Tag: regression

Feed: R-bloggers. Author: finnstats. [This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. finnstats can help you improve your data abilities and advance your profession. Homoscedasticity in Regression Analysis, The Goldfeld–Quandt test checks for homoscedasticity in regression studies in statistics. Subscribe This is accomplished by separating a dataset into two portions or groups, which is why the test is also known as a two-group test ... Read More

Feed: R-bloggers. Author: finnstats. [This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Regression in R, In a recent article, we discussed model fitting and selection. However, we haven’t considered how we’ll choose which variables to include in our model. Simple Linear Regression in r » Guide » Let’s go over our linear regression model for the mtcars data.frame again. Regression in R mtcars.lm <- ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. It can be frustrating when the same probability distribution has two different parameterizations, but such is the life of a statistical programmer. I previously wrote an article about the gamma distribution, which has two common parameterizations: one that uses a scale parameter (β) and another that uses a rate parameter (c = 1/β). The relationship between scale and rate parameters is straightforward, but sometimes the relationship between different parameterizations is more complicated. Recently, a SAS programmer was using a regression procedure to fit the parameters of a Weibull distribution. He was ... Read More

Feed: R-bloggers. Author: Selcuk Disci. [This article was first published on DataGeeek, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. The higher education students have had trouble being housing in Turkey in recent days. There have been people who even sleep on the streets like a homeless. The government has been accused of investing inadequate dormitories for sheltering the students. Let’s examine the ongoing sheltering problem of students. The dataset we have built ... Read More

Feed: R-bloggers. Author: finnstats. [This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Simple linear regression in r, we want to create models to investigate and forecast the relationship between variables, and the most basic relationship that we can think of is a straight line. Visit finnstats.com for up-to-date and accurate lessons. Let’s take a look at the first linear relationship that we are going ... Read More

Feed: R-bloggers. Author: T. Moudiki. Last week I presented ahead, an R package for univariate and multivariate time series forecasting. In particular, the function dynrmf was introduced for univariate time series, with examples of Random Forest and Support Vector Machines fitting functions (fitting and predicting through fit_func and predict_func arguments of dynrmf). First things first, here’s how to install R package ahead: 1st method: from R-universe In R console: options(repos = c( techtonique = 'https://techtonique.r-universe.dev', CRAN = 'https://cloud.r-project.org')) install.packages("ahead") 2nd method: from Github In R console: devtools::install_github("Techtonique/ahead") Or remotes::install_github("Techtonique/ahead") In version 0.2.0 of ahead, Ridge regression is the default fitting ... Read More

Feed: SAS Blogs. Author: Udo Sglavo. Jan Chvosta Note from Udo Sglavo: In our previous post, Jan Chvosta, the director of Scientific Computing at SAS, and I discussed the origins of regression analysis and some of the ways it is used today. Now we will further discuss the power of regression framework and choosing the correct regression model. Udo: In the previous post we discussed the power of the regression framework. In a way, all practitioners are attempting to accomplish a similar task. They want to choose the best regression model and fit it to the data available.  It sounds simple ... Read More

Feed: R-bloggers. Author: Nagdev Amruthnath. [This article was first published on R – Hi! I am Nagdev, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Regression analysis is a process of building a linear or non-linear fit for one or more continuous target variables. That’s right! there can be more than one target variable. Multi-output machine learning problems are more common in classification than regression. In classification, the categorical target variables are encoded ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Sergey Lukyanchikov. Summary Regression analysis is one of the methods supplied “built-in” with SAP BW Data Mining. Based on this method regression models can be created and configured to satisfy specific analysis requirements (e.g., choice between linear or non-linear approximation, etc.). The method includes regression-specific reporting that allows analysis of the modeling results. In this paper we are suggesting a number of ways to extend this reporting in order to improve insight into the results of regression modeling. The mentioned extensions are implemented via the following analytics: Dashboard - SAP BW ... Read More

Feed: R-bloggers. Author: R on Stats and R. Remember that descriptive statistics is a branch of statistics that allows to describe your data at hand. Inferential statistics (with the popular hypothesis tests and confidence intervals) is another branch of statistics that allows to make inferences, that is, to draw conclusions about a population based on a sample. The last branch of statistics is about modeling the relationship between two or more variables. The most common statistical tool to describe and evaluate the link between variables is linear regression. There are two types of linear regression: Simple linear regression is a ... Read More