to explore and summarize the data as follows:

a. Which variables are quantitative/numerical? Which are ordinal? Which are nominal?

b. Compute the mean, median, min, max, and standard deviation for each of the quantitative variables. This can be done using pandas as shown in Table 4.3.

c. Plot a histogram for each of the quantitative variables. Based on the histograms and summary statistics, answer the following questions:

i. Which variables have the largest variability?

ii. Which variables seem skewed?

iii. Are there any values that seem extreme?

d. Plot a side-by-side boxplot comparing the calories in hot vs. cold cereals. What does this plot show us?

e. Plot a side-by-side boxplot of consumer rating as a function of the shelf height. If we were to predict consumer rating from shelf height, does it appear that we need to keep all three categories of shelf height?

f. Compute the correlation table for the quantitative variable (method corr()). In addition, generate a matrix plot for these variables (see Table 3.4 on how to do this using the seaborn library).