Question

Jorge was studying average life expectancy in Europe and how it varies across different countries. He wanted to discover what variables were good predictors of life expectancy and identified 3 that he believed could possibly be good predictors:

employment rate,
access to healthcare, and
number of car accidents per year.
He conducted a multiple linear regression analysis using these variables.
When looking over the results of this analysis, he saw
that the scatterplot did not show a linear pattern between each of the explanatory variables and the response variable.
that there was not a funnel shape to the residual vs. predicted plot. that the histogram of the normal quantile plot was fairly normal.
Based on all of these findings,
he can say that the linearity condition _________
the equal variance condition ________
the normality condition _____________
the 10% condition ____________
and the success failure condition __________
Options for each:
A) is not met
B) does not need to be checked for multiple linear regression
C) is met

Answer 1

• Is not met ( A )
• Is not met ( A )
• Does not need to be checked for multiple linear regression ( B )
• Does not need to be checked for multiple linear regression ( B )

Explanation:

Linearity condition is : NOT MET

The equal variance condition is : NOT MET

The 10% condition : Does not need to be checked for multiple linear regression

The success failure condition : Does not need to be checked for multiple linear regression