Learning Goal: I’m working on a r exercise and need an explanation and answer to help me learn.Suppose I have a long-format data-frame df, and clean it with na.omit, then perform correlation in it via cor, (i.e. cor(na.omit(df))). What is the equivalent code in R? Cor (df, use=”p”)

Cor(df, use=”complete.obs”)

Cor (df, na.omit=TRUE)

Cor (df, na.rm=TRUE)

What is an acceptable way to calculate a linear regression of y as a function of x in R? glm(x~y, family=”gaussain”)

glm(x~y, family=”linear”)

glm(x~y, family=”simple”)

None of the Above

What is true about the Box-Cox regression optimization routine? Box-Cox will correct for non-linearity, but does not guarantee homoscedascity

Box-Cox will correct for heteroscedascity, but does not guarantee linearity

Box-Cox will guarantee a linear, homoscedastic scatter when done

Box-Cox does not guarantee linearity, nor homoscedasticity when done

What is the impact of two collinear variables in a multiple regression? They will mutually redundant and may corrupt the explanatory power/variance

They will fail the Breushpagan test for homoscedascity

They will fail the shapiro-Wilk Test for normality

All three of the above are true

When implementing a power transformation (PT), you could conceivably transform both you x- and y data. But, what is the caution against this (i.e. why should you transform just one variable and not both)?PTs change the units, too, so interpretation is made more complex

PTs are hard to implement: multiple PTs could prolong the computation by hours

Every time you PT, you surrender some Type-1 error, so do so sparingly

The Best practice is: PT the first variable; Log-transform the second

Requirements: 1-2 sentence explanation

# Learning Goal: I’m working on a r exercise and need an explanation and answer to

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