Lecture 2

Question 1

Lecture 2:

What is Rank Order Distribution?

Answer 1

An ordered listing of the data in a single column
- ranking data from highest to lowest

Question 2

Lecture 2:

What is Range?
- How is it calculates?
- provide example

Answer 2

The distance from the highest to lowest value
- highest # minus lowest #
- represented by “R”
- eg; data = 1, 2, 3, 4, 5, 6, 7, 8… R=8-1= 7

Question 3

Lecture 2:

What is Mean?

Answer 3

The average value (sum of all #’s divided by total data points)

Question 4

Lecture 2:

What does “N” represent?

Answer 4

The total # of people or items in the data set

Question 5

Lecture 2:

What is Frequency?

Answer 5

How often a value was measured
- eg; how many people had the same # of pull ups if measuring pull ups

Question 6

Lecture 2:

What is Simple Frequency Distribution?

Answer 6

The # of cases (f) at each given value (x)
- eg; x = # of pull ups & f = # of people who did that many pull ups

Question 7

Lecture 2:

What is Grouped Frequency Distribution?
- when is it used?

Answer 7

Data is grouped into bins (a range of values)
- used if more than 20 samples and range is greater than 20
- there are typically 15 groups formed as this loses the least amount of information

Question 8

Lecture 2:

What is interval size & how is it found?

Answer 8

Interval size (i) = range/15 (or # of bins)
- gives us the range of the bins

Question 9

Lecture 2:

What are 3 ways of displaying data?

Answer 9

1.) Spreadsheet (table)
2.) Histograms - bar graph
3.) Frequency Polygon - line graph

Question 10

Lecture 2:

What is a Curve?

Answer 10

A line that results when scores (X) are plotted against frequencies (Y) on a graph

Question 11

Lecture 2:

What is a normal curve?
- who is it named after?
- why is it considered “normal”?

Answer 11

AKA Gaussian Curve after Carl Guass & is a symmetrical bell-shaped curve
- mean, median, and mode are all same values
- frequency of scores decline in a predictable manner and scores deviate further and further from the center

Question 12

Lecture 2:

When discussing normal curves, what is the Central Limit Theorem?

Answer 12

A sum of random #’s becomes normally distributed as more & more of the random #’s are added together
- the commonness (ubiquity) of the normal distribution - randomness leads to Gaussian distributions

Question 13

Lecture 2:

When discussing deviations from the normal curve… What is the Bimodal Curve?

Answer 13

A distribution that has 2 modes

Question 14

Lecture 2:

When discussing deviations from the normal curve… What is the Skewed Curve?
- 2 types?

Answer 14

A distribution where a disproportionate # of the subjects score towards one end of the scale
- positive or negative skews (based on which end of the graph its closer to)

Question 15

Lecture 2:

When discussing skewed curves… What is a Positive Skew?

Answer 15

Frequency is more negative in a positive skew as its closer to the lower #’s & staying away from the larger #’s
- on a line graph, it curves rise than goes downhill

Question 16

Lecture 2:

When discussing skewed curves… What is a Negative Skew?

Answer 16

Frequency is more positive as it is closer to the larger #’s & staying away from the smaller #’s
- on a line graph, it begins with a steep incline up the the curve that occurs after the mid point on the graph