Let's examine the output from this regression analysis. As with the simple regression, we look to the p-value of the F-test to see if the overall model is significant. With a p-value of zero to three decimal places, the model is statistically significant.
http://www.ats.ucla.edu/stat/spss/webbooks/reg/chapter1/spssreg1.htm
The values of b (b 1 and b 2) are sometimes called  regression coefficients  and sometimes called  regression weights .. These two terms are synonymous. The multiple correlation (R) is equal to the correlation between the predicted scores and the actual scores.
http://onlinestatbook.com/2/regression/multiple_regression.html
Simple linear regression is similar to correlation in that the purpose is to measure to what extent there is a linear relationship between two variables. The major difference between the two is that correlation makes no distinction between independent and dependent variables while linear regression does. In particular, the purpose of linear regression is to predict the value of the dependent variable based upon the values of one or more independent variables.
http://www.statisticallysignificantconsulting.com/RegressionAnalysis.htm
A regression coefficient and the variance explained uniquely by a variable both reflect the relationship between a variable and the criterion independent of the other variables. If the variance explained uniquely by a variable is not zero, then the regression coefficient cannot be zero.
http://onlinestatbook.com/2/regression/multiple_regression.html
So, using Simple Regression, you can estimate the value of your variable. Logistic Regression on the other hand is used to ascertain the probability of an event. And this event is captured in binary format, i.e. 0 or 1. Example-I want to ascertain if a customer will buy my product or not.
http://stats.stackexchange.com/questions/29325/what-is-the-difference-between-linear-regression-and-logistic-regression
In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. In both cases there is only a single dependent variable.
http://www.statisticallysignificantconsulting.com/RegressionAnalysis.htm
Interpretation of Regression Coefficients. A regression coefficient in multiple regression is the slope of the linear relationship between the criterion variable and the part of a predictor variable that is independent of all other predictor variables.
http://onlinestatbook.com/2/regression/multiple_regression.html
Linear regression is a common Statistical Data Analysis technique. It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables. There are two types of linear regression, simple linear regression and multiple linear regression.
http://www.statisticallysignificantconsulting.com/RegressionAnalysis.htm
Again, as in the case of simple regression, Proportion Explained = SSY'/SSY. In simple regression, the proportion of variance explained is equal to r 2; in multiple regression, the proportion of variance explained is equal to R 2.
http://onlinestatbook.com/2/regression/multiple_regression.html
3. Re: Linear Regression is used to establish a relationship between Dependent and Indipendent variables - this is also true about logistic regression-it's just that the dependent variable is binary. – Macro May 29 '12 at 0:46. add a comment |. up vote 2 down vote.
http://stats.stackexchange.com/questions/29325/what-is-the-difference-between-linear-regression-and-logistic-regression
difference between simple regression and multiple regression
In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable.