Correlation and Association in Data

The video tutorial covers exploratory data analysis (EDA) for different types of variables, focusing on two quantitative variables using scatter plots and correlation. It explains the concept of association, including positive, negative, and no association, and discusses the strength of these associations. The tutorial uses college and car data to illustrate these concepts, encouraging viewers to predict associations before analyzing them. The video emphasizes that correlation does not imply causation and highlights the importance of understanding data relationships.

What type of variable is analyzed using a scatter plot?

• Categorical

• Nominal

• Quantitative

• Ordinal

Which of the following is a summary statistic discussed in the video?

• Mode

• Correlation

• Median

• Mean

What is the main difference between correlation and causation?

• Causation is a type of correlation

• Correlation does not imply causation

• Causation is easier to prove

• Correlation implies causation

What type of association is it when one variable increases and the other decreases?

• Positive

• Negative

• No association

• Random

Which data source is mentioned for college data?

• College Board

• National Center for Education Statistics

• U.S. Census Bureau

• U.S. News and World Report

What is the relationship between applications and acceptances in colleges?

• Random association

• No association

• Negative association

• Positive association

What does a scatter plot with a clear upward trend indicate?

• Negative association

• Random association

• No association

• Positive association

What is the strength of association if a scatter plot shows a clear trend?

• No association

• Strong

• Moderate

• Weak

What is the likely association between out-of-state tuition and student-faculty ratio?

• Random

• No association

• Negative

• Positive

What is the association between enrollment and room and board costs?

• Positive and strong

• Positive and weak

• Negative and weak

• No association