Statistics practice

Linear Regression and Residuals worksheets

Fit ordinary least-squares lines with intercepts, predict responses, calculate observed-minus-predicted residuals, and identify extrapolation.

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Fit an ordinary least-squares line with an intercept from the displayed pairs. Keep full precision until the final answer; residual equals observed y minus predicted y.

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Keep the paired observations visible

Every plot and calculation comes from the labeled x and y table printed beside it. Construction tasks keep the student plot blank, while reading tasks provide the actual point cloud and worked keys complete each requested diagram.

Learning objective

What this activity builds

Compute and interpret an OLS line from paired data, use it carefully for prediction, and calculate residuals with the correct sign.

Worked example

A fitted line is y-hat = 5 + 2x, and the observed value at x = 4 is 11. Find the residual.

11 - (5 + 2(4)) = -2.

Residual is observed minus predicted, so a negative residual lies below the fitted line.

Teaching notes

Make the practice count

  • Compute Sxx and Sxy from the displayed observations before finding the slope.
  • Keep unrounded coefficients for predictions and residuals.
  • Mark predictions outside the observed x range as extrapolation.

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Math SheetNameScoreTeacherDateLinear Regression and ResidualsFit an ordinary least-squares line with an intercept from the displayed pairs. Keep full precision until the final answer; residual equals observed y minus predicted y.1.Use the least-squares line to predict lemonade sales when temperature is 14 degrees C. Identify interpolation or extrapolation.x: Temperature (degrees C)1011121314151617y: Lemonade sales (cups)4247475252596062Lemonade sales (cups)Temperature (degrees C)810121416186560555045402.Find the ordinary least-squares line with an intercept, and draw that fitted line on the scatterplot.x: Temperature (degrees C)5791113151719y: Lemonade sales (cups)3740485161657075Lemonade sales (cups)Temperature (degrees C)05101520807060504030Use the displayed pairs as the numeric truth; association does not prove causation.math-sheet.comPage 1 of 3
Math SheetNameScoreTeacherDateLinear Regression and ResidualsFit an ordinary least-squares line with an intercept from the displayed pairs. Keep full precision until the final answer; residual equals observed y minus predicted y.3.Interpret the fitted slope in context, then classify a prediction at x = 29 as interpolation or extrapolation and state a limitation.x: Temperature (degrees C)911131517192123y: Lemonade sales (cups)545971778491102109Lemonade sales (cups)Temperature (degrees C)5101520251201008060404.For the observation (12, 60), calculate the residual using observed minus predicted.x: Temperature (degrees C)7891011121314y: Lemonade sales (cups)4249505254606267Lemonade sales (cups)Temperature (degrees C)681012141670605040Use the displayed pairs as the numeric truth; association does not prove causation.math-sheet.comPage 2 of 3
Math SheetNameScoreTeacherDateLinear Regression and ResidualsFit an ordinary least-squares line with an intercept from the displayed pairs. Keep full precision until the final answer; residual equals observed y minus predicted y.5.Use the least-squares line to predict lemonade sales when temperature is 23 degrees C. Identify interpolation or extrapolation.x: Temperature (degrees C)5791113151719y: Lemonade sales (cups)3237465159627073Lemonade sales (cups)Temperature (degrees C)05101520806040206.Find the ordinary least-squares line with an intercept, and draw that fitted line on the scatterplot.x: Temperature (degrees C)678910111213y: Lemonade sales (cups)2329293031363741Lemonade sales (cups)Temperature (degrees C)468101214454035302520Use the displayed pairs as the numeric truth; association does not prove causation.math-sheet.comPage 3 of 3

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