Descriptive Statistics

Question 1

What is tidy data and what does it include?

Answer 1

The key to being able to successfully analyze data.

Rows (across)= observations
Columns (down)= variables

Question 2

What is the code for getting the first 6 and last 6 rows of data in R

Answer 2

> Head(name of table)
Tail(name of table)

Question 3

What is the code for finding the dimensions of a table in R?

Answer 3

Dim(name of table)

This will give you the number of rows (observations) and the number of variables in the data set

Question 4

How do you get useful information on a stored data table in R?

Answer 4

Help(name of table)

Question 5

What are discrete and continuous quantitative variables?

Answer 5

Discrete-can take on a finite number of values
Continuous-infinite number of values

Question 6

What is the code for getting a stored table’s structure?

Answer 6

Str(name of table)
This will report :
‘data.frame’(object): number of observations and number of variables. Also includes the variable names, what type of variable (quant or cat) Variable in about the first 10 entries.

Question 7

What does R call categorical variables?

Answer 7

Factors
Each individual category is a “level”

Question 8

Mode

Answer 8

The most frequently occurring value among all observations in a sample

Question 9

Frequency distribution

Answer 9

In order display of each value in a data set together with a number of times that value occurs

Easy way to find the mode

Question 10

How do you make a list in R?

Answer 10

Object <- c(put your list)
Concatenate function

Question 11

How do you make a frequency distribution in R?

Answer 11

table(object)

Question 12

What are the strengths and weaknesses of the mode as a measure of location?

Answer 12

Strength-easy to compute
Weakness-not useful if there’s a large number of possible values that occur in frequently

Question 13

What are two ways to calculate the mean in R?

Answer 13

1. sum(object)/length(object)
2. mean(object)

Question 14

What are the strengths and weaknesses of the arithmetic mean as a measure of location?

Answer 14

Strength-natural and most widely used
Weakness -over sensitive to extreme values

Question 15

What are the strengths and weaknesses of using the median as a measure of location?

Answer 15

Strength – insensitive to extreme values
Weakness-
Less sensitive to the actual values of the remaining data

Question 16

What is and what are the properties of a symmetric distribution?

Answer 16

-two halves of the distribution appear like mirror images
-mean and median are approximately the same

Question 17

What is another properties of a positively skewed distribution?

Answer 17

-Skewed to the right- long tail on the right
-Mean is usually larger than the median

Question 18

What is the properties of a negatively skewed distribution?

Answer 18

-Skewed to the left - long tail on the left
-Mean is usually smaller than the median

Question 19

What is the geometric mean?

Answer 19

The antilogarithm of the arithmetic mean computed in the log scale
-good for highly skewed lab data that can be based on logs of concentrations

Question 20

What is the antilogarithm of the natural log (ln) function?

Answer 20

The exponential function

Question 21

How do you make a matrix/array in R?

Answer 21

cbind( function
File.name <- cbind(v1, v2, v3….)

Question 22

How do you turn in array into a data frame structure in R?

Answer 22

Wrap the cbind function in as.data.frame
Variable.name <- as.data.frame(cbind(v1,v2,v3….)