In what scenario is the median particularly informative as a measure of central tendency?

Cheat sheet for using R X=c(1,2,3,4,2,5) # assigns the set of data to the variable X sum(X) #calculates the sum of all data contained in X mean(X)# calculates the average of data contained in X table(X) #counts the number of time a value occurs in X cumsum(X) # calculates the cumulative sum and can be used to calculate cumulative freq X=sum(X)/length(X) # is an expression that calculates the mean Y= X-1 # subtracts 1 from each data point in X Z=seq(1,1000,by=2) # generates a sequence of numbers from 1 to 1000 by 2 (1, 3, 5…) When calculating variance and standard deviation I need to see the long form of the calculation in R Given the following values of X, calculate the sample variance, sample standard deviation and z-score for the data set. Solve the problem without using the R function SD and show all the steps using R.  I only need the R code. There is not need to do the arithmetic.  Write the mathematical formula for standard deviation and z-score calculation.   #calculate the sample variance, sample standard deviation and z-scorex

When faced with a variable that assumes a vast range of values, what strategy is commonly employed to manage the data effectively?

In the context of cumulative frequency distributions, what unique insight do they offer compared to simple frequency distributions?

What adjustment is made to calculate a sample variance to ensure it is an unbiased estimate of a population variance?

In an example from a research study on cholesterol levels, what does the use of a new drug compared to a standard drug illustrate about experimental studies?

What characteristic defines a nominal scale?

The transformation of frequency distributions into relative frequencies serves what primary function?

Which measure is identified for summarizing a frequency distribution into a single value that suggests a typical observation?

Let   , for 0 < X , 0 < Y.   Find P(X>1, Y