• For labs going forward, if you find a particular block produces a lot of output, feel free to comment it out and add a note like: “Omitted to save space in output”
  • Really happy to see so many of you starting to use ggplot2!
• Github has a great feature to allow you to render HTML pages from a Github repo. Go to https://htmlpreview.github.io and paste in the URL to the HTML file in your repo and it (usually) renders as a webpage.
• Random numbers and seeds: https://data606.net/post/2019-02-17-random_numbers_and_seeds/
• Few notes about colors in plots (my general advice, not rules per se).
  • Only use color when it adds value. For example, the colors represent different categories.
  • Use more than color to differentiate. For example, for points, change the symbol type and color.
  • Try to use color blind safe colors. See this article for more details: https://venngage.com/blog/color-blind-friendly-palette/
  • I like ColorBrewer for picking color pallets. They were designed for cartography, but are useful in many other contexts: http://colorbrewer2.org/
  • See blog post here: https://data606.net/post/2019-02-20-colors/

• Sheryl Piechocki (2.5)
• David Apolinar (2.7) https://rpubs.com/dapolloxp/468688
• Anthony Munoz (2.19)
• Javern Wilson (2.29) http://rpubs.com/javernw/jwDATA606wk3pres

samples <- rep(NA, 1000)
for(i in seq_along(samples)) {
    coins <- sample(c(-1,1), 100, replace=TRUE)
    samples[i] <- cumsum(coins)[length(coins)]
}
head(samples)

## [1]  -8   8  -2 -10  -8   6

(m.sam <- mean(samples))

## [1] 0.162

(s.sam <- sd(samples))

## [1] 9.883088

within1sd <- samples[samples >= m.sam - s.sam & samples <= m.sam + s.sam]
length(within1sd) / length(samples)

## [1] 0.677

within2sd <- samples[samples >= m.sam - 2 * s.sam & samples <= m.sam + 2* s.sam]
length(within2sd) / length(samples)

## [1] 0.951

within3sd <- samples[samples >= m.sam - 3 * s.sam & samples <= m.sam + 3 * s.sam]
length(within3sd) / length(samples)

## [1] 0.999

SAT scores are distributed nearly normally with mean 1500 and standard deviation 300. ACT scores are distributed nearly normally with mean 21 and standard deviation 5. A college admissions officer wants to determine which of the two applicants scored better on their standardized test with respect to the other test takers: Pam, who earned an 1800 on her SAT, or Jim, who scored a 24 on his ACT?

• Z-scores are often called standard scores:

\[ Z = \frac{observation - mean}{SD} \]

• Z-Scores have a mean = 0 and standard deviation = 1.

Converting Pam and Jim’s scores to z-scores:

\[ Z_{Pam} = \frac{1800 - 1500}{300} = 1 \]

\[ Z_{Jim} = \frac{24-21}{5} = 0.6 \]

SAT scores are distributed nearly normally with mean 1500 and standard deviation 300.

• ∼68% of students score between 1200 and 1800 on the SAT.
• ∼95% of students score between 900 and 2100 on the SAT.
• ∼99.7% of students score between 600 and 2400 on the SAT.