Linear regression is also known as ordinary least squares (OLS) and linear least squares, and it opens the doors into the regression world. Linear regression is one of the most widely known modeling techniques and is usually among the first few topics that people master when they learn predictive modeling. We differentiate between a simple and multiple linear regression, and in this article, we’re going to focus on these two.
In this article, we are going to learn about one of the most significant predictive analytics tools for machine learning and big data – regression. We are going to define it, learn why and in which cases we use it. We are also going to take a look at seven types of regression analysis – we are going to learn which variables are correlated with specific regression techniques and we are also going to discuss some of the key factors associated with each technique.
In this article, we are going to learn a bit more about a popular method of creating and visualizing predictive models and algorithms – decision trees. We are going to learn what are decision trees, what are the types of decision trees and when you should use each. Finally, at the end of the article, we will take a look at the advantages as well as disadvantages of using decision trees.
In this guide, we will explore Central Processing Unit (CPU) and its role in the computer system and programming.
We will start exploring CPU role from the perspective of a simple question people ask themselves: How many CPU cores do I need? In addition to this, we will try to answer should a software development computer have as many cores as possible?
This article will take you into the world of predictive analysis. We will learn why is important and what are its benefits. We will take a look at a few examples of businesses that use it, and most importantly we will explore the three common types of predictive analytical models used in predictive analytics – decision trees, regression, and neural networks. In addition to that, we will take a look at predictive analytics tools that are powered by even more models, such as classification models, clustering, forecast, outliers, and time-series models among many, as well as and 5 common predictive analytics algorithms that can be applied to a wide range of use cases.
“Bi” means two and the term bivariate analysis refers to the understanding of the relationship between two variables. In comparison, univariate analysis is about analyzing one variable, while multivariate analysis refers to understanding relationships between more than two variables.
Bivariate analysis has a lot of use in real life because it can help understand the strength of the relationship between the two variables. But before bivariate analysis can define the strength of relationship, it first must define whether there is any casualty and association between two variables – whether the value of the dependent variable will change if the independent variable is modified.
In this article, we are going to learn about the for loop in PHP – we are going to look at the for loop syntax through a simple example. In the end, we will learn what can we do if we want to introduce two conditions in a for loop and find a minimal value.
This article is going to scratch the surface of descriptive statistics – we are going to define it and see what purpose it serves. With a help of descriptive statistics, we are going to take a look at univariate analysis – an analysis of a single variable – and we will observe the three major characteristics when observing a single variable – the distribution, the central tendency, and the dispersion, and we are going to take a quick peek at bivariate and multivariate analysis, so you have a better understanding of what analyzing one, two or more variables mean.
What is a variable and what do we do with a variable? This and many more are just some of the things that we are going to learn in this article. These are the very basics of data science, but they are super important before you take a leap into topics that are much more complex. So, let’s start.