```python
import matplotlib.pyplot as plt
import scipy.stats as st
import seaborn as sns
import pandas as pd
from scipy.stats import norm, uniform, expon, t
from scipy.integrate import quad
from sympy.solvers import solve
from sympy import Symbol
import numpy as np
from pandas import Series, DataFrame
```


```python
methodeA = Series([79.98, 80.04, 80.02, 80.04, 80.03, 80.03, 80.04,
79.97, 80.05, 80.03, 80.02, 80.00, 80.02])
print(methodeA.mean())
print(methodeA.std())
```

    80.02076923076923
    0.023965787580611863



```python
from scipy.stats import norm
np.random.seed(1)
methodeA_sim1 = Series(np.round(norm.rvs(size=6, loc=80, scale=0.02), 2))
methodeA_sim1
print(methodeA_sim1.mean())
print(methodeA_sim1.std())
```

    79.99333333333333
    0.028751811537128993



```python
np.random.seed(17)
for i in range(5):
    methodeA_sim1 = Series(np.round(norm.rvs(size=6, loc=80, scale=0.02), 2))
    print("Mittelwert:", np.round(methodeA_sim1.mean(), 3))
    print("Standardabw.:", np.round(methodeA_sim1.std(), 3))
    print()
```

    Mittelwert: 80.01
    Standardabw.: 0.027
    
    Mittelwert: 80.007
    Standardabw.: 0.02
    
    Mittelwert: 79.992
    Standardabw.: 0.028
    
    Mittelwert: 79.995
    Standardabw.: 0.016
    
    Mittelwert: 79.992
    Standardabw.: 0.013
    



```python
np.random.seed(463137)
methodeA_sim2 = Series(np.round(norm.rvs(size=6, loc=80, scale=0.02), 2))
methodeA_sim2
print(methodeA_sim2.mean())
print(methodeA_sim2.std())
```

    80.04
    0.019999999999998862



```python
np.random.seed(647)
methodeA_sim3 = Series(np.round(norm.rvs(size=13, loc=80, scale= 0.02), 2))
methodeA_sim3
print(methodeA_sim3.mean())
print(methodeA_sim3.std())
```

    79.99307692307691
    0.023588350014576355



```python
norm.cdf(x=499.57, loc=500, scale=1/np.sqrt(100))
```




    8.539905470989153e-06




```python
norm.cdf(x=171.54, loc=180, scale=10/np.sqrt(2))*100
```




    11.576543872967077




```python
print(1-norm.cdf(x=80.02, loc=80, scale=0.01))
print(norm.ppf(q=[0.025, 0.975], loc=80, scale=0.01/np.sqrt(13)))
print(norm.interval(alpha=0.95, loc=80, scale=0.01/np.sqrt(13)))
```

    0.022750131948200636
    [79.99456404 80.00543596]
    (79.9945640379659, 80.0054359620341)



```python
norm.ppf(q=0.025)
```




    -1.9599639845400545




```python
series = Series([5.9, 3.4, 6.6, 6.3, 4.2, 2.0, 6.0, 4.8, 4.2, 2.1, 8.7, 4.4, 5.1, 2.7, 8.5, 5.8, 4.9, 5.3, 5.5, 7.9])
print(series.mean())
print(series.std())
```

    5.215000000000001
    1.8838021462764578



```python
t.cdf(x=series.mean(), df=series.size-1, loc=5, scale=series.std()/np.sqrt(series.size))
```




    0.6921780567888249




```python
help(norm.cdf)
```

    Help on method cdf in module scipy.stats._distn_infrastructure:
    
    cdf(x, *args, **kwds) method of scipy.stats._continuous_distns.norm_gen instance
        Cumulative distribution function of the given RV.
        
        Parameters
        ----------
        x : array_like
            quantiles
        arg1, arg2, arg3,... : array_like
            The shape parameter(s) for the distribution (see docstring of the
            instance object for more information)
        loc : array_like, optional
            location parameter (default=0)
        scale : array_like, optional
            scale parameter (default=1)
        
        Returns
        -------
        cdf : ndarray
            Cumulative distribution function evaluated at `x`
    



```python

```

967febbb1d88ac54307de664aa266688e8e3fe3b
ipynb
Jupyter Notebook
Lernphase/SW06/Skript.ipynb
florianbaer/STAT
7cb86406ed99b88055c92c1913b46e8995835cbb
MIT
Lernphase/SW06/Skript.ipynb
florianbaer/STAT
7cb86406ed99b88055c92c1913b46e8995835cbb
MIT
Lernphase/SW06/Skript.ipynb
florianbaer/STAT
7cb86406ed99b88055c92c1913b46e8995835cbb
MIT
Qwen/Qwen-72B
1. YES
2. YES