eng Science and Education Publishing American Journal of Applied Mathematics and Statistics 2328-7292 2019-05-04 7 3 105 111 10.12691/ajams-7-3-4 AJAMS2019734 article Onyegbuchulem B.O. bokey@imopoly.net 1 Nwakuya M.T 2 Nwabueze J.C 3 Otu Archibong Otu 4 Department of Maths/Statistics, Imo State Polytechnic Umuagwo, Nigeria Department of Maths/Statistics, University of Port Harcourt, River State, Nigeria Department of Statistics, Federal University of Agriculture Umudike, Nigeria Department of Research and Statistics, Central Bank of Nigeria, Owerri Quantile Regression (QR) performed better than Ordinary Least Square (OLS) when the Data is skewed. Its best result can be achieved when the Data is transformed. Quantreg package of R software was used to illustrate the various power transformation fitness for quantile regression model. The analysis shows that the best result was obtained from the square root of y transformation with an average error term of 0.9539, -0.0494, 0.0238, -0.5309 and -0.7544 for 10th, 25th, 50th, 75th and 90th quantile respectively. From the results obtained, it shows that model transformation can greatly improve the result of quantile regression model. http://pubs.sciepub.com/ajams/7/3/4/ajams-7-3-4.pdf Quantile Regression skewed distribution power transformation and model selection