ABSTRACT
Observe how the spread of the residuals increases as the value of explanatory variable increases. This is a situation known as heteroskedasticity. Any inference for regression based on a model yielding such a pattern in the residuals would not be valid. The conditions for inference in regression problems are a key part of regression analysis that are of vital importance to the processes of constructing confidence intervals and conducting hypothesis tests. For inference for regression, there are four conditions that need to be met. This can serve as a nice reminder of what to check for whenever learner perform linear regression. Using bootstrap distribution, we’ll construct the 95% confidence interval using the percentile method and the standard error method as well. It is important to note in this case that the bootstrapping with replacement is done row-by-row.
