# Statistics¶

Welcome to the Statistics module!

In this introductory course the students learn how to organize, analyze and present data using descriptive and inferential statistics methods. Statistical methods are applied in different components of natural resources management systems like assessment, objective setting, planning and monitoring.

There are many reasons to use computer software to learn statistics: cumulative distributions and lower and upper values of distributions can be confortably retreived for a large number of distributions provided, data visualization is strong supported, and large data can be processed using statistics software. Putting in simple terms one can say that there are two types of software: a) with a graphich user interface (GUI), which provides a cataloque of functions with input windows and result graphics and tables

We will learn how to use them, but more than that, we will retrieve table values from computer software

Competences

After completion of this module the participants will be able to:

1. Use tables and graphics to analyze and visualize frequency distributions

2. Understand key concepts of probability as well as descriptive and inferential statistics

3. Make generalizations from samples

4. Conduct hypothesis tests

5. Apply regression models

Contents:

1. Introduction

1.1. General concepts

1.1.1. Descriptive statistics, inference,

1.1.2. Questionnaires, observation, experiment

1.1.3. Qualitative and quantitative data

1.1.4. Nominal, ordinal, metric scales

1.1.5. Discrete and continuous data

1.1.6. Univariate, bivariate, multivariate, time series

1.1.7. Regression and correlation

1.2. Frequency

1.2.1. Absolute frequency

1.2.2. Cumulative absolute frequency

1.2.3. Relative frequency

1.2.4. Cumulative relative frequency

1.2.5. leaf diagramm, histogram, pareto diagram

1.3. Univariate Probability

1.3.1. Discrete probability distribution

1.3.2. Cumulative discrete probability distribution

1.3.3. Probability density function

1.3.4. Cumulative probability density function

1.4. R / Python

1.4.1. Basic syntax

1.4.2. System: packages, working directory, help, apropos, example, history, ls, rm, etc.

1.4.3. Vector

1.4.4. Plotting

1.4.5. Import / Export

1.4.6. Functions

1.4.7. Data structures

2. Random variables

2.1. Measures of central tendency

2.2. Measures of dispersion

2.3. Measures of symmetry

2.4. Skewness

2.5. Measures of peakedness

2.6. Kurtosis

2.7. Set operations

2.8. Probability

2.9. Properties: definition, sum of probabilities

2.10. Combinatorics

2.11. Conditional distribution

3. Discrete and continuous probability distributions

3.1. Discrete distributions

3.2. Continuous distributions

4. Confidence intervals and hypothesis testing

5. Regression and correlation

5.1. Linear regression

5.2. Multiple linear regression

5.3. Logistic linear regression

5.4. Statistical downscaling of global circulation model data

## Videos¶

I did some videos to support students learning statistics. The video quality can be considerably improved, but until this one day happens, you can use the ones provided here.