(i) First use the data set 401KSUBS.RAW, keeping only observations with fsize = 1. Find the skewness measure for inc. Do the same for log(mc). Which variable has more skewness and therefore seems less likely to be normally distributed?(ii) Next use BWGHT2.RAW. Find the skewness measures for bwght and log(bwght). What do you conclude?(iii) Evaluate the following statement: The logarithmic
(i) First use the data set 401KSUBS.RAW, keeping only observations with fsize = 1. Find the skewness measure for inc. Do the same for log(mc). Which variable has more skewness and therefore seems less likely to be normally distributed?
(ii) Next use BWGHT2.RAW. Find the skewness measures for bwght and log(bwght). What do you conclude?
(iii) Evaluate the following statement: "The logarithmic transformation always makes a positive variable look more normally distributed."
(iv) If we are interested in the normality assumption in the context of regression, should we be evaluating the unconditional distributions of y and Iog(y)? Explain.
(ii) Next use BWGHT2.RAW. Find the skewness measures for bwght and log(bwght). What do you conclude?
(iii) Evaluate the following statement: "The logarithmic transformation always makes a positive variable look more normally distributed."
(iv) If we are interested in the normality assumption in the context of regression, should we be evaluating the unconditional distributions of y and Iog(y)? Explain.
This problem has been solved!
Do you need an answer to a question different from the above? Ask your question!
Answer
(i) The measure of skewness for inc is about 1.86. When we use log( inc ), the skewness measure is a…View the full answer
Related Book For 
Introductory Econometrics A Modern Approach
4th edition
Authors: Jeffrey M. Wooldridge
ISBN: 978-0324660548