Search results
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From
28 paź 2024 · 7. Udacity (Free Courses) While Udacity is known for its paid Nanodegree programs, the platform also offers several free courses in statistics and related fields. The free courses are self-paced and provide a solid introduction to various statistical concepts, including descriptive and inferential statistics.
Learning Statistics with Python Part I. Background. 1. Why do we learn statistics? 2. A brief introduction to research design; Part II. An Introduction to Python. 3. Getting Started with Python; 4. More Python Concepts; Part III. Working With Data. 5. Descriptive statistics; 6. Drawing Graphs; 7. Data Wrangling; 8. Basic Programming; Part IV ...
ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code.
12 kwi 2024 · Key statistical concepts for your data science or data analysis journey with Python Code. In this handbook, I will cover the following Statistics topics for data science, machine learning, and artificial intelligence (including GenAI): Random variables. Mean, Variance, Standard Deviation. Covariance and Correlation.
17 sie 2023 · Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation.
Chapter 1: Why do we learn statistics? Psychology and statistics. Statistics in everyday life. Some examples where intuition is misleading, and statistics is critical. Chapter 2: A brief introduction to research design. Basics of psychological measurement. Reliability and validity of a measurement. Experimental and non-experimental design.