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?

“Living Area” and “Bathrooms” both have a strong positive correlation with home price.

None of the factors have strong correlations with wage; All of the correlations are below 0.6.

Q2 Continued from Q1: Does the strong correlation mean the variable causes home price to go up and down?

A strong association does not mean causation. In this case, home price rises with more bedrooms and bathrooms based on the correlation.

A strong association does not mean causation in regards to the wage as well.

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

The “Age”.

Within the context we can conclude that there may not be a confounding variable in regards to wage correlation.

Q4 What factors have strong negative correlation with home price?

None of the factors have strong negative correlations with the home price because none of the negative correlations exceed -0.6 or greater.

None of the factors have strong negative correlations with the wage because none of the negative correlations exceed -0.6 or greater.

Q5 What factors have weak correlation with home price?

Any factors with either positive or negative correlations below 0.4 and above -0.4 will have a weak correlation with home price. Examples could include lot size, fireplaces or age.

“Experience” has the weakest correlation with wage.

Q6 Simply based on the correlation coefficient, would you be sure that there is no relation at all? What would you do to check?

Based on the correlation coefficient, you would not be sure that there is no relation at all. In order to check, you would need to create a scatter plot to visualize the data.

Looking at the number of experience with wage, there seems to be little relation between the two variables. They seem not to ne related, but there is no way to know for sure. You could check with data visualization, such as a scatter plot.

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.