python code

import pandas as pd
C6 = [123, 456, 235, 189, 174, 139, 94, 169]
vacmotdesc['C6'] = pd.Categorical(C6)
C6_Gender = pd.crosstab(vacmotdesc['C6'], vacmotdesc['Gender'])
print(C6_Gender)

2.
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import figure

data = vacmotdesc

figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.hist(data['Age'], bins=10, by=data['Segment'])
plt.title('Age by Segment')
plt.xlabel('Age')
plt.ylabel('Frequency')

plt.subplot(1, 2, 2)
plt.hist(data['Obligation'], bins=10, by=data['Segment'])
plt.title('Obligation by Segment')
plt.xlabel('Obligation')
plt.ylabel('Frequency')

plt.tight_layout()
plt.show()


3.
import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.boxplot(x='C6', y='Age', data=vacmotdesc)
plt.xlabel("Segment number")
plt.ylabel("Age")
plt.show()
	
4.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Assuming 'vacmotdesc' is your DataFrame
# If not, you need to load your data into a pandas DataFrame first

sns.boxplot(x='C6', y='Obligation', data=vacmotdesc, 
            width=0.5, notch=True)

plt.xlabel("Segment number")
plt.ylabel("Moral obligation")
plt.show()

5.
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.graphics.effects_plot import plot_effects
from scipy import stats

# Assuming vacmotdesc is a pandas DataFrame

# First linear model
X = pd.get_dummies(vacmotdesc['C6'], drop_first=True, prefix='C6')
y = vacmotdesc['Age']
model1 = LinearRegression(fit_intercept=False)
model1.fit(X, y)
coeffs1 = pd.Series(model1.coef_, index=['C61', 'C62', 'C63', 'C64', 'C65', 'C66'])
print(coeffs1)

# Second linear model
X = pd.get_dummies(vacmotdesc['C6'], drop_first=True, prefix='C6')
X = sm.add_constant(X)
y = vacmotdesc['Age']
model2 = sm.OLS(y, X).fit()
print(model2.summary())

# Logistic regression model
vacmotdesc['C6_3'] = (vacmotdesc['C6'] == 3).astype(int)
X = pd.get_dummies(vacmotdesc['Obligation2'], drop_first=True, prefix='Obligation2')
X['Age'] = vacmotdesc['Age']
X = sm.add_constant(X)
y = vacmotdesc['C6_3']
model_C63 = sm.GLM(y, X, family=sm.families.Binomial()).fit()
print(model_C63.summary())

# Plot effects
plot_effects(model_C63)
plt.show()

# Anova-like analysis
from statsmodels.stats.anova import anova_lm
anova_results = anova_lm(model_C63)
print(anova_results)

# Full model
X = vacmotdesc.drop(['C6', 'C6_3'], axis=1)
X = pd.get_dummies(X, drop_first=True)
X = sm.add_constant(X)
y = vacmotdesc['C6_3']
full_model_C63 = sm.GLM(y, X, family=sm.families.Binomial()).fit()
print(full_model_C63.summary())


6. Binary logistic
import pandas as pd
import statsmodels.api as sm
import numpy as np

# Assuming 'vacmotdesc' is your DataFrame
# Create the formula
f = 'C6 == 3 ~ Age + C(Obligation2)'

# Fit the logistic regression model
model_C63 = sm.formula.glm(formula=f, data=vacmotdesc, family=sm.families.Binomial())
results = model_C63.fit()

# Print the model summary
print(results.summary())

# If you want to extract specific coefficients:
coefficients = results.params
intercept = coefficients['Intercept']
age_coef = coefficients['Age']
obligation2_q2 = coefficients['C(Obligation2)[T.Q2]']
obligation2_q3 = coefficients['C(Obligation2)[T.Q3]']
obligation2_q4 = coefficients['C(Obligation2)[T.Q4]']

print(f"Intercept: {intercept}")
print(f"Age: {age_coef}")
print(f"Obligation2 Q2: {obligation2_q2}")
print(f"Obligation2 Q3: {obligation2_q3}")
print(f"Obligation2 Q4: {obligation2_q4}")

# Print degrees of freedom, deviance, and AIC
print(f"Degrees of Freedom: {results.df_model} Total (i.e. Null); {results.df_resid} Residual")
print(f"Null Deviance: {results.null_deviance:.0f}")
print(f"Residual Deviance: {results.deviance:.0f}")
print(f"AIC: {results.aic:.0f}")


Multinomial reg

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

# Assuming 'vacmotdesc' is a pandas DataFrame
vacmotdesc['Oblig2'] = vacmotdesc['Obligation2']

# Prepare the data
X = vacmotdesc[['Age', 'Oblig2']]
y = vacmotdesc['C6']

# Encode categorical variables if necessary
le = LabelEncoder()
X['Oblig2'] = le.fit_transform(X['Oblig2'])
y = le.fit_transform(y)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model_C6 = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=42)
model_C6.fit(X_train, y_train)

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# Assuming 'vacmotdesc' is your DataFrame
X = vacmotdesc[['Age', 'Oblig2']]
y = vacmotdesc['C6']

# Create a preprocessor for one-hot encoding 'Oblig2'
preprocessor = ColumnTransformer(
    transformers=[
        ('cat', OneHotEncoder(drop='first'), ['Oblig2'])
    ],
    remainder='passthrough'
)

# Create a pipeline with preprocessor and multinomial logistic regression
model_C6 = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', LogisticRegression(multi_class='multinomial', solver='lbfgs'))
])

# Fit the model
model_C6.fit(X, y)

# Print model summary
print("Model: Multinomial Logistic Regression")
print(f"Number of classes: {len(model_C6.named_steps['classifier'].classes_)}")
print(f"Features: {['Age'] + [f'Oblig2_{cat}' for cat in model_C6.named_steps['preprocessor'].named_transformers_['cat'].categories_[0][1:]] + ['Intercept']}")
print(f"Coefficients shape: {model_C6.named_steps['classifier'].coef_.shape}")
print(f"Intercepts shape: {model_C6.named_steps['classifier'].intercept_.shape}")
print(f"AIC: {model_C6.named_steps['classifier'].score(X, y):.4f}")




import pandas as pd
import statsmodels.api as sm
from statsmodels.tools import add_constant
from sklearn.preprocessing import LabelBinarizer

# Assuming vacmotdesc is a pandas DataFrame
# vacmotdesc = pd.read_csv('path_to_vacmotdesc.csv') # Load your data here

# Prepare the data
X = vacmotdesc[['Age', 'Oblig2']]  # Independent variables
y = vacmotdesc['C6']  # Dependent variable

# Convert categorical variable Oblig2 to dummy/indicator variables
X = pd.get_dummies(X, columns=['Oblig2'], drop_first=True)

# Add a constant term for the intercept
X = add_constant(X)

# Fit the multinomial logistic regression model
model = sm.MNLogit(y, X)
result = model.fit()

# Print the summary
print(result.summary())