Lines of best fit and residuals
When a scatter plot shows a roughly linear pattern, a line of best fit summarizes it and lets you make predictions. Technology usually finds the least-squares regression line, and its slope and intercept should be interpreted in context: in a line predicting test score from hours studied, a slope of 5 means each extra hour is associated with about 5 more points. A residual measures how far a real data point is from the line: residual = actual value - predicted value. If the line predicts 72 points for 3 hours of study and the student actually scored 75, the residual is +3, meaning the point lies above the line. A residual plot shows all the residuals; if they scatter randomly around zero, a linear model fits well, but a clear curved pattern suggests a different model would fit better. Predictions far outside the range of the data (extrapolation) are much less reliable.
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This midterm covers a lot of topics, so it's split into 2 parts. Each part includes a question from every one of its topics.
Midterm, Part 1 of 2
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Midterm, Part 2 of 2
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This final covers a lot of topics, so it's split into 4 parts. Each part includes a question from every one of its topics.
Final, Part 1 of 4
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Final, Part 2 of 4
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Final, Part 3 of 4
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Final, Part 4 of 4
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