MS 150 Statistics fall 2005 review quiz 12 • Name:

Temperature/°C CO2
bubbles/minute
19 0.6
28 2.9
29 4.3
34 15.7
39 21.3

This term the SC 120 Biology students made measurements of the respiration rate of yeast by counting the rate at which the yeast produced carbon dioxide bubbles. Yeast produces carbon dioxide as a by-product of respiration. The student's data is recorded in the table. Calculate the following statistics for the temperature data:

  1. __________ Find the sample size n.
  2. __________ Find the median.
  3. __________ Find the mode.
  4. __________ Find the sample mean.
  5. __________ Find the sample standard deviation.
  6. __________ Find the sample coefficient of variation CV.
  7. __________ Calculate the slope of the best fit line for the temperature versus bubble rate data.
  8. __________ Calculate the y-intercept for the data.
  9. __________ Is the correlation positive, negative, or neutral?
  10. __________ Determine the correlation coefficient
  11. __________ Is the correlation none, low, moderate, high, or perfect?
  12. __________ Determine the coefficient of determination.
  13. __________ What percent in the variation in temperature explains the variation bubble rate?
  14. What would you tell an SC 120 Biology student who asked whether the temperature is a good predictor of the respiration rate for the yeast?
  15. __________ Based on the best fit line and the data, does yeast respiration increase or decrease with increasing temperature?
  16. __________ Based on the equation of the best fit line, what would be the predicted bubble rate at 24°C?
  17. __________ Based on the equation of the best fit line, what would be the predicted temperature for a bubble rate of 10 bubbles per minute?
  18. __________ Based on the equation of the best fit line, what would be the predicted bubble rate at 100°C?
Statistic or Parameter Symbol Equations Excel
Slope b =SLOPE(y data, x data)
Intercept a =INTERCEPT(y data, x data)
Correlation r =CORREL(y data, x data)
Coefficient of Determination r² =(CORREL(y data, x data))^2