# regression

## Survival analysis 2: parametric survival models

Previous topics Why do we need parametric survival models Is Time a Variable or a Constant? Steady change in hazard and survival Positive exponential change in hazard and survival Negative exponential change in hazard and survival B(u)ilding Exponential model … finally 🥳 How to compute parametric models Final thoughts Further readings and references Previous topics A good understanding of Kaplan-Meier method (KM) is a prerequisite for this post, but since you are here, I suppose you are already familiar with it 😉

## Survival analysis 1: a gentle introduction into Kaplan-Meier Curves

Previous topics Why do we need survival analysis? Death is not the only option! Or: “What is an event?” Censoring How to calculate Kaplan-Meier survival curve “manually” step by step Survival probability How to compute Kaplan-Meier survival curve Interpretation of Kaplan-Meier Curve Comparing survival of groups 2 groups Interpretation of groups comparison using 4 benchmarks Log-Rank test > 2 groups and multiple pairwise (post-hoc) Log-Rank test Multiple survival curves Conclusions What’s next?

## 4. Roc(k) is confusing: or the link between machine learning and epidemiology

Previous topics 1. Introduction to statistics 2. Linear regression 3. Logistic regression Why do we need ROC curves and confusion matrices? For assessing the predictive accuracy of the classifier (i.e. logistic regression) or for assessing the accuracy of medical tests and calculating lot’s of medical metrics (i.e. prevalence). Since confusion matrix works for both, classification and medicine, there is a nice link between machine learning and epidemiology. This is the last lecture in my coarse “Statistics for non statisticians”.

## 2. Linear regression vs. Statistical Tests ⚔ who wins?

Previous topics Introduction to statistics Why do we need linear regression for predictions for studying how things influence other things Regression is a line which tries to be as close as possible to all data points simultaneously and in this way describes your data using only two numbers, intercept and slope. If there is a relationship between two variables, then you can predict one of those variables by knowing only the value of the other.

## 1. Introduction to statistics: The (small) Big Picture or how to solve 95% of statistical problems

Why do we need statistics? to learn about the world to do science to develop artificial intelligence The bad news is - statistics is un-intuitive, boring and hard to understand, otherwise, you’d already know everything. But the good new is - you don’t need to understand it. You just need to know how to use statistics to get the most out of your data. Think about driving a car for a moment.

## Logistic regression 5: multiple logistic regression with interactions

Previous topics Why do we need interactions Two categorical predictors Visual interpretation Post-hoc analysis Model output interpretation One numeric and one categorical predictors Model interpretation Post-hoc Two numeric predictors Multiple logistic regression with higher order interactions Welcome to a new world of machine learning! Choosing a model What’s next Final thoughts Further readings and references Source Previous topics A good understanding of four topics is a prerequisite for this post:

## Logistic regression 4: multiple logistic regression

Previous topics Why do we need multiple logistic regression Two categorical predictors One categorical and one numeric predictors Multiple logistic regression with 3 variables Conclusion When NOT to use a multiple logistic regression What’s next Further readings and references Previous topics A good understanding of three topics is a prerequisite for this post: odds, log-odds and probabilites how logistic regression works simple logistic regression Why do we need multiple logistic regression If one predictor influences another predictor (they are correlated), producing two simple regressions (with each of the predictors) might give completely different results as compared to the model containing both predictors.

## Logistic regression 3: simple logistic regression

Previous topics Why do we need logistic regression Before modelling: get probabilities from counts How to conduct simple logistic regression in R Intercept only model log-odds are cool 😎, while odds are very odd Percentage change Standard error, z-value and p-value Model with one nominative predictor with only two categories The concept of odds-ratio Confidence intervals for odds-ratios Summary and interpretation Visualizing model results Model with one nominative predictor with over two categories Model with one numeric predictor Conclusion What’s next Further readings, videos and references Previous topics A good understanding of two topics is a prerequisite for this post:

## Logistic regression 2: how logistic regression works

Previous topics Why do we need logistic regression How logistic regression works Linear regression How to compute logistic regression in R Conclusion Assumptions What’s next Further readings and references Previous topics A good understanding of odds and probabilites is a prerequisite for this post. Two further topics simple linear regression and constraints of simple linear regression are not nessassery but would certainly help. Why do we need logistic regression Logistic regression predicts the probability of success.

## Logistic regression 1: from odds to probability

Previous topics Why do we need this post? Intro Odds Visualizing odds to understand their “oddness” Logarithm of odds Probabilities Odds-ratios Connect the dots! Fisher’s Exact test calculates odds-ratio Logistic regression What’s next Further readings and references Source This post was inspired by two short Josh Starmer’s StatQuest videos as the most intuitive and simple visual explanation on odds and log-odds, odds-ratios and log-odds-ratios and their connection to probability (you can watch them below).