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.
Bathrooms;living area, rooms;living area, and most things correlated to price are above .5 or .6.
This causes the price to go up. If there are more master bed and baths, then the house prices go up.
There are many confounding variables depending on what a particlar customer is looking to aqquire in a home.
Age and almost any other variable. The strongest negative correlations are between age;bathrooms, and age;price. However these are all still considered weak relationships.
Age;bedrooms has the weakest positive correlation, but lot size has the weakest collumn overall.
In order for there to be no correlation between variables the CC would have to have a value of 0, meaning the relationship is non linear. You would check the calculation and formula.
Hint: The CPS85 data set is from the mosaicData package. Explain wage instead of home price.
1.)Age and experience?* are strongly correlated with a calculation of .98.
2.)Yes the strong correlation does affect price.
3.)The confounding variable could be education;wage. The higher educated, the better job, the better wage.
4.)No strong negative correlations
5.)Wage;experience have little correlation with a CC of .09.
6.)It is again unlikely you would be able to find rekation between any of the variables.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide.