by Ryan | Oct 2, 2016 | advanced statistics

# A

Import vector of values of ice cream purchase numbers

>a <- c(8, 14, 16, 10, 11)

Generate random sample of 2 values and save to a vector

> b <- sample(a,2)

[1] 10 11

Calculate mean and standard deviation of the sample.

>mean(b)

[1] 10.5

> sd(b)

[1] 0.7071068

Create data.frame out of mean and stdev for the sample and population.

mean(a)

[1] 11.8

> sd(a)

[1] 3.193744

smp <- c(10.5,0.7071068)

> pop <- c(11.8,3.193744)

> c <- data.frame < (smp, pop)

# B

- I think that the sample proportion will have the approximately the same distribution since nq = 5.
- I think 100 is the smallest value of n for which p is approximately normal because anything smaller than n = 100 will make np < 5. The high value of p is very limiting.

by Ryan | Sep 11, 2016 | advanced statistics

Initially, I was just going to calculate the correlation coefficient manually in R without using the functions, but then I decided that if I was going to do it manually then I may as well do it by hand. I ended up making a mistake with spacing by not thinking about how many digits would be made out of squaring an already triple digit number.

From there, inputting the x and y values into R and executing the cor.tests() were very simple and straightforward. I feel like I’ve gotten to the point with R where I don’t feel like I’m completely lost all the time.

by Ryan | Sep 5, 2016 | advanced statistics

For this assignment, I decided to to make everything as one script and use the Source with Echo command to run it all at once. The only issue I had is that mode() does not yield a mode. It returns what storage mode the object uses, so I had to look up how to make a function to calculate the mode.

From there, I was able to call mat and matrange to pull up the tables of all the values.

by Ryan | Aug 22, 2016 | advanced statistics

ffff