The least-squares method for calculating slopes and intercepts of data points can give wrong results if the data doesn't meet certain conditions. Errors can happen when the x-axis measurements are imprecise or when there are outliers in the data. The Deming method is the most useful for estimating the slope of a regression line when x-axis measurements are imprecise. If the standard deviation of a single x value is more than 0.2 times the standard deviation of the x-data set, there can be significant errors in slope estimation. To avoid errors from outliers, data points with a vertical distance from the regression line more than four times the standard error should be removed.