Statistical Analysis using Python Numpy

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

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

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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