This is a detailed assignment that requires you to perform medical data analysis using statistical tests on a given data set. The goal is to identify which variables could potentially cause a patient's death.

Here's a step-by-step guide on how to approach this:

1. Import the necessary libraries: pandas for data manipulation and scipy.stats for statistical tests.

2. Load your data into a pandas DataFrame.

3. Identify the categorical variables in your data. These are the ones with dtype as int64. For each of these variables, perform a chi-squared test of independence with the death variable. You can use the chi2_contingency function from scipy.stats for this.

4. Identify the numerical variables in your data. For each of these variables, split the data by the death variable and perform two Shapiro–Wilk tests, one for each sample. You can use the shapiro function from scipy.stats for this.

5. Based on the p-values from the Shapiro–Wilk tests, decide which test to perform next. If the p-values indicate that both samples have a normal distribution (p-values greater than 0.05), perform an unpaired t-test with the parameter equal_var = False. You can use the ttest_ind function from scipy.stats for this. If the p-values indicate that the samples do not have a normal distribution, perform a Mann-Whitney U test. You can use the mannwhitneyu function from scipy.stats for this.

6. Implement a function called perform_tests that accepts a pandas DataFrame as an argument and returns a dictionary with the p-values from the tests performed. The dictionary should have four keys: mann_whitney, ttest, chi_square, and shapiro_wilk. The values for these keys should be lists of tuples with the variable name and the p-value from the corresponding test.

7. Round all p-values in the output to four decimal places.

Remember to check the requirements and the example provided in the assignment to ensure your function is working as expected.

Question

This is a detailed assignment that requires you to perform medical data analysis using statistical tests on a given data set. The goal is to identify which variables could potentially cause a patient's death.

Here's a step-by-step guide on how to approach this:

1. Import the necessary libraries: pandas for data manipulation and scipy.stats for statistical tests.

2. Load your data into a pandas DataFrame.

3. Identify the categorical variables in your data. These are the ones with dtype as int64. For each of these variables, perform a chi-squared test of independence with the death variable. You can use the chi2_contingency function from scipy.stats for this.

4. Identify the numerical variables in your data. For each of these variables, split the data by the death variable and perform two Shapiro–Wilk tests, one for each sample. You can use the shapiro function from scipy.stats for this.

5. Based on the p-values from the Shapiro–Wilk tests, decide which test to perform next. If the p-values indicate that both samples have a normal distribution (p-values greater than 0.05), perform an unpaired t-test with the parameter equal_var = False. You can use the ttest_ind function from scipy.stats for this. If the p-values indicate that the samples do not have a normal distribution, perform a Mann-Whitney U test. You can use the mannwhitneyu function from scipy.stats for this.

6. Implement a function called perform_tests that accepts a pandas DataFrame as an argument and returns a dictionary with the p-values from the tests performed. The dictionary should have four keys: mann_whitney, ttest, chi_square, and shapiro_wilk. The values for these keys should be lists of tuples with the variable name and the p-value from the corresponding test.

7. Round all p-values in the output to four decimal places.

Remember to check the requirements and the example provided in the assignment to ensure your function is working as expected.

Knowee AI · Accepted Answer