Statistical Analysis using Python Numpy

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Coursera Project Network
在此指導項目中,您將:

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中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

  • Python Statistics
  • Python Programming
  • Statistics T Test
  • Numpy
  • Statitistics Pooled Variance

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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.

指導項目工作原理

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