Stats Exam 1

Front 1

Simple random sample (SRS)

Back 1

size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected.

Front 2

probability sample

Back 2

a sample chosen by chance. We must know what samples are possible and what chance, or probability, each possible sample has.

Front 3

confounding

Back 3

Two variables are confounded when their effects on a response variable cannot be distinguished from each other.

Front 4

z-score

Back 4

the standardized value of x

Front 5

interquartile range (IQR)

Back 5

the distance between the first and third quartiles

Front 6

mean

Back 6

Arithmetic average of the observations in a data set.

Front 7

median

Back 7

Midpoint of the values in a data set.

Front 8

Q1

Back 8

the median of the observations whose position in the ordered list is to the left of the location of the overall median.

Front 9

Q3

Back 9

the median of the observations whose position in the ordered list is to the right of the location of the overall median.

Front 10

standard deviation

Back 10

Measure of the spread about the mean of a distribution. It is an average of the squares of the deviations of the observations from their mean, also equal to the square root of the variance.

Front 11

range

Back 11

all numbers in a set of given numbers

Front 12

distribution

Back 12

The values and the frequency of values taken by a variable

Front 13

five number summary

Back 13

Description of a distribution consisting of the smallest observation, the first quartile, the median, the third quartile, and the largest observation, written in order from smallest to largest.

Front 14

resistant measure

Back 14

cannot resist the influence of extreme observations

Front 15

statistically significant

Back 15

An observed effect so large that it would rarely occur by chance is called

Front 16

explanatory variable

Back 16

may explain or influence changes in a response variable

Front 17

response variable

Back 17

measures an outcome of a study

Front 18

population

Back 18

The entire group of individuals that we want information about.

Front 19

sample

Back 19

A part of the population that we actually examine in order to gather information

Front 20

individual (or basic unit)

Back 20

Object described by a set of data. Individuals may be people, but they may also be animals or things

Front 21

stratified sample

Back 21

A sample composed of separate SRSs chosen for different strata of a population, groups of individuals that are similar in some way that is important to the response

Front 22

multistage sample

Back 22

a form of stratified sampling where you take large groups and break them down into smaller groups before SRSs are chosen

Front 23

convenience sample

Back 23

A sample selected by taking the members of the population that are easiest to reach. This often produces unrepresentative data

Front 24

voluntary response sample

Back 24

consists of people who choose themselves by responding to a broad appeal. Voluntary response samples are biased because people with strong opinions are most likely to respond

Front 25

completely random

Back 25

all the subjects are allocated at random among all the treatments.

Front 26

randomized block design

Back 26

the random assignment of individuals to treatments is carried out separately within each block.

Front 27

matched pairs

Back 27

A type of experiment in which each subject receives both treatments in a random order, or the subjects are matched in pairs as closely as possible, and one subject in each pair receives each treatment

Front 28

lurking variable

Back 28

A variable that has an important effect on the relationship among the variables in a study but is not included among the variables studied.

Front 29

response bias

Back 29

individuals in the sample will sometimes forget or lie

Front 30

non-response bias

Back 30

occurs when an individual chosen for the sample can’t be contacted or refuses to participate

Front 31

question wording bias

Back 31

Confusing or leading questions can introduce strong bias, and even minor changes in wording can change a survey’s outcome

Front 32

interviewer bias

Back 32

Careful training of interviewers and careful supervision to avoid variation among the interviewers can reduce response bias

Front 33

undercoverage bias

Back 33

occurs when some groups in the population are left out of the process of choosing the sample

Front 34

placebo effect

Back 34

The response to a dummy treatment

Front 35

lack of realism

Back 35

the subjects or treatments or setting of an experiment may not realistically duplicate the conditions we really want to study

Front 36

double blind

Back 36

An experiment in which neither the subjects nor the people who work with them know which treatment each subject is receiving.

Front 37

observational study

Back 37

A study to observe individuals and measure variables of interest, but do not attempt to influence the responses

Front 38

experiment

Back 38

deliberately imposes some treatment on individuals in order to observe their responses. The purpose of an experiment is to study whether the treatment causes a change in the response.

Front 39

quantitative

Back 39

A variable that takes numerical values that measure some characteristic of each individual, like height in centimeters or salary in dollars per year. Uses histograms and stemplots. For small data sets, a stemplot is quicker to make and presents more detailed information.

Front 40

categorical

Back 40

A variable that places each individual into a category. Uses bar graphs and pie charts. Use a pie chart only when you want to emphasize each category’s relation to the whole

Front 41

steps of statistical problem solving

Back 41

STATE: What is the practical question, in the context of the real-world setting?
FORMULATE: What specific statistical operations does this problem call for?
SOLVE: Make the graphs and carry out the calculations needed for this problem.
CONCLUDE: Give your practical conclusion in the setting of the real-world problem.

Front 42

density curves

Back 42

An idealized description of the overall pattern of a distribution that smooths out the irregularities in the actual data. A density curve has total area 1 underneath it.

Front 43

normal distributions

Back 43

Distribution described by a special family of bell-shaped, symmetric density curves, called Normal curves. The mean μ and standard deviation σ completely specify a Normal distribution N(μ, σ).