Nina Zumel takes us through Cohen’s D, a useful tool for determining effect sizes in experiments:

Cohen’s dis a measure of effect size for the difference of two means that takes the variance of the population into account. It’s defined as

d = | μ_{1}– μ_{2}| / σ_{pooled}

where σ_{pooled}is the pooled standard deviation over both cohorts.

Read the whole thing.

Megan Quinn shows how you can use Pandas and linear regression to predict sales figures:

Pandas is an open-source Python package that provides users with high-performing and flexible data structures. These structures are designed to make analyzing relational or labeled data both easy and intuitive. Pandas is one of the most popular and quintessential tools leveraged by data scientists when developing a machine learning model. The most crucial step in the machine learning process is not simply fitting a model to a given data set. Most of the model development process takes place in the pre-processing and data exploration phase. An accurate model requires good predictors and, in order to acquire them, the user must understand the raw data. Through Pandas’ numerous data wrangling and analysis tools, this important step can easily be achieved. The goal of this blog is to highlight some of the central and most commonly used tools in Pandas while illustrating their significance in model development. The data set used for this demo consists of a supermarket chain’s sales across multiple stores in a variety of cities. The sales data is broken down by items within the stores. The goal is to predict a certain item’s sale.

Click through for an example of the process, including data cleansing and feature extraction, data analysis, and modeling.

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).

Violating one of the assumptions isn’t the end of the world, though it can make understanding the model and generating accurate predictions harder.

Kristian Larsen has a couple of posts on Monte Carlo style simulation in Python. First up is a post which covers how to generate data from different distributions:

One method that is very useful for data scientist/data analysts in order to validate methods or data is Monte Carlo simulation. In this article, you learn how to do a Monte Carlo simulation in Python. Furthermore, you learn how to make different Statistical probability distributions in Python.

You can also bootstrap your data, reusing data points when building a set of samples:

A useful method for data scientists/data analysts in order to validate methods or data is Bootstrap with Monte Carlo simulation In this article, you learn how to do a Bootstrap with Monte Carlo simulation in Python.

Both posts are worth the read.

Vincent Granville explains why using p-values for model-worthiness can lead you to a bad outcome:

Recently,

p-values have been criticized and even banned by some journals, because they are used by researchers, who cherry-pick observations and repeat experiments until they obtain ap-value worth publishing to obtain grant money, get tenure, or for political reasons. Even the American Statistical Association wrote a long article about why to avoidp-values, and what you should do instead: see here. For data scientists, obvious alternatives include re-sampling techniques: see here and here. One advantage is that they are model-independent, data-driven, and easy to understand.Here we explain how the manipulation and treachery works, using a simple simulated data set consisting of purely random, non-correlated observations. Using

p-values, you can tell anything you want about the data, even the fact that the features are highly correlated, when they are not. The data set consists of 16 variables and 30 observations, generated using the RAND function in Excel. You can download the spreadsheet here.

And for a more academic treatment of the problem, I love this paper by Andrew Gelman and Eric Loken, particularly because it points out that you don’t have to have malicious intent to end up doing the wrong thing.

Bruno Rodrigues shows one technique for forecasting intermittent data:

Now, it is clear that this will be tricky to forecast. There is no discernible pattern, no trend, no seasonality… nothing that would make it “easy” for a model to learn how to forecast such data.

This is typical intermittent demand data. Specific methods have been developed to forecast such data, the most well-known being Croston, as detailed in this paper. A function to estimate such models is available in the

`{tsintermittent}`

package, written by Nikolaos Kourentzes who also wrote another package,`{nnfor}`

, which uses Neural Networks to forecast time series data. I am going to use both to try to forecast the intermittent demand for the`{RDieHarder}`

package for the year 2019.

Read the whole thing. H/T R-Bloggers

Emanuele Meazzo builds a linear regression in Power BI using a Python visual:

As a prerequisite, of course, you’ll need to have python installed in your machine, I recommend having an external IDE like Visual Studio Code to write your Python code as the PowerBI window offers zero assistance to coding.

You can follow this article in order to configure Python Correctly for PowerBI.

Step 2 is to add a Python Visual to the page, and let the magic happen.

Click through for the step-by-step instructions, including quite a bit of Python code and a few warnings and limitations.

Kristian Larsen shows off some linear algebra concepts in R:

In this article, you learn how to do linear algebra in R. In particular, I will discuss how to create a matrix in R, Element-wise operations in R, Basic Matrix Operations in R, How to Combine Matrices in R, Creating Means and Sums in R and Advanced Matrix Operations in R.

The post is so chock-full of examples, the only block of multi-line text is the description.

Vincent Granville has an easy trick for removing serial correlation from a data set:

Here is a simple trick that can solve a lot of problems.

You can not trust a linear or logistic regression performed on data if the error term (residuals) are auto-correlated. There are different approaches to de-correlate the observations, but they usually involve introducing a new matrix to take care of the resulting bias. See for instance here.

Click through for the alternative.

Stephanie Glen shows us cross-validation in one picture:

Cross Validation explained in one simple picture. The method shown here is

k-fold cross validation, where data is split into k folds (in this example, 5 folds). Blue balls represent training data; 1/k (i.e. 1/5) balls are held back for model testing.Monte Carlo cross validation works the same way, except that the balls would be chosen with replacement. In other words, it would be possible for a ball to appear in more than one sample.

You’ll have to click through for the picture.

Kevin Feasel

2019-06-21

Data Science, R

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