In [4]:
from math import erf, sqrt
import numpy as np
import scipy.stats
For full credit, you must have the following items for each problem:
[1 point] Describe what and why the method you're using is applicable. For example, 'I chose the signed rank test because these are two matched datasets describing one measurement'
[1 point] Write out the null hypothesis. For example, 'The null hypothesis is that the two measurements sets came from the same population (synonymous with probability distribution)'
[1 point] Report the p-value and your alpha value
[1 point] if you accept/reject the null hypothesis and answer the question
You have a sample of an unknown metal with a melting point of $1,070^\circ{}$ C. You know that gold has a melting point of $1,064^\circ{}$ C and your measurements have a standard deviation of $5^\circ{}$ C. Is the unknown metal likely to be gold?
Recall from confidence intervals, that the standard deviation in distance from the true mean is $\sigma / \sqrt{N}$ when you know the true standard deviation, $\sigma$. You take three additional samples and get $1,071^\circ{}$ C, $1,067^\circ{}$ C, and $1,075^\circ{}$ C. Does your evidence for gold change? USe the original measurement as well.
In [17]:
data_1 = [3.05, 3.01, 3.20, 3.16, 3.11, 3.09]
data_2 = [3.18, 3.23, 3.19, 3.28, 3.08, 3.18]
In [21]:
data_empty_tummy = [17.1, 29.5, 23.8, 37.3, 19.6, 24.2, 30.0, 20.9]
data_garbage_tummy = [14.2, 30.3, 21.5, 36.3, 19.6, 24.5, 26.7, 20.6]
In [26]:
temperature = [15, 18, 21, 24, 27, 30, 33]
chem_yield = [66, 69, 69, 70, 64, 73, 75]
State which test is most appropriate for the following: