3. Established Correlation: For regression to work properly, strong correlations must exist between inputs and outputs so that reliable predictions can be made from the data collected. Without established correlations, predictions might not always match reality due to the fact that randomness has been introduced into the equation by other factors not considered in the model itself.
4. Sufficient Data Set: Regression requires a large enough dataset with sufficient variability among its features in order for meaningful conclusions to be drawn from it; otherwise too much uncertainty may remain when attempting to make predictions or draw generalizations about future events or conditions based on past observations alone without sufficient data points included in your sample size available for reference purposes only
5 Regression Assumptions: Finally, certain assumptions must also hold true before relying upon regression results; namely that errors are normally distributed within your dataset along with homoscedasticity (equal variance) among residuals across different levels of input-variables being tested against one another as well etc.. Otherwise skewed results could occur which would render your entire predictive model inaccurate or useless at best depending upon how badly distorted those particular assumptions had become over time etc..

An example of this condition where regression could prove useful would be predicting sales figures over time based on previous sales patterns observed during any given period of months/years previously measured/recorded accordingly under similar market conditions where each variable was measurable numerically i.e., total weekly/monthly sales volume + average unit cost per item sold + total number of customers served during same period = estimated gross revenues generated during observation quarter respectively etc.