8.1 Correlation plots
Larsen Hellested
10/8/2019
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 positve correlation with home price?
The factors that have positive correlations with home pricing is the bedrooms, the living areas, rooms and the price.
Q2 What factors have strong positve correlation with home price?
The living area and the price have strong positive correlations with home price.
Q3 What factors have negative correlation with home price?
The age of the home has negative correlations with home price.
Q4 What factors have strong negative correlation with home price?
There are no strong negative correlations with home prices.
Q5 What set of two variables has the highest positive Pearson Product-Moment correlation coefficient? What set of two variables has the greatest negative Pearson Product-Moment correlation coefficient?
Living area and number of rooms has the highest positive correlation coefficient, and the bathrooms and age is the strongest neggative correlation coefficent.
Q6 What set of two variables has the Pearson Product-Moment correlation coefficent that is closest to zero? Would you be sure that the two variables are not related at all? What would you do to check?
Lot size and age, and land value and age. We would have to use a scatter plot to find if the two variables are related at all. The scatter plot shows that there is no linear relationship to the 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.
Experience and age have the most positive correlation.
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.