DO NOT USE CHATGPT! need help with these questions.

Similarly, we might think that increasing the default tip suggestions made some riders angry and would make them respond by tipping zero dollars. Run a logistic regression in which the depenent variable is the zero_tip and the independent variables are CMT, post_feb, and CMTxpost. At the mean, what was the marginal effect of increasing the default tip suggestions on the probability that a rider would decide to top zero dollars? Store answer in the variable effect_on_zero_tip as a float. Is the effect statistically significant? Store answer in the variable stat_sig as either the string "yes" or "no".

My code is wrong:

import pandas as pd import statsmodels.api as sm

# Create a DataFrame with sample data data = pd.DataFrame({ 'zero_tip': [1, 0, 0, 1, 0, 1, 1, 0, 0, 1], 'CMT': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0], 'post_feb': [1, 1, 1, 0, 1, 0, 1, 1, 0, 0] })

# Define independent variables (features) and the dependent variable X = data[['CMT', 'post_feb']] y = data['zero_tip']

# Add a constant term to the independent variables for logistic regression X = sm.add_constant(X)

# Create a logistic regression model logit_model = sm.Logit(y, X)

# Fit the logistic regression model to the data result = logit_model.fit()

# Calculate the marginal effects marginal_effects = result.get_margeff()

# Get the marginal effect on 'zero_tip' effect_on_zero_tip = marginal_effects.margeff[0]

# Calculate the p-value for 'CMT' p_value = result.pvalues['CMT']

# Determine if the effect is statistically significant stat_sig = 'yes' if p_value < 0.05 else 'no'

# Print the results print("Marginal Effect on zero_tip:", effect_on_zero_tip) print("P-value for CMT:", p_value) print("Statistical Significance:", stat_sig)

# Summary of the logistic regression model print(result.summary())

#hidden tests for problem 4.2 are within this cell

You have failed this test due to an error. The traceback has been removed because it may contain hidden tests. This is the exception that was thrown:

AssertionError: Problem 4.2: my answer for effect_on_zero_tip does not match the official answer.

question 2: Let's hypothesize that the mean tip percentage in the population is 17%. Use sample_mean, standard_error, and the hypothesized value to calculate the t-statistic. Then use scipy.stats's norm.cdf function to calculate the p-value. Finally, decide whether you reject or fail to reject the hypothesis. Store your answers in the variables t_stat, p_val, and reject_or_not. The variable reject_or_not should either be the string 'reject' or the string 'fail to reject'. Remember to look at the units of the variable tip_percentage (in the output of taxi.head() above) in order to set the hypothesized value correctly.

My code is wrong:

import numpy as np import pandas as pd from scipy.stats import norm

# Read the dataset taxi = pd.read_csv('assets/taxi.csv')

# Extract the 'tip_percentage' column tip_percentages = taxi['tip_percentage']

# Define the hypothesized mean hypothesized_mean = 0.17

# Calculate sample statistics sample_mean = np.mean(tip_percentages) sample_size = len(tip_percentages) sample_var = np.var(tip_percentages, ddof=1) # Set ddof=1 for sample variance standard_error = np.sqrt(sample_var / sample_size)

# Calculate the t-statistic t_stat = (sample_mean - hypothesized_mean) / standard_error

# Calculate the two-tailed p-value p_val = 2 * norm.cdf(-np.abs(t_stat))

# Define the significance level alpha = 0.05

# Determine whether to reject or fail to reject the null hypothesis reject_or_not = 'reject' if p_val < alpha else 'fail to reject'

# Print the results print(f"t-statistic: {t_stat}") print(f"p-value: {p_val}") print(f"Decision: {reject_or_not}")

You have failed this test due to an error. The traceback has been removed because it may contain hidden tests. This is the exception that was thrown: AssertionError: Problem 2.1:Your answer for t_stat does not match the official answer.

write code with explanation