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A snapshot of the summary - Stats Data and Models
1 Stats starts here
What is one of the interesting chalanges of Statistics?
Here can be more than one right answer.
2.2 Data Tables
Why do we make data tables?
To organize the values and make the context of the data clear.
What do rows of a data table correspond with?
Individual cases about Whom (or which- if they're not people) we record some characteristics.
What are experimental units?
Animals, websites or other inanimate subjects on which we experiment.
2.4 What and why
What is a quantitative variable?
A measured variable with units answers questions about the quantity of what is measured.
What should you do when it isn't clear if a variable is categorial or quantitive?
Think about Why you are looking at it and what you want it to tell you.
2.6 Identifying identifiers
What are identifier variables?
It assigns a variable as a unique identity.
What can you do with identifier variables?
-Combine data from different source
-provide unique labels.
2.8 What can go wrong
What can go wrong in reading data?
-You can label a variable as categorial or quantitative without thinking about the question you want it to answer.
-When variable's values are numbers, don't assume it's quantitative. They don't have to be.
-not being skeptical about discovering the truth with the data you're interpretating.
3 Displaying and describing categorial data
3.1 The three rules of data analysis
Why do you have to make pictures of the data?
-A display of your data will reveal things you are nog likely to see in a table of numbers and will help you to think clearly about the patterns and relationships that may be hiding in your data.
-It will show the important features and patterns. It shows you things you did not ecpect to see: the extraordinary (possibly wrong) data values or unexpecterd patterns.
-the best way to tellothers about your data.