Although the number of under-five deaths worldwide has declined from 12.7 million in 1990 to 5.9 million in 2015, many children are still dying in the poor and unsanitary regions of Africa. This paper had two aims: to study the associations between the response variable, under-five mortality rate and the predictors, which are selected explanatory factors of child mortality; and to assess the full multiple linear model by using Box-Cox transformation for the skewed response variable. The associations were studied based on the results of the multiple linear regression both before and after the Box-Cox transformation. Numerous sources such as the WHO have confirmed the associations of the transformed model. To justify the adequacy of the Box-Cox transformation, histograms of the residuals and the normal Q-Q plots of both models were plotted. The transformed model satisfied not only the normality of residuals assumptions of the linear regression but also other assumptions of the linear regression including the linear relationship between dependent and independent variables and homoscedasticity. The Box-Cox transformed model also had a lower AIC than the non-transformed model. Thus, Box-Cox transformation can be a tool for correcting the non-normality of residuals and fulfill other linear regression assumptions.