he OLR model established for the olive oil content data. The open circles or ‘In

nd for the observations within the confidence bands (90% confidence level)

rosses or ‘Out bands’ stand for the observations beyond the confidence bands

dence level).

dition to a regression model which only employs one independent

many biological applications require more than one independent

In this case, a univariate regression analysis model is extended to

ariate regression analysis model. Suppose y is a dependent

ݔଵ and ݔଶ are two independent variables, ߚ଴ is an intersect, ߚଵ

e two regression coefficients for two independent variables, ߝ is

erm of a multivariate regression analysis model. A multivariate

n analysis model is defined below,

ݕൌߚ଴൅ߚଵൈݔଵ൅ߚଶൈݔଶ൅ߝ



ose all six independent variables of the olive oil content data were

nalyse how they contribute to olive oil content. An OLR model

tablished using the following code,

model=lm(Oil.content ~ .,data=x)

e 4.14 shows the visualised multivariate OLR model for

g all independent variables of this olive oil content data. The R²

s 0.3434. The F-statistic p value was 6.57e−5. Among these

ent variables, one had significant association with the dependent

uppose the critical p value was 0.05. It was the stone width. Note

F-statistic p value was calculated by the pf function using the F-