Understanding Linear Regression https://hackaday.com/2025/05/08/understanding-linear-regression/ #linearregression #MachineLearning #math
Understanding Linear Regression - Although [Vitor Fróis] is explaining linear regression because it relates to machi... - https://hackaday.com/2025/05/08/understanding-linear-regression/ #linearregression #machinelearning #math
How linear regression works intuitively and how it leads to gradient descent
Assessment Of Snow Cover Dynamics And The Effects Of Environmental Drivers In High Mountain Ecosystems
--
https://doi.org/10.1016/j.eiar.2025.107969 <-- shared paper
--
#GIS #spatial #mapping #remotesensing #earthobservation #snow #ice #snowcover #dynamics #climatechange #mountains #ecosystems #spatialanalysis #spatiotemporal #MODIS #model #modeling #extremeweather #water #hydrology #climate #zones #trendanalysis #linearregression #RandomForest #cryosphere
Is machine learning merely a form of curve-fitting?
#machinelearning #ai #curvefitting #linearregression #buzzwords
Forecasting in Excel using Linear Regression
Forecasting #LinearRegression Hello Friends, In this video, you will learn how to do the sales forecasting in Excel. We have ... source
https://quadexcel.com/wp/forecasting-in-excel-using-linear-regression/
Bingqian Su et al. established a #Database based on #RobiniaPseudoacacia growth and its driving factors on China’s #LoessPlateau, developed #PlantGrowthModels considering #ForestAge, #Density, #ClimateFactors and #TopographicFactors using #LinearRegression and three #MachineLearningMethods.
https://doi.org/10.1093/jpe/rtae104
How to Train Machine Learning model withou ML Library with simple Python code a internal work ? then follow below link - it has video also
Accuracy! To counter regression dilution, a method is to add a constraint on the statistical modeling.
Regression Redress restrains bias by segregating the residual values.
My article: http://data.yt/kit/regression-redress.html
How to assess a statistical model?
How to choose between variables?
Pearson's #correlation is irrelevant if you suspect that the relationship is not a straight line.
If monotonic relationship:
"#Spearman’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".
"#Kendall’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."
Ref: https://statisticseasily.com/kendall-tau-b-vs-spearman/
#AI #interpretability vs #explainability 
"The explanations themselves can be difficult to convey to nonexperts, such as end users and line-of-business teams" https://www.techtarget.com/searchenterpriseai/feature/Interpretability-vs-explainability-in-AI-and-machine-learning
When and Why to Use #Bootstrap for #LinearRegression?
To explore the insights and rationale behind this approach, we invite you to read our latest #BlogPost.
>>> https://medium.com/@neuralrow/bootstrap-algorithm-for-linear-regression-3f20c27b9178
#MachineLearning #DataScience #NeuralRow #Statistics #Probability #DataAnalyst #Heteroskedasticity
#StatisticalInference #HypothesisTest
Master SAS/STAT for Complex Statistical Analysis | CoListy
Prepare for SAS/STAT certification, focusing on variance analysis, regression, and model performance. | CoListy
#freeonlinelearning #colisty #courselist #sas/stat #statisticalanalysis #linearregression #logisticregression #analysisofvariance #predictivemodeling #modelperformance #sascertification #dataanalysis #sasprofessionals #statisticalsoftware #modelpreparation
Redressing #Bias: "Correlation Constraints for Regression Models":
Treder et al (2021) https://doi.org/10.3389/fpsyt.2021.615754
"In real life, we weigh the anticipated consequences of the decisions that we are about to make. That approach is much more rational than limiting the percentage of making the error of one kind in an artificial (null hypothesis) setting or using a measure of evidence for each model as the weight."
Longford (2005) http://www.stat.columbia.edu/~gelman/stuff_for_blog/longford.pdf
The Coding Train dude is precious. This is the Math teacher I wish I had for every grade I was taught math. https://www.youtube.com/watch?v=szXbuO3bVRk #math #mathematics #linearregression
In Elisa Yao's newest article, she breaks down the process of implementing Linear Regression in Python using a simple dataset known as “Boston Housing”, step by step.
https://towardsdatascience.com/predict-housing-price-using-linear-regression-in-python-bfc0fcfff640
"Feature importance helps in understanding which features contribute most to the prediction"
A few lines with #sklearn: https://mljourney.com/sklearn-linear-regression-feature-importance/
Greg Cocks 
