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.…Continue
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…Continue
Added by Stephanie Glen on October 10, 2019 at 12:01pm — No Comments
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…Continue
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.…Continue
Added 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…Continue
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.…Continue
Added 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
Added 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…Continue
Added by Stephanie Glen on July 31, 2019 at 4:00am — No Comments
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…Continue
Added 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…Continue
Added 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:
Added 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.
Naive Bayes and K-NN, are both examples of supervised learning (where the…Continue
Added by Stephanie Glen on June 19, 2019 at 6:49am — No Comments
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, 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…Continue
Added by Stephanie Glen on June 10, 2019 at 5:30am — No Comments
Added by Stephanie Glen on May 31, 2019 at 8:00am — No Comments