Springe direkt zu Inhalt
Freie Universität Berlin
Department of Earth Sciences
Service Navigation
Homepage
SOGA Startpage
SOGA-R
Privacy Policy
Accessibility Statement
Search terms
Information about data transfer when using Google Search™
Department of Earth Sciences
/
Statistics and Geodata Analysis using Python (SOGA-Py)
Menu
Introduction to Python
Overview Introduction to Python
Getting Started
Mamba installation
Mamba use
Introduction to Integrated Development Environments (IDE)
Introduction to Jupyter Lab
Using Python as a Calculator
Build-in-Function in Python
Objects and their use in Python
Datatypes
Atomic Datatypes in Python
Datastructures in Python
User-defined-functions (UDF) in Python
Controlling the control flow in Python
Basics of Statistics
Overview Basics of Statistics
Descriptive Statistics
Measures of Central Tendency
The Mean
The Median
The Mode
Measures of Dispersion
Variance and Standard Deviation
The Range
Measures of Position
Quartiles and Interquartile Range
The Five Number Summary
Percentiles and Percentile Rank
Outliers and Boxplots
Measures of Relation
Covariance
Correlation
The Contingency Coeficient
Discrete Random Variables
Discrete Random Variables - An Example
The Mean and Standard Deviation
The Binomial Distribution
Requirements
The Binomial Distribution
Mean and Standard Deviation
Binomial and Hypergeometric Distribution
The Poisson Distribution
The Poisson Distribution - An Example
Shape, Mean and Standard Deviation
Poisson Approximation to the Binomial Distribution
Continous Random Variables
Probability Density Functions
The Normal Distribution
The Standard Normal Distribution
Determining the z-Value
Standardizing a Normally Distributed Variable
The Standard Normal Distribution: An Example in R
The Continuous Uniform Distribution
The Continuous Uniform Distribution in Python
The Students t-Distribution
The Students t-Distribution in Python
The Chi-Square Distribution
The Chi-Square Distribution in Python
The F-Distribution
The F-Distribution in Python
The Central Limit Theorem
Introductory video
The Population Distribution
Population and Sample Statistics
The Sampling Error
The Sampling Distribution
The Standard Error
Normally Distributed Population
Not Normally Distributed Population
Inferential Statistics
The Point Estimate
The Interval Estimate
Precision and Accuracy
Population Mean - The z-Distribution
The One-Mean z-Interval Procedure
Population Mean - The t-Distribution
The One-Mean t-Interval Procedure
Hypothesis Tests
Introduction to Hypothesis Testing
Hypothesis Formulation
Error and Significance Level
Critical Value and the p-Value
One Population Mean
Hypothesis Tests when Sigma Is Known
Hypothesis Tests when Sigma is Unknown
Two Population Means
Standard Deviations Assumed Equal
Standard Deviations Not Assumed Equal
Paired Samples
Hypothesis Tests for Two Population Means - Exercises
Population Standard Deviations
One Population Standard Deviation
Two Population Standard Deviations
Chi-Square Tests
Chi-Square Goodness-of-Fit Test
The Chi-Square Independence Test
Regression and Correlation
Inferences About the Slope
Inferences About the Correlation
Inferences in Regression and Correlation - Exercises
Probability Tables
The z-Distribution
The t-Distribution
The Chi Square Distribution
The F-Distribution
Linear Regression
Simple Linear Regression
Parameter Estimation
Simple Linear Regression - An example
Model Diagnostic
Polynomial Regression
Polynomial Regression - An example
Logistic Regression
The Logit Function
The Logistic Regression Model
Logistic Regression in Python - An Example
Advanced statistics
Overview Advanced statistics
Feature scales
Reasons for transformations
Linear Transformations
Transformations for double contraint intervals
Zero Missings
Intro to compositional data
Multivariate approaches
Multiple linear regression
Parameter estimation
Multiple linear regression analysis - a simple example
Multiple linear regression analysis - an advanced example
Regularization methods
Principal Component Analysis
Principal Component Analysis - the basics
Principal Component Analysis in Python
Principal Component Analysis - An example
Application of Principal Component Analysis for regression modelling
Factor Analysis
The Exploratory Factor Model
A simple example of Factor Analysis in Python
Time Series Analysis
Basic properties of time series
Date, time and time series in Python
Data sets used
Weather station Berlin-Dahlem
Modern carbon dioxide measurement
Earth surface temperature anomalies
Ice core atmospheric CO2 record
Basic operations on time series using Python
Aggregation of time series data
Dealing with missing values
Imputing missing values
Smoothing
Smoothing by filtering
Kernel smoothing
Lowess
Smoothing Splines
Seasonal decompositon
STL decomposition
Trends and seasonal effects
Working data set
Linear trend estimation
Eliminating the seasonal effect
Time series statistical models
White noise models
Random walk models
Moving Average models
Autoregressive models
ARIMA models
ARIMA modelling in Python
Spatial Point Patterns
Spatial point process
Berlin City Data
Analysis of spatial point patterns
Centrography
Intensity
Interactions
Simulation envelopes
Spatial Interpolation
Data sets used
Lake Rangsdorf
DWD Weather Data Germany
DWD Weather Data East Germany
SRTM DEM
Nearest Neighbor Interpolation
Inverse Distance Weighting
IDW Interpolation
Geostatistical Interpolation
Estimation of the Mean Function
Estimation of the Semivariogram
Modeling the Semivariogram
Kriging
Geostatistical Interpolation with Python
Mean Annual Rainfall Germany
Lake Rangsdorf
Raster Data Analysis
Data Sets
Landsat Satellite Data
Reading Raster Data
Raster Processing I
Raster Processing II
Machine learning
Overview Machine learning
Introduction to machine learning with Python
Artifical Neural Networks (ANN)
Widrow Hoff learning rule
Multilayer Perceptron Algorithm
Clustering Example
Classification example
A typical machine learning workflow
Path Navigation
Homepage
SOGA-Py
Machine learning
Artifical Neural Networks (ANN)
Multilayer Perceptron Algorithm
5 / 8
Multilayer Perceptron Algorithm
Your browser cannot display iframes. You can find the content at the following URL
https://userpage.fu-berlin.de/soga/soga-py/400/40200_algorithms/40213_Multilayer_Perceptron_Algorithm.html
Please enable JavaScript in Your browser.
SOGA
SOGA-R