Assumptions in the model : There should be a linear relationship between dependent (response) variable and independent (predictor) variable(s). If we fit a linear model to a non-linear, the regression model would not capture the trend mathematically, thus resulting in an inefficient model. Also this will result in erroneous prediction on out of sample data There should be no relationship between the…
INTRODUCTION : Logistic Regression is a regression analysis appropriate to use when dependent variable is dichotomous in nature i.e. binary (0 or 1). It is used when the variable is qualitative or probabilistic in nature. Example of usage for logistic regression – Predicting whether a claim for insurance will be raised or not based on factors like age of driver,…
2)There should be no relationship between the residuals (error) terms. Absence of this phenomenon is called Auto Correlation. The presence of correlation in error terms reduces model’s accuracy. This usually occurs in time series model. If the error terms are correlated, it underestimates the true standard error. 3) The independent variables should not be correlated. Absence of this phenomenon is…
