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Assessing the growth rate

To compute the growth rate, you can do a linear regression of the logarithm of the total bacterial area versus time. Compute the growth rate and get a 95% confidence interval using pairs bootstrap. The time points, in units of hours, are stored in the numpy array t and the bacterial area, in units of square micrometers, is stored in bac_area.

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Anleitung zur Übung

  • Compute the logarithm of the bacterial area (bac_area) using np.log() and store the result in the variable log_bac_area.
  • Compute the slope and intercept of the semilog growth curve using np.polyfit(). Store the slope in the variable growth_rate and the intercept in log_a0.
  • Draw 10,000 pairs bootstrap replicates of the growth rate and log initial area using dcst.draw_bs_pairs_linreg(). Store the results in growth_rate_bs_reps and log_a0_bs_reps.
  • Use np.percentile() to compute the 95% confidence interval of the growth rate (growth_rate_bs_reps).
  • Print the growth rate and confidence interval to the screen. This has been done for you, so hit 'Submit Answer' to view the results!

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Compute logarithm of the bacterial area: log_bac_area
log_bac_area = ____

# Compute the slope and intercept: growth_rate, log_a0
____, ____ = ____

# Draw 10,000 pairs bootstrap replicates: growth_rate_bs_reps, log_a0_bs_reps
____, ____ = ____(
    ____, ____, size=____
)
    
# Compute confidence intervals: growth_rate_conf_int
growth_rate_conf_int = ____

# Print the result to the screen
print("""
Growth rate: {0:.4f} 1/hour
95% conf int: [{1:.4f}, {2:.4f}] 1/hour
""".format(growth_rate, *growth_rate_conf_int))
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