a robotic arm doing three different tasks, each task is colored differently as seen below. Data correspond to a Z-axis of the gyroscope sensor.
The idea is to detect anomalies. However, I need ...
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How to implement Girardi & Ergun's (2013) three-step multivariate GARCH estimation of CoVaR in R?
I'm trying to calculate multivariate GARCH estimation of conditional value-at-risk, by adopting a three-step model from Girardi & Ergun (2013) paper entitled "Systemic risk measurement: ...
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what R function can be used to fit an additional MA term at lag 3 to my ARIMA model?
I want to fit an ARIMA(1,1,0)(0,1,1)[12] with drift, with an additionnal MA at lag 3 as when I have fitted an ARIMA(1,1,0)(0,1,1)[12] with drift model, I have seen there was still autocorrelation in ...
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Selecting optimal lag values for Neural Network in univariate time series forecasting - How many lags to use as input variables?
What is the recommended approach for selecting lag values in a univariate time series forecasting problem, specifically for input variables in a feedforward neural network (FFNN)? In my research ...
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