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 postive 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?

By looking at correlation coefficients we can see causation so the answer is No.

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

No, because the relationship of size of house and how expensive it is a genuine relationship.

Q4 What factors have strong negative correlation with home price?

Age also has a negative and weak correlation because age and price does not mean that the house is going to be less expensive, but there is no strong negative correlations in this data set.

Q5 What factors have little correlation with home price?

Lot size and pctcollege have little correlation becuse the numbers are negative and almost zero, they have nothing to do with the 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 could be related in non linear fashion, but to find this we would have to create a scatterplot to see the correct correlation.

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.