The video tutorial explains the difference between correlation and causation, emphasizing that correlation does not imply causation. It provides examples of positive, negative, and no correlation using scatterplots. The video also discusses how causation is a cause-and-effect relationship and highlights the importance of understanding these concepts for making informed business decisions. The tutorial concludes by encouraging viewers to use statistical analysis and controlled experiments to determine causation accurately.

What is the range of values that correlation can take?

• 0 to 2

• -1 to 1

• 0 to 1

• -2 to 2

Which method is most commonly used to calculate correlation?

• Fisher's

• Pearson's

• Kendall's

• Spearman's

What type of correlation is shown when temperature increases and ice cream sales also increase?

• Negative correlation

• No correlation

• Positive correlation

• Inverse correlation

In a scatterplot, what indicates a strong correlation between two variables?

• Dots are scattered randomly

• Dots form a clear pattern

• Dots are far apart

• Dots are in a circular pattern

What is causation also known as?

• Random association

• Cause-and-effect relationship

• Correlation

• Statistical anomaly

Which of the following is an example of causation?

• Rainfall and low temperatures

• Traffic and cellphone sales

• Height and weight

• Temperature and ice cream sales

What is a common mistake when interpreting correlation?

• Thinking correlation is always positive

• Ignoring correlation altogether

• Believing correlation is always negative

• Assuming correlation implies causation

In the example of Rena's bad mood and tiredness, what was the third variable causing both?

• Weather

• Work stress

• Lack of sleep

• Traffic jam

Why are controlled experiments necessary in determining causation?

• They are faster

• They are cheaper

• They eliminate bias

• They establish cause-and-effect relationships

What should be considered before making business decisions based on correlation?

• Past experiences

• Common sense

• Statistical analysis

• Intuition