Disciplines/Subjects: Mathematics, Statistics, Data Science
Key Themes: Linear Regression Analysis, Data Collection, Hypothesis Testing, Real-World Applications
This project allows learners to choose a topic of personal interest and apply linear regression analysis to explore relationships between variables. Whether analyzing economic data, environmental factors, or social trends, learners will collect and clean data, build regression models, and evaluate their fit using statistical software like R or Python. They will also perform hypothesis testing, calculate confidence intervals for the regression coefficients, and interpret the results. The project culminates in a detailed report that applies these techniques to solve practical problems, improving both analytical and data modeling skills.
Habits of mind: Curiosity, Growth Mindset, Strive for Excellence
Transferable skills: Modeling, Interpreting Data/Information to Make Valid Claims, Identifying Patterns and Relationships
Content Knowledge:
Understanding the principles and assumptions behind linear regression models.
Proficiency in using statistical software (e.g., R, Python) for regression analysis.
Ability to perform hypothesis testing and interpret confidence intervals to assess the significance of variables.
Knowledge of model evaluation techniques, including residual analysis and multicollinearity detection.