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.…

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

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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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 s*ignificance 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…*

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

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.…

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

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…

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

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.

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

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.…

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

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

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

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

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

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…

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

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.…

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

Decision Trees, Random Forests and Boosting are among the…

ContinueAdded by Stephanie Glen on July 28, 2019 at 7:30am — 1 Comment

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…

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

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…

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

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:**

- How well does your model match your data (in other words, what is the…

Added by Stephanie Glen on July 10, 2019 at 5:30am — 1 Comment

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…

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

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…

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

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

R-squared can help you answer the question "How does my model perform, compared to a naive model?". However, r^{2} 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…**

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

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

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

- P-Value Explained in One Picture
- Difference Between Correlation and Regression in One Picture
- Significance Level vs Confidence level vs Confidence Interval
- Correlation Coefficients in One Picture
- Difference Between Stratified Sampling, Cluster Sampling, and Quota Sampling
- Machine Learning vs Statistics vs Statistical Learning in One Picture
- One Way vs Two Way ANOVA + Factorial ANOVA: A Comparison in one Picture

- P-Value Explained in One Picture
- Machine Learning vs Statistics vs Statistical Learning in One Picture
- Decision Tree vs Random Forest vs Gradient Boosting Machines: Explained Simply
- Difference Between Stratified Sampling, Cluster Sampling, and Quota Sampling
- Math You Don't Need to Know for Machine Learning
- Regression Analysis in One Picture
- Can you be a Data Scientist without coding?

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