Statistical Analysis using Python Numpy

提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

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ダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

あなたが開発するスキル

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

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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.

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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よくある質問

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