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

By Andrew Ng 1. Logistic Regression      1.1   Visualizing Data Part             └ plotData function     1.2   Advanced  Optimization  Part             └ mapFeature function             └ costFunctionReg function             └ fminunc function         1.3   Decision Boundary and Prediction   Part             └ plotDecisionBoundary function             └ predict function 1. Logistic Regression We'll implement regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly. Suppose you're the product manager of factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model. 1.1 V