Statistical Analysis using Python Numpy

提供方
Coursera Project Network
在此指导 项目中,您将:

Obtain two Numpy arrays from the DataFrame column to represent Female student scores and Male Student scores.

Add the Numpy code to determine the T-value and P-value of the data sets.

Add the function to remove outliers from each set of data, then re-compute the T-value and P-value.

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups. The T-Test is well known in the field of statistics. It is used to test a hypothesis using a set of data sampled from the population. To perform the T-Test, the population sample size, the mean, or average, of each population, and the standard deviation are all required. These will all be calculated in this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

Python StatisticsPython ProgrammingStatistics T TestNumpyStatitistics Pooled Variance

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Analyze the T-Test problem and use the Python Pandas to read from the CSV into a Data Frame.

  2. Obtain two Numpy arrays from the DataFrame column to represent Female student scores and Male Student scores.

  3. Compute the variance of the two arrays using the standard deviation from each array.

  4. Add the Numpy code to compute the pooled Variance and standard deviation and determine the T-value and P-value of the data sets.

  5. Add a function to remove outliers from each set of data, then re-compute the T-value and P-value.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

常见问题

常见问题

还有其他问题吗?请访问 学生帮助中心