A&A SL: SL textbook: Bivariable Statistics

the highest and lowest data values

Interpolating

• when predicting that the x and y values would lie between the poles

• the accuracy of the prediction is determined by the fit within the linear model

Extrapolating

• when predicting that the x and y values would lie outside of the poles

• the accuracy of the prediction is determined by the linear fit and under the assumption that the trend will pass the poles

only worth drawing when there is a strong correlation between variables

How to draw:

when drawing a line by observation

will vary from person to person

sign indication of r

• when + r = positive correlation

• when - r = negative correlation

• when 0 = r = no correlation

size indication of r

• when r is close to + 1 or - 1 then there will be a strong correlation

• when r is close to 0 then there will be a weak correlation

AKA: explanatory variable/response variable

if a change in one variable explains the change in the other variable

• independent variable explains the dependent variable

a causal relationship can not be determined based on high occurrence alone

isolated points from the trend formed by the main data body

discard if it is a result of a recording error

keep if it is genuine data

used to determine how closely a trend is followed

correlation will be described as either:

• strong

• moderate

• weak

subjective way to determine data trends

when a trend exists

the points will form an approximately straight line

no correlation

a negative correlation

an increase in the independent variable

a decrease in the dependent variable

a positive correlation

an increase in the independent variable

an increase in the dependent variable

the relationship or association between two numerical values

one variable does not inherently cause the other

shows the relationship between two numerical values

the independent variable is on the horizontal axis

the dependent variable is on the vertical axis