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Data Science Interview Challenge
Data Science Interview Challenge
Challenge 3: Hypothesis Testing
The fascinating realm of statistics houses the intricate process of hypothesis testing. At its core, hypothesis testing is about making inferences regarding populations based on sample data. We formulate hypotheses and test them, drawing conclusions about broader datasets by analyzing a subset.
For instance, if you're studying the impact of a new teaching method in a classroom and observe a significant improvement in students' grades, can you conclusively say that the method is effective? The answer lies in hypothesis testing.
Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.
import matplotlib.pyplot as plt import seaborn as sns # Load the dataset data = sns.load_dataset('tips') # Sample of data display(data.head()) # Total bill amounts grouped by smoking status sns.boxplot(x='smoker', y='total_bill', data=data) plt.title('Total Bill Amounts Grouped by Smoking Status') plt.show() # Number of smokers vs. non-smokers by gender sns.countplot(x='sex', hue='smoker', data=data) plt.title('Number of Smokers vs. Non-Smokers by Gender') plt.show()
Swipe to show code editor
In this exercise, leveraging the Seaborn's tips dataset, you'll:
- Test if there's a significant difference in the
total_billamounts between smokers and non-smokers. Use Mann–Whitney U test. - Examine the relationship between the
sexandsmokercolumns, determining if these two categorical variables are independent of each other.
Note
In this task, the significance level (alpha) for the p-value is set at
0.1, rather than the conventional0.05. The choice of alpha can vary across tasks based on the context, the level of rigor required, or specific industry practices; commonly adopted values include0.01,0.05, and0.1.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge 3: Hypothesis Testing
The fascinating realm of statistics houses the intricate process of hypothesis testing. At its core, hypothesis testing is about making inferences regarding populations based on sample data. We formulate hypotheses and test them, drawing conclusions about broader datasets by analyzing a subset.
For instance, if you're studying the impact of a new teaching method in a classroom and observe a significant improvement in students' grades, can you conclusively say that the method is effective? The answer lies in hypothesis testing.
Here's the dataset we'll be using in this chapter. Feel free to dive in and explore it before tackling the task.
import matplotlib.pyplot as plt import seaborn as sns # Load the dataset data = sns.load_dataset('tips') # Sample of data display(data.head()) # Total bill amounts grouped by smoking status sns.boxplot(x='smoker', y='total_bill', data=data) plt.title('Total Bill Amounts Grouped by Smoking Status') plt.show() # Number of smokers vs. non-smokers by gender sns.countplot(x='sex', hue='smoker', data=data) plt.title('Number of Smokers vs. Non-Smokers by Gender') plt.show()
Swipe to show code editor
In this exercise, leveraging the Seaborn's tips dataset, you'll:
- Test if there's a significant difference in the
total_billamounts between smokers and non-smokers. Use Mann–Whitney U test. - Examine the relationship between the
sexandsmokercolumns, determining if these two categorical variables are independent of each other.
Note
In this task, the significance level (alpha) for the p-value is set at
0.1, rather than the conventional0.05. The choice of alpha can vary across tasks based on the context, the level of rigor required, or specific industry practices; commonly adopted values include0.01,0.05, and0.1.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів