14. Descriptive Statistics

Question 1

descriptive Statistics

Answer 1

The branch of statistics dealing with how to describe and summarize data.

How can I communicate the important characteristics of my data?

Question 2

frequency distribution

Answer 2

a (chart) showing the unique values of the data set, along with their frequency within the data set

Question 3

bar graph

Answer 3

used to depict a frequency distribution of CATEGORICAL variables (space between bars)

Question 4

histogram

Answer 4

used to depict a frequency distribution of a QUANTITATIVE variable (no space between bars)

Question 5

mean

Answer 5

sum of all values divided by number of values

X = E(x) / n

Question 6

median

Answer 6

centermost value when the set is ordered

Question 7

mode

Answer 7

most frequent value in a set

Question 8

mean, median & mode

Answer 8

measures of central tendency

Question 9

nominal

Answer 9

a variable that can be CATEGORIZED, but not quantified.

Question 10

ordinal

Answer 10

a variable that can be RANKED, but not quantified.

Question 11

interval

Answer 11

a variable that can be QUANTIFIED, without a true relationship to 0

Question 12

ratio

Answer 12

a variable that can be QUANTIFIED, where 0 indicates absence of quantity

Question 13

variance

Answer 13

measure of average distance to mean, measured in square units

Question 14

standard deviation

Answer 14

measure of average distance to mean

Question 15

variance (formula)

Answer 15

E(x-M)^2 / n

Question 16

standard deviation

Answer 16

(E (x-M)^2 / n ) ^1/2

Question 17

normal distribution

Answer 17

a distribution where 68% falls within one standard dev, 95% within 2 standard dev, and 99.7 within 3 standard devs of the mean

Question 18

unstandardized difference between means

Answer 18

compare two data sets
by finding the difference between the data set means, in natural units.
(ie. M1 - M2)

Question 19

cohen’s d

Answer 19

compare two data sets
by finding the difference between the data set means, in standardized units.
ie) M1 - M2 / SD
note: The SD can be for set 1 or 2

Question 20

0. 2 = small
1. 5 = medium
2. 8 = large

Answer 20

thresholds of effect size for interpreting cohen’s d

Question 21

effect size

Answer 21

magnitude of relationship between two variables

Question 22

Pearson correlation coefficient

Answer 22

vector value [-1,1] that describes magnitude (absolute value) and direction (sign) of relationship between variables, when when variable is controlled for.

p = E (Zx Zy) / n

• only valid for linear relationships*
• scatterplot data first*

Question 23

partial correlation coefficient

Answer 23

vector value describing magnitude and direction of relationship between variables, when more than one variable is controlled for.

Question 24

curvilinear regression

Answer 24

technique used to determine nature of relationship between variables that have a curviliear relationship (ie. elliptical)

Question 25

regression analysis

Answer 25

using one or more independent variables to predict the values of the dependent variables

Question 26

regression analysis (appropriate cases)

Answer 26

predict values dependent variable with quantitative IV and DV

Question 27

ANOVA (appropriate cases)

Answer 27

predict dependent variable values with categorical IV, quantitative DV

Question 28

ANCOVA (appropriate cases)

Answer 28

predict dependant variable values with mixed IVs and quantitative DV

Question 29

simple regression
Y = dependent variable value
m = slope = regression coefficient
x = the single IV value
b = y intercept of the line of regression

Answer 29

regression analysis in which only one IV is controlled for

Y = mx + b

Question 30

multiple regression
Y = dependent variable value
m1 = first regression coefficient
x1 = first x value
m2 = second regression coefficient
x2 = second x value 
... etc 
b = line of regression y intercept

Answer 30

regression analysis in which more than one IV is controlled for
Y = mx1 + mx2 + … + b

Question 31

contingency table

Answer 31

used to compare the relationship of categorical variables.

if % are horizontal, compare down the columns, else reverse