Data analysis exam 2

two categorical values

two way contingency tables, grouped bar graphs

joint distribution

refers to the distribution of two variables together

marginal distribution

refers to the overall distribution of one variable without conditioning on the other variable

conditional distribution

refers to the distribution of a variable given the specific level of the other

one categorical and one quantitative variable

descriptive statistics and boxplots

two quantitative variables

scatterplots

covariance

the average product of the deviation scores for X and Y

sign of covariance

determined by which group of deviation products—positive or negative—has the stronger overall contribution to S_xy

absolute size of covariance

depends on how consistently and strongly the deviations of Xi and Yi vary together

correlation

refers to the covariance of the two standardized variables

descriptive statistics

describe or summarize data, use graphical or numerical methods

inferential statistics

infer the characteristics of the entire group of interest, estimate the parameters of a population

probability

a mathematical function of an event in a sample space, quantifying the likelihood of that event occurring in accordance with specific axiomatic rules

random experiment

a well-defined procedure or action that randomly produces an (observable) outcome in the sample space

sample space

the set of all possible outcomes from a random experiment

event

a subset of the sample space or a set of outcomes in the sample space

mathematical function of an event

function that assigns a numerical value to each event

complement of an event

an event where the considered event does not occur

union of events

a composite event where at least one of the considered events occurs

intersection of events

a composite event where all the considered events occur simultaneously

mutually exclusive events

the events that can never occur simultaneously

non-mutually exclusive events

the events that can occur simultaneously

classical approach

assigns probabilities by logic, assuming that outcomes are equally likely

relative frequency approach

assign probabilities on the basis of data

subjective approach

assign probabilities on the basis of personal belief or prior experiences

simple probability

the probability that event A occurs in a single random experiment

joint probability

the probability that events A and B occur together in a single random experiment

joint distribution

refers to the distribution of two variables together, indicates how many cases fall into each combination of the two variables

marginal probability

the probability that event A occurs without any conditions related to other events

marginal distribution

provides the totals for one of the variables, ignoring the other

conditional probability

the probability that event A occurs, given that event B occurs

conditional distribution

refers to the distribution of a variable given the specific level of the other

random variable

a real-valued function of an outcome of a random experiment to a numerical value

discrete random variable

if the variable can randomly take on a finite or countable number of possible values

continuous random variable

if the variable can randomly take on any real value (within an interval)

probability density function

the smooth curve of a relative frequency histogram as the sample size reaches infinity

normal distribution

characterized by standard deviation and mean, symmetric bell-shaped

standard normal distribution

characterized by a standard deviation of 1 and a mean of 0