# Welcome!

### My name is Aaron Schlegel and I love data, automation and statistics.

My name is Aaron Schlegel and I love solving problems through data, learning as much as possible, and creating tools and processes that help people do their work better. I’m gold in some trades, silver in others, including data analysis, web development and programming. I eat Excel, Python, VBA, HTML/CSS, and Adobe products as part of a balanced breakfast with R on the side. I seek the unbiased truth buried in data sets and think psychohistory is one of the coolest ideas in fiction ever created.

### Self-education is, I firmly believe, the only kind of education there is.

- Isaac Asimov

##### Tableau

3 Years Experience

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### Writing, to me, is simply thinking through my fingertips. - Isaac Asimov

The more common approach to QR decomposition is employing Householder reflections rather than utilizing Gram-Schmidt. In practice, the Gram-Schmidt procedure is not recommended as it can lead to cancellation that causes inaccuracy of the computation of , which may result in a non-orthogonal matrix. Householder reflections are another method of......

QR decomposition is another technique for decomposing a matrix into a form that is easier to work with in further applications. The QR decomposition technique decomposes a square or rectangular matrix, which we will denote as , into two components, , and . Where is an orthogonal matrix, and is......

Hierarchical clustering is a widely used and popular tool in statistics and data mining for grouping data into ‘clusters’ that exposes similarities or dissimilarities in the data. There are many approaches to hierarchical clustering as it is not possible to investigate all clustering possibilities. One set of approaches to hierarchical......

The iterated principal factor method is an extension of the principal factor method that seeks improved estimates of the communality. As seen in the previous post on the principal factor method, initial estimates of or are found to obtain from which the factors are computed. In the iterated principal factor......

As discussed in a previous post on the principal component method of factor analysis, the term in the estimated covariance matrix , , was excluded and we proceeded directly to factoring and . The principal factor method of factor analysis (also called the principal axis method) finds an initial estimate......

In the first post on factor analysis, we examined computing the estimated covariance matrix of the rootstock data and proceeded to find two factors that fit most of the variance of the data using the principal component method. However, the variables in the data are not on the same scale......

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