Purpose

Examines the relationship between one independent variables with one dependent continuous variable  Calculates the likelihood of event with binary outcome (ie, yes or no)  It is an extension of simple linear regression and examines the relationship between one or more independent and dependent variables simultaneously 
Nature of dependent and independent variables

Dependent variable should be continuous Independent variables could be at any level of measurement

Dependent variable should be categorial Independent variables could be at any level of measurement

Dependent variables should be continuous Independent variables could be at any level of measurement

Assumptions

Assumes that the distribution of dependent data is normal or Gaussian Requires a linear relationship between dependent and independent variables

Assumes that the distribution of dependent data is binomial. It does not require a linear relationship between dependent and independent variables The independent variables should not be correlated

Assumes that the distribution of dependent data is normal or Gaussian Requires a linear relationship between dependent and independent variables The independent variables should not be correlated. Higher correlation among the independent variables may affect the relationship between independent and dependent variable

Nature of curve

It uses a straight line  It uses an Scurve  It uses a straight line 
Example

Examining the relationship between hours of training and levels of patient selfcare and predict how long training should last for every unit increase in selfcare levels  Estimating the likelihood of development of pressure ulcers (dichotomous outcome: yes or no) due to longer hospital stay, number of times of positioning, BMI (Body Mass Index) and age  Examining the relationship between hours of training and patient selfcare levels while controlling for other variables (eg, family support, duration of disease) that may affect the relationship 