For Data Science Learners
Rejection Region in Hypothesis Testing, What is the definition of a Rejection Region? A rejection region is a section of a graph where the null hypothesis is rejected (assuming your test results fall into that area). Rejection Region The primary goal of statistics is to test theories or experiment results. For example, you may have…
An educated assumption about something in the world around you is a hypothesis. It should be able to be verified through experiment or observation. Consider a new drug that you believe may be effective. You believe there is a better method to teach. A possible new species’ habitat. A more equitable method of administering standardized…
Test for Normal Distribution in R, Many statistical tests, such as correlation, regression, t-test, and analysis of variance (ANOVA), presuppose that the data has particular features. They demand that the data follow a normal or Gaussian distribution. These tests are known as parametric tests since their validity is determined by the data distribution. Normality and…
glm function in r, we’ll look at what generalized linear models are in R and how to make them. We’ll also go over Logistic and Poisson Regression in depth. So, let’s get this tutorial started In R, what are Generalized Linear Models? In R, generalized linear models are an extension of linear regression models that…
Best online course for R programming, Do you want to learn how to program in R for free?. If you answered yes, this post is for you. The 7 Best Free R Programming Courses are listed in this article. Best online course for r programming You do not need to pay for any of the…
Create contingency tables in R, Contingency tables are helpful for condensing a huge number of observations into smaller, more manageable tables. We’ll learn about contingency tables and how to make them in this R tutorial. Complex/flat tables, cross-tabulation, and recovering original data from contingency tables will all be covered. As you can see, this course…
In this lesson, we’ll look at how to integrate R with Hadoop. For Big Data analysis, we’ll show you a variety of R and Hadoop integration strategies. When it comes to large data, R is the go-to tool for data scientists and analysts. It may be ideal for many data science jobs, but when it…
Read More “Methods for Integrating R and Hadoop complete Guide” »
Detecting and Dealing with Outliers, We’re going to look a little bit more at these mammals’ sleep data. Let’s take a summary of them. library(mice) summary(mammalsleep) species bw brw African elephant : 1 Min. : 0.005 Min. : 0.14 African giant pouched rat: 1 1st Qu.: 0.600 1st Qu.: 4.25 Arctic Fox : 1 Median…
Read More “Detecting and Dealing with Outliers: First Step” »
Dealing With Missing Values in R, one of the issues is that when you have a large matrix of data and some of the columns have a few missing values, it might be difficult to work with. Checking Missing Values in R – (datasciencetut.com) You won’t be able to perform a lot of multivariate or…
Checking Missing Values in R, we’ll undertake data wrangling, which is the pre-processing and preparation of data. In fact, practicing data science will consume more than 70% of your time. We’ll only look at a few of the most important commands to make things as simple as possible. However, you will devote a significant amount…
