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 has a very strong positive correlation with home price.

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

The stronger the correlation, the more connected a vairable is with another variable. If negative, the variable could correlate with a price decrease but if positive, it could show a price increase. However, this is correlational data, and correlation does not mean causation.

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

Yes, I think that in this example there is a lot more that effects the price of a house. These variables could be the economy, budget, and emotions in the transaction.

Q4 What factors have strong negative correlation with home price?

The age of a home has a strong negative correlation with price. The older the house, the cheaper it is in most cases.

Q5 What factors have little correlation with home price?

Lot size has very little correlation with home price in this example. You can tell because it is the lowest correlation number, and the color of the cell is very light (almost white)

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

No, I would check for non-linear relationships between the two variables. These coefficients are representative of only linear relationships.

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.

1.) The strongest correlational value to wage is education. It is not very strong, and only comes in at 0.38. 2.) No, this only shows the relationship between the two variables. This data is correlational not causal. 3.) Yes, in this example there is no reference to the exampled persons’ character. There are also non-linear relationships that are not expressed in these coefficients. 4.) There are no negative correlational relationships with wage. 5.) Experience has very little correlation with wage. 6.) I would check to see if there was any sort of nonlinear relationship between wages and another variable. ## 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.