Interview Q&A / Data Science

1. What do the slope and intercept mean in a simple linear regression?

   The slope is the average change in y for a one-unit increase in x, and the intercept is the predicted y when x is zero. Least squares picks the line that minimises the sum of squared vertical errors.

   BeginnerQuick answer1 min read
2. What does R-squared tell you, and what does it not tell you?

   R-squared is the share of the variation in the target that the model explains, from 0 to 1. It says nothing about whether the model is correct, unbiased or good at predicting new data, and it never decreases when you add features.

   BeginnerQuick answer1 min read
3. What are the assumptions of linear regression?

   The main ones are a linear relationship, independent errors, constant error variance (homoscedasticity), roughly normal errors for inference, and no severe multicollinearity. Violations mainly hurt interpretation and confidence intervals rather than raw prediction.

   IntermediateQuick answer1 min read
4. What is a residual, and what can a residual plot tell you?

   A residual is actual minus predicted. Plotting residuals against fitted values should look like random scatter around zero; curves, fans or clusters reveal non-linearity, changing variance or missing variables.

   IntermediateQuick answer1 min read
5. What is multicollinearity, and how do you detect it?

   Multicollinearity is when predictors are strongly correlated with each other, which makes coefficient estimates unstable and hard to interpret. Detect it with a correlation matrix or variance inflation factor; VIF above about 5 to 10 is a warning.

   IntermediateQuick answer1 min read