3.5-3.7 Quiz Review

There is a linear relationship between temperature in degrees Fahrenheit (x) and chirps per minute (y) for the striped ground cricket. The equation of the regression line relating these variables is predicted y = =0.31 + 0.212x. Predicted the cricket chirp rate when the temperature is 82 °F. Do not round your answer.

Tags

CCSS.HSS.ID.C.7

The equation of the regression line for predicting y = chirps per minute from x = temperature in degrees Fahrenheit is predicted y = -0.31 + 0.212x. One observation in these data measured 16.2 chirps per minute at 83 °F. Calculate the residual for this observation. Do not round any of your answers.

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.7

The equation of the regression line for predicting y = chirps per minute from x = temperature in degrees Fahrenheit is predicted y = -0.31 + 0.212x. One observation in these data measured 16.2 chirps per minute at 83 °F. The residual for this point was -1.086. Interpret the residual in context.

• The actual temperature was 1.086 higher than the temperature predicted by the model

• The actual temperature was 1.086 lower than the temperature predicted by the model

• The actual chirps per minute were 1.086 greater than the chirps per minute predicted by the model

• The actual chirps per minute were 1.086 lower than the chirps per minute predicted by the model

Tags

CCSS.HSF.LE.B.5

The equation of the regression line for predicting y = chirps per minute from x = temperature in degrees Fahrenheit is predicted y = -0.31 + 0.212x. Interpret the slope of the regression line.

• For every one °F increase in the temperature, the chirps per minute increases by 0.212 chirps.

• For every one °F increase in the temperature, the predicted chirps per minute increases by 0.212 chirps.

• For every one increase in chirps per minute, the predicted temperature increases by 0.212 °F

• For every one chirp increase in chirps per minute, the temperature increases by 0.212 °F

Tags

CCSS.HSF.LE.B.5

The equation of the regression line for predicting y = chirps per minute from x = temperature in degrees Fahrenheit is predicted y = -0.31 + 0.212x. Interpret the y intercept

• When the number of chirps per minute is 0 chirps, the predicted temperature is -0.31 °F

• When the number of chirps per minute is 0 chirps, the temperature is -0.31 °F

• When the temperature is 0 °F, the predicted number of chirps per minute is -0.31 chirps.

• When the temperature is 0 °F, the number of chirps per minute is -0.31 chirps.

Tags

CCSS.HSF.LE.B.5

Based on the previous question, does the y-intercept have meaning in this context?

• Yes, the temperature could be 0 °F and the chirps per minute could be -0.31

• No, the number of chirps per minute can't be negative. This is most likely an extrapolation

Tags

CCSS.HSF.LE.B.5

When Mentos are dropped into a newly opened bottle of Diet Coke, carbon dioxide is released from the Diet Coke very rapidly, causing the Diet Coke to be expelled from the bottle. Using 16-ounce (2-cup) bottles of Diet Coke, two statistics students dropped either 2, 3, 4, or 5 mentos into a randomly selected bottle. Then, they subtracted this measurement from the original amount in the bottle to calculate the amount of Diet Coke expelled (in cups). The scatterplot shows the relationship between y = amount expelled (in in cups) and x = number of Mentos, along with the least-squares regression line. The standard deviation of the residuals for this model is 0.067 cup. Interpret this value.

• The actual amount of Diet Coke expelled is typically about 0.067 cup away from the amount of Diet Coke expelled predicted by the model.

• The actual number of Mentos is typically about 0.067 Mentos away from the number of Mentos predicted by the model.

• 6.7% of the variability in the amount of Diet Coke expelled is accounted for by the linear relationship with number of Mentos

• 6.7% of the variability in the number of Mentos is accounted for by the linear relationship with Diet Coke expelled.

Tags

CCSS.HSS.ID.A.1

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.7

When Mentos are dropped into a newly opened bottle of Diet Coke, carbon dioxide is released from the Diet Coke very rapidly, causing the Diet Coke to be expelled from the bottle. Using 16-ounce (2-cup) bottles of Diet Coke, two statistics students dropped either 2, 3, 4, or 5 mentos into a randomly selected bottle. Then, they subtracted this measurement from the original amount in the bottle to calculate the amount of Diet Coke expelled (in cups). The scatterplot shows the relationship between y = amount expelled (in in cups) and x = number of Mentos, along with the least-squares regression line. The coefficient of determination is 0.44. Interpret this value.

• The actual amount of Diet Coke expelled is typically about 0.44 cup away from the amount of Diet Coke expelled predicted by the model.

• The actual number of Mentos is typically about 0.44 Mentos away from the number of Mentos predicted by the model.

• 44% of the variability in the amount of Diet Coke expelled is accounted for by the linear relationship with number of Mentos

• 44% of the variability in the number of Mentos is accounted for by the linear relationship with Diet Coke expelled.

Tags

CCSS.HSS.ID.B.6

CCSS.HSS.ID.C.7

When Mentos are dropped into a newly opened bottle of Diet Coke, carbon dioxide is released from the Diet Coke very rapidly, causing the Diet Coke to be expelled from the bottle. Using 16-ounce (2-cup) bottles of Diet Coke, two statistics students dropped either 2, 3, 4, or 5 mentos into a randomly selected bottle. Then, they subtracted this measurement from the original amount in the bottle to calculate the amount of Diet Coke expelled (in cups). The scatterplot shows the relationship between y = amount expelled (in in cups) and x = number of Mentos, along with the least-squares regression line. A residual plot is also given. Use the residual plot to determine whether the regression model is appropriate.

• The model is not appropriate because there is a leftover curved patter in the residual plot.

• The model is not appropriate because there is no leftover curved pattern in the residual plot.

• The model is appropriate because there is a leftover curved pattern in the residual plot.

• The model is appropriate because there is no leftover curved patter in the residual plot.