Sample Standard Deviation Equation:

\(\text{SampleStandardDeviation} = \sqrt\frac{\sum{(x_i-\bar{x})^2}}{n-1}\)

n = size of sample

\(x_i\) = each observed value of a sample

\(\bar{x}\) = sample mean

Population Standard Deviation Equation:

\(\sigma = \sqrt\frac{\sum{(X-\mu)^2}}{N}\)

N = size of population

X = each population value

\(\mu\) = population mean

data(airquality)
temp_vals = airquality$Temp
mean_temp = mean(temp_vals, na.rm=T)
sqdf = (temp_vals - mean_temp)^2
var = sum(sqdf, na.rm=T) / (sum(!is.na(temp_vals)) - 1)
stddev = sqrt(var)
stddev

## [1] 9.46527

library(ggplot2)
ggplot(data = airquality, aes(x=Temp, y=Ozone)) +
  geom_point(na.rm=T)

library(ggplot2)
ggplot(airquality,aes(x=Temp)) + geom_histogram(binwidth=1)

—

library(plotly) data(beaver1)

plot_ly(data=beaver1, x=~temp, y=~time, type=“scatter”, mode=“markers”) %>% layout(title=“Beavers Temperature vs Time”) xax = list(title=“Temperature”) yax = list(title=“Time”)