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?

The living area and number of bathrooms have a strong correlation because they are a darker red on the correlation plot.

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

According to the correlation coefficent the answer is no.

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

Because the size and value of the house is a genuine relationship, the answer is no.

Q4 What factors have strong negative correlation with home price?

Age has a negative impact of the price of a home but there are no particularly strong negative correlations in this dataset.

Q5 What factors have little correlation with home price?

Lotsize and pctcollege have very little correlation with home price, because their number is nearly 0.

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

It is possible they are related in a non-linear fashion.

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.

Wage has a high correlation with age, which makes sense, as you get older you make more money.

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.