| title: “8.1 Correlation plots” |
| author: “Joaquin German” |
| date: “10/8/2019” |
| output: html_document |
| editor_options: |
| chunk_output_type: console |
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.
Fireplace and the price of the house has a positive. The connection is only moderate to weak because it is only 0.38. So houses with fireplaces do cost more but not alot more.
The factor that has the strongest correlation with home price is the living area. This is a strong correlation because it is 0.71. This means that the price increases with a larger living area.
The one factor that has a negative correlation with home price is age. There is no correlation between age and home price because it is -0.19
There are no STRONG negative correlations with home price.
The living area and number of rooms has the highest correlation with a 0.73. This is a strong connection
Hint: The CPS85 data set is from the mosaicData package. Explain wage instead of home price.
1. The factors that have a positive relationship is experience, age, and education 2. There is no factor with a strong correlation only moderate correlation 3. There is no negative relationship 4. There is no strong negative relationship ## 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.