(a) Comment on the suitability of this data set for simple linear regression modelling. (b) Explain the difference between an outlier and an influential observation The data is loaded into R Studio and assigned to two vectors x and y. (c) Write R code that constructs a simple linear regression model with X as the explanatory variable and Y as the response, calculates and plots the leverage of each observation and then formally tests whether there are any influential observations in this data with reference to an appropriate F test assigning the value TRUE in R if there is one or more influential observations and FALSE if there are no influential observations. (d) What should the modeler do if the code in (c) above returns TRUE?
(a) Comment on the suitability of this data set for simple linear regression modelling. (b) Explain the difference between an outlier and an influential observation The data is loaded into R Studio and assigned to two vectors x and y. (c) Write R code that constructs a simple linear regression model with X as the explanatory variable and Y as the response, calculates and plots the leverage of each observation and then formally tests whether there are any influential observations in this data with reference to an appropriate F test assigning the value TRUE in R if there is one or more influential observations and FALSE if there are no influential observations. (d) What should the modeler do if the code in (c) above returns TRUE?
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Transcribed Image Text:Question 2
Some data is collected with 12 observations of two variables X and Y and the following
scatter plot of the data is produced.
Yi
8
D
0
00088
1
Plot of Y versus X
2
Xi
3
(a) Comment on the suitability of this data set for simple linear regression modelling.
(b) Explain the difference between an outlier and an influential observation
The data is loaded into R Studio and assigned to two vectors x and y.
(c) Write R code that constructs a simple linear regression model with X as the explanatory
variable and Y as the response, calculates and plots the leverage of each observation
and then formally tests whether there are any influential observations in this data with
reference to an appropriate F test assigning the value TRUE in R if there is one or more
influential observations and FALSE if there are no influential observations.
(d) What should the modeler do if the code in (c) above returns TRUE?
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