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from scipy import stats

from scipy.stats import t

from scipy.stats import ttest_1samp

import numpy as np

import numpy as np

import numpy as np # linear algebra

import pandas as pd

import pandas as pd

import pandas as pd

import pandas as pd

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')

mean = np.mean(df['Result'])
std = np.std(df['Result'])

import numpy as np
from scipy.stats import t
conf_level = 0.95
n = len(df['Result'])
df = n - 1
t_stat = t.ppf((1 + conf_level) / 2, df)
margin_of_error = t_stat * std / np.sqrt(n)
lower = mean - margin_of_error
upper = mean + margin_of_error

import pandas as pd
import numpy as np
from scipy.stats import ttest_1samp
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
null_mean = 0
alpha = 0.05
t_stat, p_val = ttest_1samp(df['Result'], null_mean)

import pandas as pd
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
df['Gender'] = df['Gender'].replace({'F': 1, 'M': 0})
df['Gender'] = df['Gender'].astype('float64')

import pandas as pd
from scipy import stats
df.dropna(inplace=True)
bigResult = df[df['Result'] >= 4.0].iloc[:, 1:]
smallResult = df[df['Result'] < 4.0].iloc[:, 1:]
f_val, p_val = stats.f_oneway(bigResult, smallResult)

import pandas as pd
df = pd.read_excel('/kaggle/input/iedata/iedatason.xlsx', sheet_name='Form Yanıtları 1')
df['Gender'] = df['Gender'].replace({'F': 1, 'M': 0})
df['Gender'] = df['Gender'].astype('float64')
df.head()