# Inferential Statistics 101 — part 8

• We can also tell how the Celsius and the Fahrenheit are related.
• What performance measures it uses to call a particular line as the best line?
• The independent variables of a population regression function should be additive in nature.
• Realizations of X and Y (samples) from the process should be random. Usually, time series data doesn’t satisfy this assumption since the value which the variable takes at any time instance t, depends on its value at time instance t-1.
• The expected value of error is zero.
• If a relation exists between anyone of the independent variable and the error.
• Measurement error in the independent variable.
• Existence of reverse causality i.e. closed loop relation exist between dependent and anyone of the independent variable.
• Measurement of error in the independent variable: Realization of an independent variable contains some errors due to some reasons like measurement error etc. The rule mentioned for omitted variable applies for measurement error too. i.e., if measurement error is correlated with any one of the independent variables, the estimator will be biased. If not, it doesn’t produce serious consequences.
• Homoscedasticity
1. It will be difficult to find out precise effect of each predictor.

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