2.1: Frequency Distributions

Question 1

Frequency Distribution Tables

Answer 1

Help organize values so you can see patterns and detect outlier values

Question 2

What are the two types of outlier values and how do you remove them?

Answer 2

1. Mistakes: Simply remove data value from stats
2. Extremes: Keep the data value but use as a limitation of the study (causing scew).

Question 3

Grouped Frequency Table

Answer 3

Combine nearby items into score “bins” that represent a range of values.

Question 4

What is an example of a variable that can be grouped?

Answer 4

Age- different decades

Question 5

What are the three characteristics that bins (groupings) have to have?

Answer 5

1. Exclusive
2. Equal sized
3. Exhaustive

Question 6

Exclusive

Answer 6

Each item fits in no more than one bin (minimum and max values).

Question 7

Equal sized

Answer 7

Each bin has the same range

Question 8

Exhaustive

Answer 8

Every item in the data fits into a bin.

Question 9

Raw Frequency Table

Answer 9

Reflects the sample size

Question 10

What is a limitation or raw frequency tables?

Answer 10

It can be hard to compare across studies with different sample sizes.

Question 11

Relative Frequency Tables

Answer 11

Express the frequency in proportions or percentages.

Question 12

What is the formula to find the proportion of a bin?

Answer 12

Proportion of a bin = # of values in bin / total # of values

Question 13

Cumulative Frequency

Answer 13

Counts accumulated scores across bins

Question 14

What are cumulative frequency graphs useful for?

Answer 14

Counting scores up to a threshold value.

Question 15

When is cumulative frequency possible?

Answer 15

Only is the levels have an order to them (age ranges)

Question 16

How can cumulative frequency also be counted?

Answer 16

In relative proportions or percentages

Question 17

% Total

Answer 17

The percentage of observations in one bin compared to the total number of values.

Question 18

Cumulative Percent

Answer 18

Relative to the total number of observations that have data

Question 19

Histograms

Answer 19

A graphical depiction of a frequency table by plotting how often different values occur.

Question 20

What are the 4 expectations of histograms

Answer 20

1. X values have possible values
2. Bins should be equal sized, exclusive, no gaps, and exhaustive of all possible scores.
3. Y axis is the frequency
4. The increment of values on the y axis should be equal sized

Question 21

What are histograms used for?

Answer 21

To describe the shape of a variable’s distribution.

Question 22

What do areas with higher frequencies represent?

Answer 22

There are more values that fall within that given region.

Question 23

What are the 3 types of distribution shapes?

Answer 23

1. Normal
2. Skewed
3. Bi-modal

Question 24

Normal Distribution

Answer 24

A symmetrical distribution of data with a single peak and a bell shape.

Question 25

Skewed Distribution

Answer 25

Many observations clumped on one end, with a “tail” of extreme values on the other (skewed) end.

Question 26

Where is the tail end of a negative skew?

Answer 26

Towards the more negative values

Question 27

Where is the tail end of a positive scew?

Answer 27

Towards the more positive values.

Question 28

Bi-Modal Distribution

Answer 28

Graphs that have two peaks.

Question 29

Which of these variables is most likely to be positively skewed and why? a. Narcissism b. GPA c. Hours spent studying

Answer 29

Hours spent studying -no max score -more common to have very large (outlier) values