Top stories published by Human Systems Data in March of 2017

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Top Stories published by Human Systems Data in March of 2017

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2017

March

Jacob Willinger in Human Systems Data

Feb 28, 2017

RSE vs R²

The assigned chapter was heavy and fast on the statistical notation as it guided us through linear regression. I can’t say that I found it easy to digest, but I do appreciate how it began with a framing of the topic through the seven questions (Is there a relationship between the two? How strong is it…

1 response

Hansol Rheem in Human Systems Data

Mar 27, 2017

Limitations of the Multiple Regression Model

Can we see the forest for the trees? When examining a phenomenon with multiple causes, will it help us understand the phenomenon if we look at the causes one at a time? Or, is it better when we see the overall pattern created by the multiple causes…

2 responses

Jacob Willinger in Human Systems Data

Mar 29, 2017

Regression showdown: R vs SPSS

Straying from a conventional reading this week, our instructions were to work through an exercise on how to run a multiple regression in R, found here: https://www.tutorialspoint.com/r/r_multiple_regression.htm.

2 responses

Eric del Rio in Human Systems Data

Mar 28, 2017

Attempt at Multiple Regression in R

Hey Folks,

In my last post I talked about Polarity in Data Visualization and the challenges of integrating the strengths of Infovis and statistical graphics into an intriguing and accurate visual. The present post is a first-hand account of…

2 responses

Vipin Verma in Human Systems Data

Mar 28, 2017

Analysis of Means or Variance?

Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means. It is rather odd that the technique is called “Analysis of Variance” rather than “Analysis of Means”. ANOVA tests the null hypotheses that all the sample means are equal…

2 responses

Vipin Verma in Human Systems Data

Feb 28, 2017

Linear Regression — Learning the ‘R’ way!

This chapter focuses on single and multiple linear regression approaches to supervised learning for quantitative data. It helps discover the relationship between various variables, the extent of the relationship, the accuracy within and the predictability…

1 response

Maya Toteva in Human Systems Data

Feb 28, 2017

Linear Regression and Confidence Intervals in layman’s Terms

Linear regression is one of the most popular techniques used in statistics. It assesses how successfully a predictive variable does its job in predicting the outcome variable. It operates under the assumption, that the two…

Verica Buchanan in Human Systems Data

Mar 28, 2017

Multiple Regression Model for Movie Box Office Revenue

This multiple regression analysis models a movie’s first year box office revenue in millions of dollars. The input variables are the following: total production costs/millions, total promotional costs/millions, total book…

1 response

Vipin Verma in Human Systems Data

Mar 26, 2017

Multiple Regression — The ‘R’ Way!

The purpose of multiple regression is to learn about the relationships between several predictors and an outcome variable. In this context, the computational problem that needs a solution is to fit a model(regression equation) to the training data that we have. Let us…

2 responses

Lisa Lacouette in Human Systems Data

Mar 28, 2017

Ice Cream Consumption & Murder Rates

I will be honest and admit I haven’t given multiple regression as much thought as I should have. I am currently growing my statistical knowledge. As a dutiful student, I accepted the idea of linear regression as being the exploration of the relationship between an…

These were the top 10 stories published by Human Systems Data in March of 2017. You can also dive into daily archives for March of 2017 by using the calendar at the top of this page.

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Human Systems Data

Human Systems Engineering 598 is a graduate level methods course on data analytics with human subjects data at Arizona State University. The instructor is Dr. Erin Chiou, Assistant Professor of Human Systems Engineering.

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