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  • Book Overview & Buying How to Prove Anything
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How to Prove Anything

How to Prove Anything

By : B. McGraw
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How to Prove Anything

How to Prove Anything

By: B. McGraw

Overview of this book

This book collects popular essays from the renowned Cranberry-Lemon University Press’s most illustrious scholars. Despite their questionable research methods, bizarre obsessions, personal vendettas, and often stunning lack of impartiality, the authors have irrefutably broken new ground academically. How to Prove Anything sheds new light on a wide range of topics. Famed academics Dr. Tiffany Love and Dr. Chad Broman present a glimpse into their shared life via a series of papers that unravel the mysteries of modern romance. From time series analysis of mood swings to Pavlovian behavioral modification to sub-Nyquist sampling for balancing relationship attention with videogame performance, Chad and Tiffany find new and often startling uses for tried-and-true algorithms, gaining insights from which we can all benefit. Can I avoid arguments by predicting moods with time series analysis? Can linear programming help us determine who should do the dishes? And, most pressingly of all… Can I fix him? Academic writing has brought new knowledge into the world for hundreds of years. This book may be the most vital contribution of all. While some of the applications in this book may be niche even obscure reading will provide intellectual stimulation, spiritual enlightenment or, at the very least, some entertainment.
Table of Contents (33 chapters)
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4
Me and My Friend Show That My Dad’s Airstream Trailer Goes Faster with Dints, and That We Shouldn’t Be Grounded for Hitting It with a Baseball Last Saturday
31
Another Book You May Enjoy
32
Index

3. Methodologies

Due to the meticulously cleaned data, black box time analysis tools were easily applied and evaluated against Tiffany’s historical data. With more than two years of data, these forecasting models could be cross-validated for a historical first in our ten-year relationship, far beating the overfitted multivariate approach, which caused the end of my relationship with my high school sweetheart a year into college [5]. Tiffany’s data was modeled in this paper using a seven-day moving average, sextuple exponential smoothing, Autoregressive Moving Average (ARMA), and one overly complicated machine learning black box.

3.1 Moving Average

The most simple model applied to Tiffany’s mood swing data was a seven-day moving average. While this extremely rudimentary approach may not have been the best for implementing higher dimensionality predictors, it created less-noisy forecasts compared to the more complex alternatives.

While her data appeared...

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