Reference no: EM133675226
Data Science
Inferential Statistics
Scenario 1: Tutorial Attendance
Question: Does the mean tutorial attendance differ significantly between two groups of students attending different tutorial sessions?
group1_attendance = [18, 20, 22, 19, 21] # Tutorial attendance for group 1
group2_attendance = [16, 17, 15, 18, 19] # Tutorial attendance for group 2
Null Hypothesis (H0): There is no significant difference in the mean tutorial attendance between the two groups.
Alternative Hypothesis (H1): There is a significant difference in the mean tutorial attendance between the two groups.
Scenario 2: Exam Scores
Question: Is there a significant difference in the mean exam scores between two groups of students who used different study methods?
Null Hypothesis (H0): There is no significant difference in the mean exam scores between the two groups.
Alternative Hypothesis (H1): There is a significant difference in the mean exam scores between the two groups.
Data:
group1_scores = [85, 90, 88, 82, 87] # Exam scores for group 1
group2_scores = [79, 82, 83, 85, 81] # Exam scores for group 2
Scenario 3: Sales Performance
Question: Is the mean sales performance of a team significantly different from the population mean sales performance?
Null Hypothesis (H0): There is no significant difference in the mean sales performance between the team and the population.
Alternative Hypothesis (H1): There is a significant difference in the mean sales performance between the team and the population.
Data:
Population mean sales performance: μ = 500
Team sales performance (sample data): [480, 510, 490, 520, 500]
Scenario 4: Website Conversion Rate
Question: Is the mean conversion rate of a new website significantly higher than the industry average conversion rate?
Null Hypothesis (H0): There is no significant difference in the mean conversion rate between the new website and the industry average.
Alternative Hypothesis (H1): The mean conversion rate of the new website is significantly higher than the industry average.
Data:
Industry average conversion rate: μ = 0.1
Conversion rate of the new website (sample data): [0.12, 0.09, 0.11, 0.13, 0.10]
Scenario 5: Customer Preferences
Question: Is there a significant association between customers' preferences for product color and their age group?
Null Hypothesis (H0): There is no significant association between customers' preferences for product color and their age group.
Alternative Hypothesis (H1): There is a significant association between customers' preferences for product color and their age group.
Data:
Sample data of customers' preferences and age groups:
import pandas as pd # Sample data
data = {
'Color Preference': ['Red', 'Blue', 'Green', 'Red', 'Blue', 'Green', 'Red', 'Blue', 'Green'],
'Age Group': ['18-30', '18-30', '18-30', '31-45', '31-45', '31-45', '46-60', '46-60', '46-60']
}
df = pd.DataFrame(data)
Scenario 6: Employee Satisfaction
Question: Is there a significant association between employees' job satisfaction levels and their years of experience?
Null Hypothesis (H0): There is no significant association between employees' job satisfaction levels and their years of experience.
Alternative Hypothesis (H1): There is a significant association between employees' job satisfaction levels and their years of experience.
Data:
Sample data of employees' job satisfaction levels and years of experience: import pandas as pd
# Sample data data = {
'Job Satisfaction': ['Satisfied', 'Dissatisfied', 'Satisfied', 'Satisfied', 'Dissatisfied', 'Dissatisfied'], 'Years of Experience': ['0-5', '0-5', '6-10', '6-10', '11-15', '11-15']
}
df = pd.DataFrame(data)
Scenario 7: Exam Scores Across Multiple Classes
Question: Is there a significant difference in the mean exam scores among three different classes?
Null Hypothesis (H0): There is no significant difference in the mean exam scores among the three classes.
Alternative Hypothesis (H1): There is a significant difference in the mean exam scores among the three classes.
Data:
Sample data of exam scores for three classes: import pandas as pd
# Sample data data = {
'Class A': [85, 90, 88, 82, 87],
'Class B': [79, 82, 83, 85, 81],
'Class C': [75, 78, 79, 83, 80]
}
df = pd.DataFrame(data)
Scenario 8: Employee Performance Across Departments
Question: Is there a significant difference in the mean performance ratings across different departments?
Null Hypothesis (H0): There is no significant difference in the mean performance ratings across different departments.
Alternative Hypothesis (H1): There is a significant difference in the mean performance ratings across different departments.
Data:
Sample data of performance ratings for employees in three departments: import pandas as pd
# Sample data data = {
'Department A': [4, 5, 3, 4, 5],
'Department B': [3, 4, 2, 3, 4],
'Department C': [2, 3, 4, 2, 3]
}
df = pd.DataFrame(data)