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?

Factors that have a strong posative correlation to home price would be the amount of fireplaces and bedrooms that are in a home. This is shown by the darker red shaded box’s in the correlation plot.

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

No, the strong correlation does not have any causes to home prices to go up or down.

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

The confounding varialbe would be age in this case.

Q4 What factors have strong negative correlation with home price?

There are no strong negative correlation seen with the factors that decide home price.

Q5 What factors have little correlation with home price?

Two factors that have little correlation with home price would be the lot size and pctcollege because their values are negative and almost zero. Thier values being 0 tells you it does not have alot to do with the home price.

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

Theres no certain relation shown but it is defianly not a linear function. To check this you could create a scatter plot with the data to depict is theres a non-linear relation bassed on the correlation coefficient.

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.