q sci 381 exam

statistics

connecting numerical data to understand probabilites

descriptive statistics

organizing, summarizing, and displaying data

inferential statistics

drawing conclusions about a population from data

data

observations, counts, measurements, or responses

population

number defined by the scale

parameter

the rate of the population

sample

number that is a part of a population

representative sample

sample with characteristics of the population

variable

characteristics of population such as categories

nominal measurement

qualitative measurement with categorical methods

ordinal measurment

qualitative or quantitive measurement with category and a meaningful order to them

interval measurment

quantitive measurement with category, order, and meaningful differences

ratio measurement

quantitive measurement with all the aspects as well as true 0 starting point.

simple random sampling

selecting at random

stratified random sampling

placed into different characteristic groups, people are picked from these groups

cluster random sampling

population naturally in subgroups with simular characteristcs but not put into groups answering the research question

systematic sampling

random start point then selecting every 1/x thing after

multistage sampling

when people are randomly selected from each roup

scatterplot graph

case by case x and y axis

boxplot graphs

z-score

number of standard deviations that falls above or below the mean, to see if how far an observation is from the mean using standard deviation.

A positive score means the value is above average, a negative score is below average, and 0 is the average

empirical rule

68% of the data in 1 SD of the mean

95% of the data in 2 SD of the mean

99.7 of the data in 3 SD of the mean

histogram

shows frequency

stem and leaf plot

permutation

when the order of arragnements of objects matters

distinguishable permutations

ordered arrangements matter but there are different things

comination permentution

when the order does matter so n choose r

random variable

numerical value from random process

empirical distribution

based on observed data

theoretical distribution

based on mathematical formula

binomial distribution

probability of getting success after a certain number of trials BUT trials are independent, the probability of success is consistent, trial outcome is win or loss, and the number of trials is fixed

geometric distribution

probability of first win on trial x BUT trial must be independent, repeated until won, the probability the same, and the x is independent trials until first win

poisson distribution

probability of x events happening in fixed time or space BUT count the events inan interval, the average rate of the event is constant, and the event outcome is independent of others

normal distribution

a symmetric, unimodal, bell shaped cuve

mutually exclusive

when two or more events can’t happen at the same time

standard deviation

A low SD indicates data points are clustered closely around the mean (high consistency), while a high SD indicates the data is spread over a wider range (higher variability)

coefficient of variation

A CV of 10% means the standard deviation is 10% of the mean.

Normal distribution

It is always symmetric and bell-shaped 

The mean, median and mode are all equal, and have value μ 

The distribution is defined across all numeric values from –infinity to infinity, and approaches 0, but never reaches 0, as x gets further from the mean 

The area under the curve from –infinity to infinity is equal to one to satisfy that the summed probability across all outcomes equals one

the mean is 0 and the standard deviation is 1

z-score interpretation

how far an observation was from the mean in units of standard deviation

ex. An observation with a z-score of 2 is always going to be 2 standard deviations above the mean