What are the limitations of analyzing real data with missing values and why is it impossible to really know such data?

Data Analytics

This project is mostly based on issues with data quality: see more carefully chapter 2 Data and Sampling Distributions from ‘Practical Statistics for Data Scientists’. Cover in the project the following:

1. Explain figures 2.5 and 2.6 from the text book Practical Statistics for Data Scientists chapter 2 Data and Sampling Distributions .
2. Discuss the following:
   1. How can you tell if the data is an outlier or if it is something important?
   2. Which data is the noise and how is the noise different from outliers?
3. When there are missing values, explain the pros and cons of the following strategies:
   1. Elimination of Data Objects
   2. Estimation of Missing Values
4. What are the limitations of analyzing real data with missing values and why is it impossible to really know such data?

Answer for each question at least 200 words for each question.