Data: Dependent and independent variables should be quantitative. Plots: Consider scatterplots, partial plots, histograms and normal probability plots. Also, consider 95-percent-confidence intervals for each regression coefficient, variance-covariance matrix, variance inflation factor, tolerance, Durbin-Watson test, distance measures (Mahalanobis, Cook and leverage values), DfBeta, DfFit, prediction intervals and case-wise diagnostic information. For each model: Consider regression coefficients, correlation matrix, part and partial correlations, multiple R, R2, adjusted R2, change in R2, standard error of the estimate, analysis-of-variance table, predicted values and residuals. 
For each variable: Consider the number of valid cases, mean and standard deviation. Here is how the Value of Dependent Random Variable Y using Simple Linear Regression Line calculation can be explained with given input values -> 100 = 40+(15*4).Assumptions to be considered for success with linear-regression analysis: How to calculate Value of Dependent Random Variable Y using Simple Linear Regression Line using this online calculator? To use this online calculator for Value of Dependent Random Variable Y using Simple Linear Regression Line, enter Regression Constant (B 0), Regression Coefficient (B) & Independent Random Variable X (X) and hit the calculate button. Dependent Random Variable Y is denoted by Y symbol. 
Value of Dependent Random Variable Y using Simple Linear Regression Line calculator uses Dependent Random Variable Y = Regression Constant+( Regression Coefficient* Independent Random Variable X) to calculate the Dependent Random Variable Y, Value of Dependent Random Variable Y using Simple Linear Regression Line formula is defined as the value of the dependent random variable Y corresponds to the given value of the independent random variable X determined using the simple linear regression line.
How to Calculate Value of Dependent Random Variable Y using Simple Linear Regression Line?
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