# Statistics 2 Flashcards

correlation

the strength of association between two quantitative variables

correlation

describes the extend to which one variable relies on the other

correlation coefficient

persons QUANTIFIES the strength of the LINEAR association between two quantitative variable

pearsons coefficient ranges from

-1 to 1

if a graph has any curve in it

NOT LINEAR - CANNOT CALCITE COEFFICIENT

when data is linear

when there is variation around and on the line

linear regression

used to describe the linear relationship between quantitative outcome and one or more predictor variables

linear regression can be used to

estimate mean scores on the outcome for subject with specific profile of score not he predictors

error in predictions

- simple relationship between weight and height
- regression line fitted to data, actual points may not lie on the line- vertical differences are errors – residuals
- each individuals data point will not lie on the line

when using linear regression

we must look at errors- residuals

residuals must be

normally distributed

- with constant variance]

if residuals have constant variance

size of error is unrelated to vale of predictor variable

if regression is 1.4

with each unit increase in the dependent variable, the independent variable increase by 1.4

confounding factors

factors which destroy relationships- meaning relationships are not causative

–> sometimes looking at simple correlation will not tell you the whole story

what can help tell the whole story

causal diagrams

-which show all factors in the system