Statistics Chapter 1

Front 1

Statistics

Back 1

The science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.

Front 2

Variable

Back 2

A characteristic or attribute that can assume different values

Front 3

Data

Back 3

values (measurements or observations) that the variables can assume.

Front 4

Random variables

Back 4

variables whose values are determined by chance.

Front 5

Data set

Back 5

a collection of data values

Front 6

Descriptive statistics

Back 6

consists of the collection, organization, summarization, and presentation of data. . . example census.

Front 7

Population

Back 7

consists of all subjects (human or otherwise) that are being studied.

Front 8

Sample

Back 8

A group of subjects selected from a population.

Front 9

Inferential statistics

Back 9

consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

Front 10

Qualitative Variables

Back 10

Variables that can be placed into distinct categories according to some characteristic or attribute.

Front 11

Quantitative Variables

Back 11

Numerical and can be ordered or ranked.

Front 12

The 2 kinds of quantitative variables are?

Back 12

Discrete and continuous variables

Front 13

Discrete variables

Back 13

assume values that can be counted such as 1, 2, 3, ...

Front 14

Continuous variables

Back 14

can assume an infinite number of values between any two specific values. They are obtained by measuring. They often include fractions and decimals.

Front 15

What are the 4 measurement scales?

Back 15

Nominal, ordinal, interval, and ratio.

Front 16

Nominal Measurement

Back 16

classifies data into mutually exclusive (nonoverlapping), exhausting categories in which no order or ranking can be imposed on the data. Exp. college instructors classified bu subject (ie. english, history, math)

Front 17

Ordinal Measurement

Back 17

Classifies data into categories that can be ranked; however, precise differences between the ranks do not exist. Exp. first place, second place, third place.

Front 18

Interval Measurement

Back 18

ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero. Exp. IQ tests. There is a meaningful difference between an IQ of 90 and an IQ of 91.

Front 19

Ratio Measurement

Back 19

Possesses all the characteristics of interval meansurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population.

Front 20

Examples of nominal level data

Back 20

zip code, gender, eye color, political affiliation, religious affiliation, major field, nationality

Front 21

Examples of ordinal level data

Back 21

grade (A, B, C, D, F), Judging (first second, third), Rating scale (Poor, good excellent), Ranking of tennis players.

Front 22

Examples of interval level data

Back 22

SAT score, IQ, Temperature

Front 23

Examples of Ratio level data

Back 23

Height, weight, time, salary, age.

Front 24

Systematic samples

Back 24

number each subject of the population and then select every x'th subject. For example every 1 in 40

Front 25

Random samples

Back 25

use random numbers assign every person a number and pick them all at random usually computer generated.

Front 26

Stratified samples

Back 26

divide the population into groups according to some characteristic that is important to the study.

Front 27

Cluster samples

Back 27

The population is divided into groups by some means such as school or geographic area then ttehes clusters are randomly selected and all members are surveyed.

Front 28

Convenience Sample

Back 28

Just parking yourself outside of a mall. Not the best because you often get biased results

Front 29

Observational study

Back 29

researchers merely observe what is happening or what has happened in the past and tries to draw conclusions based on these observations.

Front 30

Experimental Study

Back 30

The researcher manipulates one of the variables and tries to determine how the manipulation influences other variables.

Front 31

independent variable

Back 31

The one that is manipulated by the reasercher in an experimental study

Front 32

dependent variable

Back 32

the resultant variable in an experimental study

Front 33

treatment group

Back 33

the group that recieves special instructions in an experiment

Front 34

control group

Back 34

the normal group in the experiment

Front 35

hawthorn effect

Back 35

found that subjects who knew they were participating in an experiment changed their behavior in ways that might affect the result of the study

Front 36

confounding variable

Back 36

is one that influences the dependent or outcome variable but cannot be separated from the independent variable. Exp. students on an exercise program might also improve their diet unbeknownst to researchers and the diet then becomes a confounding variable.