Understand, visualizing, and calculating p-value. Welcome to this lesson on calculating p-values. Before we jump into how to calculate a p-value, it’s important to think about what the p-value is really for.
Welcome to this lesson on calculating p-values.
Before we jump into how to calculate a p-value, it’s important to think about what the p-value is really for.
Without going into too much detail for this post, when establishing a hypothesis test, you will determine a null hypothesis. Your null hypothesis represents the world in which the two variables your assessing don’t have any given relationship. Conversely the alternative hypothesis represents the world where there is a statistically significant relationship such that you’re able to reject the null hypothesis in favor of the alternative hypothesis.
Before we move on from the idea of hypothesis testing… think about what we just said. You effectively need to prove that with little room for error, what we’re seeing in the real world could not be taking place in a world where these variables are not related or in a world where the relationship is independent.
Sometimes when learning concepts in statistics, you hear the definition, but take little time to conceptualize. There is often a lot of memorization of rule sets… I find that understanding the intuitive foundation of these principles will serve you far better when finding their practical applications.
Continuing on this vein of thought. If you want to compare your real world stat with the fake world, that’s exactly what you should do.
As you’d guess we can calculate our observed statistic by creating a linear regression model where we explain our response variable as a function of our explanatory variable. Once we’ve done this we can quantify the relationship between these two variables using the slope or coefficient identified through our ols regression.
But now we need to come up with a this idea of the _null world_… or the world where these variables are independent. This is something we don’t have, so we’ll need to simulate it. For our convenience, we’re going to leverage the infer package.
First things first, let’s get our observed statistic!
The dataset we’re working with is a Seattle home prices dataset. I’ve used this dataset many times before and find it particularly flexible for demonstration. The record level of the dataset is by home and details price, square footage, ## of beds, ## of baths, and so forth.
In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.
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