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.
The living area and number of bathrooms represent a strong positive correlation with home price.
We don’t know the causation since there is no context to look at.
When two variables are being associated with one correlation, there is always a third variable. In this case, I would assume that the third variable for living area and home price and bathrooms and home price would be the number of people living in that home.
There are no variables that have a strong negative correlation with home price.
Lot size has the least correlation with home price at 0.16.
No, we’re not sure, we would check by visualizing data from a scatterplot.
Hint: The CPS85 data set is from the mosaicData package. Explain wage instead of home price.
Question 1. There is no strong positive correlation with wage.
Question 2. No, because there is no strong correlation and we don’t know the causation.
Question 3. There is no confounding variable since the relationship is reasonable.
Question 4. There are no variables that have a strong negative correlation with wage.
Question 5. Experience has the least correlation with wage at 0.09.
Question 6. There is a weak linear relationship but there is still a relationship, we check this by visualizing data from a scatterplot.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.