Z and T score to probability in python3
I'm developing a basic calculator that will take some data and return the Z and T scores, now, I need to check this values against the T and Z tables, however, the only idea i get, is to use 2D array, and recreate the same lists that any person would use to calculate them, but that would mean loading a big table into the memory, is there any function i could use?
python-3.x probability
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
add a comment |
I'm developing a basic calculator that will take some data and return the Z and T scores, now, I need to check this values against the T and Z tables, however, the only idea i get, is to use 2D array, and recreate the same lists that any person would use to calculate them, but that would mean loading a big table into the memory, is there any function i could use?
python-3.x probability
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
add a comment |
I'm developing a basic calculator that will take some data and return the Z and T scores, now, I need to check this values against the T and Z tables, however, the only idea i get, is to use 2D array, and recreate the same lists that any person would use to calculate them, but that would mean loading a big table into the memory, is there any function i could use?
python-3.x probability
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
I'm developing a basic calculator that will take some data and return the Z and T scores, now, I need to check this values against the T and Z tables, however, the only idea i get, is to use 2D array, and recreate the same lists that any person would use to calculate them, but that would mean loading a big table into the memory, is there any function i could use?
python-3.x probability
python-3.x probability
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
asked Nov 22 '18 at 22:19
Fred O.Fred O.
114
114
add a comment |
add a comment |
1 Answer
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I am assuming you want to convert the Z and T scores to p-values of the corresponding distributions, the z-distribution and the t-distribution. To do so you can use the following survival functions from the library 'scipy.stats':
For the Z score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.norm.sf(abs(Z))
For the T score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.t.sf(abs(T))
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I am assuming you want to convert the Z and T scores to p-values of the corresponding distributions, the z-distribution and the t-distribution. To do so you can use the following survival functions from the library 'scipy.stats':
For the Z score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.norm.sf(abs(Z))
For the T score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.t.sf(abs(T))
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
add a comment |
I am assuming you want to convert the Z and T scores to p-values of the corresponding distributions, the z-distribution and the t-distribution. To do so you can use the following survival functions from the library 'scipy.stats':
For the Z score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.norm.sf(abs(Z))
For the T score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.t.sf(abs(T))
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
add a comment |
I am assuming you want to convert the Z and T scores to p-values of the corresponding distributions, the z-distribution and the t-distribution. To do so you can use the following survival functions from the library 'scipy.stats':
For the Z score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.norm.sf(abs(Z))
For the T score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.t.sf(abs(T))
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
I am assuming you want to convert the Z and T scores to p-values of the corresponding distributions, the z-distribution and the t-distribution. To do so you can use the following survival functions from the library 'scipy.stats':
For the Z score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.norm.sf(abs(Z))
For the T score:
# one-sided (multiply by 2 if two-sided)
p_value = scipy.stats.t.sf(abs(T))
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
answered Nov 23 '18 at 0:58
DavidPMDavidPM
33519
33519
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
add a comment |
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
Have a question, shouldnt i need to input the degrees of freedom?
– Fred O.
Nov 27 '18 at 1:14
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
No, these tests do not have degrees of freedom. As for the 'abs()' (the absolute value), I wrote a comment yesterday but I do not see it anymore. For simplicity, yes, keep the absolute value and avoid differentiating between positive and negative values. Such distributions are symmetric.
– DavidPM
Nov 27 '18 at 8:41
add a comment |
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