SR2 Chapter 2 Hard
Posted on 1 March, 2020 by Brian
Tags: statistical rethinking, solutions, conditional probability, counting, bayes rule, pandas
Category: statistical-rethinking-2
Here’s my solution to the hard exercises in chapter 2 of McElreath’s Statistical Rethinking, 1st edition. When writing this up, I came across a very relevant article. We’ll ... [Read more...]

Abstract
The Air Pressure System (APS) is a type of function used in heavy vehicles to assist braking and gear changing. The APS failure dataset consists of the daily operational sensor data from failed Scania trucks. The dataset is crucial to the man...

[Read more...]The Source code chapter of my evidence-based software engineering book has been reworked (draft pdf). When writing the first version of this chapter, I was not certain whether source code was a topic warranting a chapter to itself, in an evidence-based software engineering book. Now I am certain. Source code ... [Read more...]

Last week I wrote about the relationship between weight and height in US adults, as seen in the US Centers for Disease Control and prevention (CDC) Behavioral Risk Factor Surveillance System, an annual telephone survey of around 400,000 interviews per year. In particular, I tested the widely-circulated claim that Body Mass ... [Read more...]

matricks package in 0.8.2 version has been released on CRAN! In
this post I will present you, what are advantages of using matricks
and how you can use it.
Creating matrices
The main function the package started with is m. It’s a smart shortcut
fo...

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We’re taking a break from our series on portfolio construction for two reasons: life and the recent market sell-off. Life got in the way of focusing on the next couple of posts on rebalancing. And given the market sell-off we were too busy gamma hedging our convexity exposure, looking ...

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SR2 Chapter 2 Medium
Posted on 29 February, 2020 by Brian
Tags: statistical rethinking, solutions, conditional probability, counting, grid approximation
Category: statistical-rethinking-2
Here’s my solutions to the medium exercises in chapter 2 of McElreath’s Statistical Rethinking, 1st edition. My intention is to move over to the 1nd edition when it comes out ... [Read more...]

Fancy (re-)creating your website using Hugo, with or without blogdown?
Feeling a bit anxious?
This post is aimed at being the Hugo equivalent of “What to know before you adopt a pet”.
We shall go through things that can/will break in the future, and what you can do ... [Read more...]

To complete the analysis on the significance of the sector on the salary for different occupational groups in Sweden I will in this post examine the correlation between salary and sector using statistics for education.
The F-value from the Anova table is used as the single value to discriminate how ... [Read more...]

Introduction Historically, only governments and large corporations have had access to quality satellite images. In recent years, satellite image datasets have become available to anyone with a computer and an internet connection. The quality, quantity, and precision of these datasets is continuously improving, and there are many free and commercial ...

[Read more...] All you need to do with PCA is in Factoshiny! PCA – Principal Component Analysis – is a well known method for exploring and visualizing data. The function Factoshiny of the package Factoshiny allows you to perform PCA in a really easy way. You can include extras information such as categorical variables, ...

[Read more...] Join Robert Muenchen’s workshop about Machine Learning with R at Machine Learning Week on May 31 – June 4, 2020 in Las Vegas! Workshop Description The Workshop will take place in May 31, 2020. R offers a wide variety of machine learning (ML) functions, each of which works in a slightly different way. This one-day, … ...

[Read more...] Introduction To reason rigorously under uncertainty we need to invoke the language of probability (Zhang et al. 2020). Any model that falls short of providing quantification of the uncertainty attached to its outcome is likely to yield an incomplete and potentially misleading picture. While this is an irrevocable consensus in statistics, ...

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Neural networks have really taken over for solving image recognition and high sample rate data problems in the last couple of years. In all honesty, I promise I won’t be teaching you what neural networks are or CNN’s are. There are hundred’s of resources that are published ...

[Read more...] Neural networks have really taken over for solving image recognition and high sample rate data problems in the last couple of years. In all honesty, I promise I won’t be teaching you what neural networks are or CNN’s are. There are hundred’s of resources that are published ...

[Read more...]
Introduction
In my last post we looked at how we can recreate base equivalents of the dplyr functions select(), filter(), mutate() and arrange(), amongst others. I wrote these functions and presented them in a new package called poorman. In this post I will be discussing new functionality that I have ... [Read more...]

Functions and iteration
Introduction
Functions in R allow us to automate things, instead of copy-pasting code.
3 major advantages :
Functions can have names, so it makes code easier to read
If your requirements change, you only have one place ...

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Machine Learning at the Boundary:
There is nothing new in the fact that machine learning models can outperform traditional econometric models but I want to show as part of my research why and how some models make given predictions or in this instance classifications.
I wanted to show the decision ...

[Read more...] Version 0.4.0 of nnetsauce, with fruits and breast cancer classification

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Introduction
Null and alternative hypothesis
Hypothesis testing
Different versions of the Student’s t-test
How to compute Student’s t-test by hand?
Scenario 1: Independent samples with 2 known variances
Scenario 2: Independent samples with 2 equal but unknown variances
Scenario 3: Independent samples with 2 unequal and unknown variances
Scenario 4: Paired samples where the variance ...

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