Lecture_1_Statistics_AI

Statistics for AI by Mag. Thomas Forstner

• Definition: Statistics is the science of collecting, organizing, presenting, and interpreting data.

• Key Functions:

  • Exploration & Visualization: Examines complex datasets.

  • Data Compression: Summarizes data for insights.

  • Modeling: Represents real-world problems.

  • Estimation & Prediction: Estimates unknown parameters.

  • Hypothesis Testing: Tests research questions.

  • Process: Explore ➔ Summarize ➔ Model ➔ Estimate ➔ Test.

• Importance: Solves personal issues, aids comprehension of scientific papers, and builds data competence.

• Categories:

  • Descriptive Statistics: Summarizes data.

  • Inductive Statistics: Infers population characteristics.

• Data Collection: Must be objective, valid, and reliable. Types: Primary (original) and Secondary (previously collected).

• Measurement Levels:

  • Nominal: Categories without order.

  • Ordinal: Categories with order.

  • Quantitative: Measured values, including Discrete and Continuous data.

• Frequency:

  • Absolute Frequency: Counts occurrences.

  • Relative Frequency: Proportion of a value in total.

  • Cumulative Frequency: Total occurrences at or below a given value.