In this exercise you will learn to visualize the pairwise relationships between a set of quantitative variables. To this end, you will make your own note of 8.1 Correlation plots from Data Visualization with R.

Q1 What factors have strong positve correlation with home price?

By default, it creates a ggplot2 graph were darker red indicates stronger positive correlations. living area and bathrooms

Q2 Continued from Q1: Does the strong correlation mean the variable causes home price to go up and down?

No because we need to know causation.

Q3 Continued from Q1: Do you think there is a confounding variable?

A confounding variable is a variable, other than the independent variable that you’re interested in, that may affect the dependent variable. The cofounding variable is age.

Q4 What factors have strong negative correlation with home price?

darker blue indicates stronger negative correlations and white indicates no correlation. None with home price.

Q5 What factors have little correlation with home price?

The lot size has little correlation with home price

Q6 Simply based on the correlation coefficient, would you be sure that there is no relation at all? What would you do to check?

they are not related in a linear relationship.

Q7 Plot correlation for CPS85 in the same way as above. Repeat Q1-Q6.

Hint: The CPS85 data set is from the mosaicData package. Explain wage instead of home price.

Q8 Hide the messages, the code and its results on the webpage.

Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.

Q9 Display the title and your name correctly at the top of the webpage.

Q10 Use the correct slug.