ANOVA
Now that you've established equal variance using a Levene test and assessed visually the approximate normality of the log-transformed salaries, it's time to conduct an ANOVA test! Recall that the purpose of the ANOVA test is to determine if biotech, enterprise software and health care companies had equal average funding. Since these three groups satisfy the conditions of an ANOVA test, you know the conclusions from that test will be valid.
The DataFrames you created (biotech_df, enterprise_df and health_df) have been loaded for you. The packages pandas as pd, NumPy as np, Matplotlib as plt, and the stats package from SciPy have all been loaded as well. The log-transforms of the funding values that you computed in a previous exercise are provided for you.
本练习是课程的一部分
Foundations of Inference in Python
练习说明
- Conduct a one-way ANOVA test using each of the three log-transformed fundings in the following order of arguments: Biotechnology, Enterprise Software, Health Care.
- Print out if the p-value is significant at 5%.
交互式实操练习
通过完成这段示例代码来试试这个练习。
biotech_log_funding = np.log(biotech_df['funding_total_usd'])
enterprise_log_funding = np.log(enterprise_df['funding_total_usd'])
health_log_funding = np.log(health_df['funding_total_usd'])
# Conduct a one-way ANOVA test to compare the log-funding
s, p_value = ____
# Print if the p-value is significant at 5%
____