Applied Regression Modeling

Consider the following data:

X	2.3	1.8	2.2	1.2	1.6	2.6	1.4	1.1	1.2	1.6
Y	0.7	1.0	0.7	0.2	0.7	1.2	0.7	0.1	0.4	0.5

Use statistical software to fit a simple linear regression model with response variable, Y, and predictor variable, X (call this model 1). Then fit another simple linear regression model with response variable, Y, and predictor variable, log_e(X) (call this model 2). Use the fitted models to answer the following questions.

Model 2 with predictor variable, log_e(X), provides a more useful description of the association between Y and X than that of model 1 with predictor variable, X, because:

Model 2 provides a more useful description of the association between Y and X than that of model 1 because:

Model 2 provides a more useful description of the association between Y and X than that of model 1 because:

True or false? Model 2 is not a linear regression model because the logarithmic transformation is non-linear.

True or false? The natural logarithm transformation tends to "spread out" higher values and "pull in" lower values.