Magic 8 Ball: An App to Maximize Wins in Competitive Pool Matches (Part 3). It has something special that is used by many players. Read our article here.
The basics of hypothesis testing are explained in detail in this article.
I will try to explain CLT so that you don’t get trapped in the same misunderstanding as mine in the above. In the end part, I also discuss the theorem’s importance in one of the core competencies that every data scientist should comprehend, namely hypothesis testing.
Example of Chi-Square Test in Python. We will provide a practical example of how we can run a Chi-Square Test in Python.
Hypothesis tests are significant for evaluating answers to questions concerning samples of data. In this article, you can explore a type of hypothesis, why we need it, and how to calculate it?
Standing on the null hypothesis, the teacher makes observations and sees the event which happens very rarely. That makes him doubt the hypothesis he made about the student initially. So, the teacher rejects the hypothesis and thinks that the student is lying.
There is a workflow that must be followed so that the correct assumptions can be made before ultimately deciding on which type of hypothesis test will be ran. In this post, I will be taking you through that workflow.
ML: Sampling Distribution & Z-test. In this post, we start to look at the specific methods for it. The first method we are going to study is Z-test.
The Ultimate Guide of Classification Metrics for Model Evaluation. Combine machine learning models with hypothesis testing. Stop pulling your hair and read this article.
As data scientists, we need to know the proper way to build a hypothesis and test it with the tools that we learn. This post will guide you to build a proper and solid hypothesis.
Let's make the “Confusion matrix” less confusing! The confusion matrix shows the ways in which your classification model is confused when it makes predictions.
Have a strong argument why picking a classification algorithm over the other based on the significance level in performance. There are many statistical hypothesis-testing approaches to evaluate the mean performance difference resulting from the cross-validation to address this concern.
Hypothesis testing is a type of statistical method which is used in making statistical decisions using experimental data. As we have already seen in Inferential Statistics and Central Limit Theorem(CLT), we will work with sample data and confirm our assumption about the population in Hypothesis Testing.
with Python simulation and examples. One of the most important concepts discussed in the context of inferential data analysis is the idea of sampling distributions.
Two common wrong phrases about statistical significance. You might see me twitch whenever I hear a colleague say “significant” when they clearly mean “statistically significant”.
In this article, I try to capture some of the advice I give to my students early on: what to look for in a research topic; and how to think about their research objective; and how to translate this into an appropriate research question(s) that will serve them well during their project.
Business analytics and data science is a convergence of many fields of expertise. Professionals form multiple domains and educational backgrounds are joining the analytics industry in the pursuit of becoming data scientists.
Linear Regression is the Supervised Machine Learning Algorithm that predicts continuous value outputs. In Linear Regression we generally follow three steps to predict the output.
I have read a few stories on Medium about writing advice, and there were some of them which, along with other tips, suggested that putting numbers in your story’s title will increase the number of views, as people tend to be more attracted by such headlines, and therefore, more people will click on your story.
Hypothesis tests are significant for evaluating answers to questions concerning samples of data. A statistical Hypothesis is a belief made about a population parameter.