MS 150 Statistics Quiz 05 • Name:

Data table from SC 120 Biology yeast respiration rate experiment. Treat the data as being linear. Do not remove outliers.

Celsius °CCO2 bubbles per minute
230
274
328
3712
4216
4714
  1. ______________ Determine the slope of the linear regression (best fit line) for the data.
  2. ______________ Determine the y-intercept of the linear regression for the data.
  3. ______________ Use the slope and intercept to calculate the predicted bubble rate for 30°C?
  4. ______________ Use the slope and intercept to calculate the predicted temperature for a bubble rate of 10 bubbles per minute?
  5. ______________ Does the relationship between temperature and bubble rate appear to be linear, non-linear, or random (no relationship)?
  6. ______________ Determine the correlation coefficient r.
  7. ______________ Based on the linear regression, does yeast respiration as measured by the bubble rate increase or decrease with increasing temperature?
  8. ______________ Is the correlation positive or negative?
  9. ______________ Is the correlation none, weak, moderate, strong, or perfect?
  10. ______________ Determine the coefficient of determination.
  11. ______________ What percent in the variation in temperature accounts for the variation the bubble rate?
  12. Biology question: What appears to be happening to the yeast at 47°C?
  13. ______________ Can we safely predict the bubble rate for 100°C?
  14. Why can we or why can we not safely predict the the bubble rate for 100°C?
  15. 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?

The following table and data are under consideration in the three questions below.

Cantilever length Lc versus mass m background rectangle Cantilver
Lc (cm)m (g)
50.00
56.010
59.520
62.040
64.560
66.580
68.7100
70.3120
72.0140
73.5160
74.5180
75.6200
76.5220
77.0240
major grid lines axes x-axis and y-axis data points as circles text layers Cantilever length Lc versus mass m A rational function Lc (cm) m (g) y-axis labels 0 24 48 72 96 120 144 168 192 216 240 x-axis labels 50 53 56 59 62 65 68 71 74 77 80

  1. ____________________ What is the nature of the relationship: linear, non-linear, or random?
  2. ____________________ Would performing a linear regression and correlation on this data be statistically valid?
  3. ____________________ Why or why not?