Below is a discussion of the metrics from the table above and what each result indicates about the relationship between school_rating and reduced_lunch :

count: the number of schools at each rating. Most of the schools in Sally's sample have a 4- or 5-star rating, but 25% of schools have a 1-star rating or below. This confirms that poor school performance isn't merely anecdotal, but a serious problem that deserves attention.

mean: the average percentage of students on reduced_lunch among all schools by each school_rating . As school performance increases, the average number of students on reduced lunch decreases. Schools with a 0-star rating have 83.6% of students on reduced lunch. And on the other end of the spectrum, 5-star schools on average have 21.6% of students on reduced lunch. We'll examine this pattern further. in the graphing section.

std: the standard deviation of the variable. Referring to the school_rating of 0, a standard deviation of 8.813498 indicates that 68.2% (refer to readme) of all observations are within 8.81 percentage points on either side of the average, 83.6%. Note that the standard deviation increases as school_rating increases, indicating that reduced_lunch loses explanatory power as school performance improves. As with the mean, we'll explore this idea further in the graphing section.

min: the minimum value of the variable. This represents the school with the lowest percentage of students on reduced lunch at each school rating. For 0- and 1-star schools, the minimum percentage of students on reduced lunch is 53%. The minimum for 5-star schools is 2%. The minimum value tells a similar story as the mean, but looking at it from the low end of the range of observations.

25%: the bottom quartile; represents the lowest 25% of values for the variable, reduced_lunch . For 0-star schools, 25% of the observations are less than 79.5%. Sally sees the same trend in the bottom quartile as the above metrics: as school_rating increases the bottom 25% of reduced_lunch decreases.

50%: the second quartile; represents the lowest 50% of values. Looking at the trend in school_rating and reduced_lunch , the same relationship is present here.

75%: the top quartile; represents the lowest 75% of values. The trend continues.

max: the maximum value for that variable. You guessed it: the trend continues!

The descriptive statistics consistently reveal that schools with more students on reduced lunch under-perform when compared to their peers. Sally is on to something.