regression analysis: simplify complex data relationships coursera weekly challenges 2 answers
Test your knowledge: Foundations of linear regression
1. Fill in the blank: The best fit line is the line that fits the data best by minimizing some _____.
- residual values
- predicted values
- loss function
- regression function
2. What is the sum of the squared differences between each observed value and the associated predicted value?
- Residual least squares
- Sum of squared residuals
- Sum of squared predicted values
- Ordinary least squares
3. What tool would be most effective for calculating the ordinary least squares?
- Python
- Google Sheets
- SQL
- Microsoft Excel
Test your knowledge: Assumptions and construction in Python
4. How does a data professional determine if a linearity assumption is met?
- They confirm whether data on the X-Y coordinate falls along a downward curved line.
- They confirm whether data on the X-Y coordinate resembles a random cloud.
- They confirm whether data on the X-Y coordinate falls along an upward curved line.
- They confirm whether data on the X-Y coordinate falls along a straight line.
5. Which of the following statements accurately describes the normality assumption?
- The normality assumption can only be confirmed before a model is built.
- The normality assumption can only be confirmed after a model is built.
- The normality assumption can only be confirmed while a model is being built.
- The normality assumption can be confirmed anytime during model building.
6. A data professional is using a scatterplot to plot residuals and predicted values from a regression model to check for homoscedasticity. What does this scenario represent?
- Cone
- Straight line
- Random cloud
- Curved line
7. What type of visualization uses a series of scatterplots that show the relationships between pairs of variables?
- Residual matrix
- Linear matrix
- Scatterplot matrix
- Scatterplot residuals
Test your knowledge: Evaluate a linear regression model
8. What is the area surrounding a regression line, which describes the uncertainty around the predicted outcome at every value of X?
- Confidence interval
- Confidence band
- R squared
- Ordinary least squares
Shuffle Q/A 1
9. Fill in the blank: R squared measures the _____ in the dependent variable, Y. This is explained by the independent variable, X.
- proportion of variation
- coefficient of variation
- proportion of accuracy
- coefficient of accuracy
10. Which linear regression evaluation metric is sensitive to large errors?
- Adjusted R squared
- Mean squared error (MSE)
- Mean absolute error (MAE)
- The coefficient of determination
