Rubbing & Scrubbing My Data

by Kristin Sainani CONTENTS 1. Descriptive statistics and looking at data 2. Review of study designs; Measures of disease risk and association 3. Probability, Bayes' Rule, Diagnostic Testing 4. Probability distributions 5. Statistical Inference 6. P-values (errors, statistical power, and pitfalls) 7. Statistical Tests 8. Regression Analysis 9. Logistic Regression, Cox Regression 1. Descriptive statistics and looking at data 1.1 Types of Data 1.1.1 Quantitative Variable It is a numerical data(e.g., Age, Blood pressure, BMI, Pulse) that you can add, subtract, multiply, and divide. ㆍ Continuous (quantitative) variable: can theoretically take on any value within a given range (e.g., height=68.99955... inches) ㆍ Discrete (quantitative) variable: can only take on certain values (e.g., count data) However, In the real world, sometimes the distinction between continuous and discrete actually doesn't make much difference. For example, when we analyze a family size from discrete value(e...

I completed this course and earned the certificate at November 17, 2015. This machine learning course is considered as the famous fundamental machine learning course for beginners and is provided by Stanford University, Andrew Ng. I summarized the programming assignments (please don't look if you're on-going) at my github and some lecture notes at here (below). Session 1. Week 1 - Introduction, Linear Regression with One Variable, Linear Algebra Review Week 2 - Linear Regression with Multiple Variables, Octave/Matlab Tutorial Week 3 - Programming 1: Linear Regression (Predicting house prices) [ Github ][ Report ] Session 2. Introduction Need to know how to get the algorithms and math to work in problems. Best way to do building intelligent machines is to have some way for machines to lean things themselves. Machine Learning Definition Tom Mitchell(1998) "A computer program is said to learn from experience E with respect to some task T and some performance measure P , if...