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Stephanie Glen's Blog (40)

P-Value Explained in One Picture

P-values ("Probability values") are one way to test if the result from an experiment is statistically significant. This picture is a visual aid to p-values, using a theoretical experiment for a pizza business.…

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Added by Stephanie Glen on October 18, 2019 at 8:47am — 2 Comments

Difference Between Correlation and Regression in One Picture

Correlation and regression analysis both deal with relationships between variables.  There are many different types of correlation and regression; This image focuses on the differences between the two most common ones: Pearson correlation and…

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Added by Stephanie Glen on October 10, 2019 at 12:01pm — No Comments

Significance Level vs Confidence level vs Confidence Interval

You may have figured out already that statistics isn't exactly a science. Lots of terms are open to interpretation, and sometimes there are many words that mean the same thing—like "mean" and "average"—or sound like they should mean the same thing, like significance level and confidence level.  

Although they sound very similar, significance level and confidence level are in fact two completely different concepts. Confidence levels and confidence…

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Added by Stephanie Glen on September 30, 2019 at 12:00pm — No Comments

Correlation Coefficients in One Picture

Correlation coefficients enable to you find relationships between a wide variety of data. However, the sheer number of options can be overwhelming. This picture sums up the differences between five of the most popular correlation coefficients.…

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Added by Stephanie Glen on September 22, 2019 at 6:03am — No Comments

Difference Between Stratified Sampling, Cluster Sampling, and Quota Sampling

What is the Difference Between Stratified Sampling and Cluster Sampling?

The main difference between stratified sampling and cluster sampling is that with cluster sampling, you have natural groups separating your population. For example, you might be able to divide your data into natural groupings like city blocks, voting districts…

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Added by Stephanie Glen on September 14, 2019 at 5:28am — No Comments

Machine Learning vs Statistics vs Statistical Learning in One Picture

Statistics, Statistical Learning, and Machine Learning are three different areas with a large amount of overlap. Despite that overlap, they are distinct fields in their own right. The following picture illustrates the difference between the three fields.

References…

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Added by Stephanie Glen on September 6, 2019 at 8:47am — No Comments

One Way vs Two Way ANOVA + Factorial ANOVA: A Comparison in one Picture

ANOVA is a test to see if there are differences between groups. Put simply, "One-way" or "two-way" refers to the number of independent variables (IVs) in your test. However, there are other subtle differences between the tests, and the more general factorial ANOVA. This picture sums up the differences.…

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Added by Stephanie Glen on August 27, 2019 at 9:21am — No Comments

Descriptive vs. Inferential Statistics in One Picture

This simple picture shows the differences between descriptive statistics and Inferential statistics. 

More information about Descriptive and Inferential Statistics:

What is "Descriptive…

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Added by Stephanie Glen on August 24, 2019 at 7:32am — No Comments

Z Test / T Test in One Picture

The following picture shows the differences between the Z Test and T Test. Not sure which one to use? Find out more here:…

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Added by Stephanie Glen on August 13, 2019 at 10:30am — No Comments

Machine Learning in Hospitals: Easing Wait Times in the ER

Like many emergency rooms in the United Kingdom, the A&E department at Salford Royal NHS Foundation Trust, Greater Manchester, faces high congestion. This results in treatment delays and access issues. The Data Science team at the Northern Care Alliance (NCA) National Health Service (NHS) Group of hospitals is implementing support mechanisms to ease wait times, using machine learning and regression to…

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Added by Stephanie Glen on August 5, 2019 at 5:29am — 1 Comment

Regression Analysis in One Picture

The basic idea behind regression analysis is to take a set of data and use that data to make predictions. A useful first step is to make a scatter plot to see the rough shape of your data.…

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Added by Stephanie Glen on July 31, 2019 at 4:00am — No Comments

Comparing Model Evaluation Techniques Part 3: Regression Models

In my previous posts, I compared model evaluation techniques using Statistical Tools & Tests and commonly used Classification and Clustering evaluation techniques

In this post, I'll take a look at how you can compare regression models. Comparing…

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Added by Stephanie Glen on July 24, 2019 at 3:12pm — No Comments

Comparing Model Evaluation Techniques Part 2: Classification and Clustering

In part 1, I compared a few model evaluation techniques that fall under the umbrella of 'general statistical tools and tests'. Here in Part 2 I compare three of the more popular model evaluation techniques for classification and clustering: confusion…

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Added by Stephanie Glen on July 21, 2019 at 9:47am — No Comments

Comparing Model Evaluation Techniques Part 1: Statistical Tools & Tests

Evaluating a model is just as important as creating the model in the first place. Even if you use the most statistically sound tools to create your model, the end result may not be what you expected. Which metric you use to test your model depends on the type of data you’re working with and your comfort level with statistics.

Model evaluation techniques answer three main questions:

  1. How well does your model match your data (in other words, what is the…
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Added by Stephanie Glen on July 10, 2019 at 5:30am — 1 Comment

Model evaluation techniques in one picture

The sheer number of model evaluation techniques available to asses how good your model is can be completely overwhelming.  As well as the oft-used confidence intervals, confusion matrix and…

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Added by Stephanie Glen on June 29, 2019 at 7:38am — No Comments

Comparing Classifiers: Decision Trees, K-NN & Naive Bayes

A myriad of options exist for classification. In general, there isn't a single "best" option for every situation. That said, three popular classification methods— Decision Trees, k-NN & Naive Bayes—can be tweaked for practically every situation.

Overview

Naive Bayes and K-NN, are both examples of supervised learning (where the…

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Added by Stephanie Glen on June 19, 2019 at 6:49am — No Comments

Assumptions of Linear Regression in One Picture

If any of the main assumptions of linear regression are violated, any results or forecasts that you glean from your data will be extremely biased, inefficient or misleading. Navigating all of the different assumptions and recommendations to identify the assumption can be overwhelming (for example, normality has more than half a dozen options for testing).
This image highlights the assumptions and the most common testing options.…
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Added by Stephanie Glen on June 15, 2019 at 7:53am — No Comments

Alternatives to R-squared (with pluses and minuses)

R-squared can help you answer the question "How does my model perform, compared to a naive model?". However, r2 is far from a perfect tool. Probably the main issue is that every data set contains a certain amount of unexplainable data. R-squared can't tell the difference between the explainable and the…

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Added by Stephanie Glen on June 10, 2019 at 5:30am — No Comments

R-Squared in One Picture

R-squared measures how well your data fits a regression line. More specifically, it's how much variation in the…

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Added by Stephanie Glen on May 31, 2019 at 8:00am — No Comments

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