What you'll learn
By the end of this course,
participants will be able to:
- Apply advanced statistical techniques such as ANCOVA,
factorial ANOVA, and repeated measures design.
- Conduct complex data analysis using Python's
statistical libraries.
- Interpret and report results from multivariate models.
- Implement AI tools to solve scientific research
problems.
Description
Course Overview: This course dives deeper into statistical modeling and
analysis using Python, focusing on advanced methods such as ANCOVA, factorial
ANOVA, repeated measures, and logistic regression. Participants will also
explore AI tools for scientific research to expand their data analysis
capabilities.
Target Audience: This course is ideal for those who have foundational
knowledge of statistics and Python programming and are looking to apply
advanced statistical techniques to real-world data.
Course Outline:
1.
Independent and Dependent Variables
- Understanding the roles of independent and dependent
variables in statistical models.
- Identifying these variables in different experimental
setups.
2.
Analysis of Covariance (ANCOVA) Model Test
- Introduction to ANCOVA and when to apply it.
- Conducting ANCOVA in Python to control for covariates
while testing main effects.
3.
Factorial ANOVA Model Test
- Explanation of factorial ANOVA for examining
interactions between factors.
- Implementing two-way and three-way ANOVA models using
Python.
4.
Repeated Measures Design – One-Way Repeated-Measures ANOVA Test
- Introduction to repeated measures design.
- Running one-way repeated-measures ANOVA in Python.
- Understanding within-subject factors and interpreting
results.
5.
Mixed Design ANOVA Model Test
- Combining between-subject and within-subject factors.
- Implementing mixed-design ANOVA using Python.
6.
Log Linear Analysis for Several Categorical Variables
- Introduction to log-linear analysis for categorical
data.
- Building models for several categorical variables in
Python.
7.
Multivariate Analysis of Variance (MANOVA) Model Test
- Overview of MANOVA for testing the influence of
independent variables on multiple dependent variables.
- Conducting MANOVA using Python and interpreting
multivariate results.
8.
Repeated Measures Design – Two-Way Repeated-Measures ANOVA Test
- Expanding on one-way repeated measures to include
two-way repeated measures ANOVA.
- Implementing this technique in Python and analyzing
complex repeated measures designs.
9.
Logistic Regression (Binary and Multinomial)
- Understanding binary and multinomial logistic
regression.
- Applying logistic regression models in Python to
predict categorical outcomes.
- Interpretation of odds ratios and model diagnostics.
10.
AI Tools for Scientific Research
- Overview of AI and machine learning tools for enhancing
scientific research.
- Using Python-based AI tools for predictive modeling,
pattern recognition, and data classification.
