Introduction to Regression Questions
Linear regression is used for a wide range of predictive modeling tasks and to understand relationships between different variables or trends in the data. For example, an ed-tech company may hire a data scientist to measure the impact of education level on income, which can be utilized in marketing and partnership efforts.
In this module, we'll review linear regression concepts and what to expect in interviews.
What to expect
Questions related to regression are usually given in the following formats:
Conceptual questions
- What are the assumptions of linear regression?
- How does multicollinearity affect the interpretation of regression coefficients?
- Given an output from a regression model, which variables are significant? What is the relationship between the response variable and the independent variable weight? What is the difference between Multiple R-squared and Adjusted R-squared?
Applied questions
- Imagine you're working for a tech company that wants to analyze the relationship between website performance metrics (e.g. page load time, bounce rate, and conversion rate) and user engagement. How would you analyze this and identify opportunities for optimizing website performance?
How to prepare
To brush up on regression questions:
- Review key regression concepts, assumptions, and interpretation practices.
- Use a statistical software package like Python (NumPy, Pandas, SciPy) or R and publicly available datasets from websites like Kaggle to analyze data, check assumptions, and create regression models.
- Visualize data and regression results using libraries like Matplotlib or Seaborn in Python, or ggplot2 in R.
- Create scatter plots, line plots, histograms, and other visualizations to explore relationships between variables and assess model assumptions.
- Solve problems that apply regression concepts to real-world scenarios, such as predicting housing prices, customer churn, and stock prices.