Regression analysis is a statistical process for estimating the relationship between two or more variables. The relationship is modeled as $y \sim x$ or $y = f(x)$. Both model descriptions indicate, that the variable $y$ is a function of $x$. Therefore, the variable $y$ is denoted as the response variable or dependent variable, whereas the variable $x$ is denoted as the predictor variable or independent variable.

---

The following functions and R-packages are used in this section (in alphabetical order):

• caTools

• c()
• caTools::sample.split()
• cbind()
• ceiling()
• coef()
• confint.default()
• cooks.distance()
• cov()
• data()
• data.frame()
• dim()
• dfbetas()
• dffits()
• fitted()
• floor()
• hatvalues()
• list()
• lm()
• max()
• min()
• mean()
• nrow()
• paste0()
• poly()
• predict()
• rbind()
• read.csv()
• residuals()
• rnorm()
• round()
• runif()
• sample()
• seq()
• set.seed()
• sqrt()
• str()
• sum()
• summary()
• var()
• which()
• which.min()

• abline()
• legend()
• lines()
• matplot()
• par()
• plot()
• points()
• title()

---

Citation

The E-Learning project SOGA-R was developed at the Department of Earth Sciences by Kai Hartmann, Joachim Krois and Annette Rudolph. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Hartmann, K., Krois, J., Rudolph, A. (2023): Statistics and Geodata Analysis using R (SOGA-R). Department of Earth Sciences, Freie Universitaet Berlin.