STATS S301 IU

mutually exclusive

2 events cannot occur at the same time

sample space

all possible outcomes of an experiment

population

all subjects of interest

inferential statistics

allows us to make claims based on a sample

continuous data

any real number, infinite number of vales

percentiles

approximate percent of values in data set that are below value of interest

mean

average

pareto chart

bar charts that show frequency of the categories that cause quality control problems. show categories in a decreasing order

discrete data

can be counted, whole numbers, finite number of values

class

category

median

center point value, best choice when outliers present

CV=standard deviation (s) divided by mean(x with line), all times 100

coefficient variation formula

primary data

collected for your own use

nCx=n!/(n-x)!x!

combination formula

empirical probability

conducting an experiment to observe the frequency with which event occurs

secondary data

data collected by someone else that you're "borrowing", no control over how the data was collected

Information

data that are transformed into useful facts that can be used for a purpose like making a decision

relative frequency distributions

display the proportions of observations of each class relative to the total number of observations. Sum of distributions totals to 1

bar charts

good tool for displaying qualitative data that is organized in categories and can be arrange in horizontal or vertical fashion

stacked bar charts

group several values in a single column within the same category, shows totals

clustered bar charts

group several values side by side within the same category, shows comparison

range

highest value-lowest value, one measure of variability

contingency table

identify relationship between 2 or more variables

(AnB)

intersection, and

ratio data

interval, true point of zero

median>mean

left skew

discrete probability distribution

list of all possible outcomes for discrete random variable along with relative frequencies

expected monetary value

mean of discrete probability distribution in terms of money

sample correlation coefficient

measures both strength and direction of linear relationships between two variables

coefficient variation

measures standard deviation in terms of percentage of the mean

sample covariance

measures the direction of the linear relationship between two variable

outliers

much higher/lower values in the set, can mislead the mean value

<mean

negative z-score

ordinal data

nominal, can be ranked

z-score

number of standard deviations a particular value is from the mean of its distribution

dependent

occurrence of 1 event affects the other

independent

occurrence of 1 event has no impact on the other, P(A|B)=P(A)

value of interest= percentile divided by 100, all times the total number of data values

percentile formula

scatter plot

picture relationship between two quantitative variables

u

population mean symbol

N

population size symbol

o^2

population variance symbol

index point

position of median in data set

>mean

positive z-score

conditional probability

probability of an event, given another event has occurred

nominal data

qualitative, descriptive

interval data

quantitative

subjective probability

rely on experience and intuition

ogive

represented by red line above a pareto chart

symmetrical distribution

right side of histogram mirrors left side

mean>median

right skew

empirical rule

rule that says distribution that follows a bell curve means approx 68%, 95%, 99.7% of values fall between 1, 2, 3, standard deviations above and below the mean

rxy= sample covariance, divided by x standard deviation times y standard deviation

sample correlation coefficient formula

rxy

sample correlation coefficient symbol

sxy=sum of (x values minus x mean) and (y values minus y mean), all divided by n-1

sample covariance formula

sxy

sample covariance symbol

x with line over it

sample mean symbol

s^2

sample variance symbol

frequency distribution

shows the number of data observations in specific intervals

central tenancy

single value to describe the center point

more, less

smaller CV means ____ consistency within a set of values, larger CV means ____ consistency

variance

spread of data points around a set mean

square root of variance = ____

standard deviation

s

standard deviation symbol

sample

subset of population

descriptive statistics

summarize/display data

n

total number of data values in sample

cumulative relative frequency distribution

totals the proportion of observations that are less/equal to the class you're looking at

(AuB)

union, or

pie charts

used when comparing relative sizes of classes to each other

x

value in sample

mode

value that appears most often, best describes central tendency

cross-sectional data

values collected from multiple subjects during a SINGLE time period

time series data

values that correspond to specific measurements taken over a RANGE of periods

classical probability

we know number of possible outcomes

law of large numbers

when an experiment is conducted many times, empirical probability will be similar to classical probability

when the mean and median are roughly equal

when is data symmetrical

z=x value-mean, divided by standard deviation

z-score formula

=mean

z-score of 0

-3>x>3

z-score that shows outlier