Stats Ch 1-5 Midterm

statistics

the science of​ collecting, organizing,​ summarizing, and analyzing information to draw conclusions or answer questions. It is also about providing a measure of confidence in any conclusions.

a statistic

a numerical summary of a sample.

descriptive statistics

the organizing and summarizing of data through​ tables, graphs, and numerical summaries.

inferential statistics

uses methods that take results from a sample and extends them to the​ population, and measures the reliability of the result.

an individual

is a person or object that is a member of the group being studied.

parameter

is a numerical summary of a population.

a population

is the entire group of individuals to be studied

qualitative variable

attribute characteristic

quantitative variable

numerical measure

discrete variable

countable finite number

continuous variable

not countable, infinite number

nominal level of measurement

the values of the variable name, label, or categorize. In addition, the naming scheme does not allow for the

values of the variable to be arranged in a ranked or specific order.

ordinal level of measure

has the properties of the nominal level of measurement, the values of the variable can be arranged in a ranked or specific order

interval level of measurement

has the properties of the ordinal level of measurement and the differences in the values of the variable have meaning. addition and subtraction can be performed

ratio level of measurement

has the properties of the interval level of measurement and the ratios of the values of the variable have meaning. multiplication and division can be performed

observational study

measure the value of the response variable without attempting to influence the value of either the response or explanatory variables. observes, does not influence outcome

designed experiment

research assigns individuals in a study to groups, intentionally manipulates the value of explanatory variable, records the value of the response variable for each individual

confounding

study when the effects of two or more explanatory variables are not separated

lurking variable

an explanatory variable was not considered in a study, but affects the value of the response variable in the study

confounding variable

explanatory variable that was considered in a study whose effect cannot be distinguished from a second explanatory variable in the study

cross sectional studies

observational studies that collect info about individuals at a short period of time

case control sstudies

studies require looking at existing records

cohort studies

study where data is collected over time in groups

census

a list of individuals in a population along with characteristics of each individual

random sampling

process of using chance to select individuals from a population

sample without relpacement

individual who is selected is removed from the population and cannot be chosen again

sample with replacement

a selected individual is placed back into the population and could be chosen a second time.

stratified sample

separating into strata and obtaining a simple random sample. individuals must be homogeneous

systemic sample

selecting every kth individual from population. first individual selected is a number between 1 and k

cluster sample

sample selecting all individuals within a randomly selected collection or group

convenience sample

a sample where individuals are obtained, not random

sampling bias

technique to pick individuals favors one part of population over another

nonresponse bias

individuals who don’t respond to survey have diff opinions than those who do

response bias

answers do not reflect true feelings

design

describe the overall plan in conducting the experiment

completely randomized design

each experimental unit is randomly assigned to a treatment

matched-pair design

design where experimental units are paired up

blocking

grouping homogenous experimental units and randomly assigning them a treatment

frequency distribution

lists each category of data and number of occurrences for each category of data

relative frequency

frequency/sum of all frequencies

histogram

drawing rectangles for each class of data

class midpoint

sum of consecutive lower class limits divided by two

treatment

any combination of the values of the factors (explanatory variables)

response variable

the quantitative or qualitative variable for which the experimenter wishes to determine how its value is affeccted by the explanatory variable

factor

a variable whose effect on the response variable is to be assessed by the experimenter

placebo

an innocuous medication, such as sugar tablet, that looks, taste, and smells iike the experimental medication

blinding

nondisclosure of the treatment an experimental unit is recieving

response variable

Tthe variable whose value can be explained by the value of the explanatory or predictor variable.