```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
from scipy.integrate import quad
from sympy.solvers import solve
from sympy import Symbol
import numpy as np
from pandas import Series, DataFrame
```


```python
import matplotlib.pyplot as plt
import numpy as np
from pandas import Series
from scipy.stats import norm, probplot
x = Series([24.4, 27.6, 27.8, 27.9, 28.5,
30.1, 30.3, 31.7, 32.2, 32.8,
33.3, 33.5, 34.1, 34.6, 35.8,
35.9, 36.8, 37.1, 39.2, 39.7])
alphak = (np.arange(1, x.size + 1) - 0.5) / x.size
quantile_theor = norm.ppf(q=alphak, loc=x.mean(), scale=x.std())
quantile_empir = np.sort(x)
plt.xlabel("Theor. Quantile")
plt.ylabel("Empirische Quantile")
plt.plot(quantile_theor, quantile_empir, "o")
plt.show()
```


```python
x = Series([24.4, 27.6, 27.8, 27.9, 28.5,
30.1, 30.3, 31.7, 32.2, 32.8,
33.3, 33.5, 34.1, 34.6, 35.8,
35.9, 36.8, 37.1, 39.2, 39.7])
probplot(x, plot=plt)
```


```python

```

68c3a72e6c2d7ee7dd825c00e6afb68e667956be
ipynb
Jupyter Notebook
Lernphase/SW05/.ipynb_checkpoints/Skript-checkpoint.ipynb
florianbaer/STAT
7cb86406ed99b88055c92c1913b46e8995835cbb
MIT
Lernphase/SW05/.ipynb_checkpoints/Skript-checkpoint.ipynb
florianbaer/STAT
7cb86406ed99b88055c92c1913b46e8995835cbb
MIT
Lernphase/SW05/.ipynb_checkpoints/Skript-checkpoint.ipynb
florianbaer/STAT
7cb86406ed99b88055c92c1913b46e8995835cbb
MIT
Qwen/Qwen-72B
1. YES
2. YES