Introduction The least squares method should be used for deriving the best fit line based on observed data of certain variables. The outcome of this method is to reduce the sum of squares of errors which can be found in an equation (Björck, 1996). This method applies to the regression analysis. In this paper, regression analysis is performed to predict the relationship between the time series (independent variable) and the time spent watching television each day measured in minutes (dependent variable). Regression #1: Original Data First set of data which has 10 observations has been used for carrying out the regression analysis using the model. The graph presented below shows the best fit line for the data selected over 10 observations. The vertical distance between the actual values and best fit line which is drawn upon the predicted values can also be observed from this graph. The graph indicates a positive correlation between two variables selected for the regression analysis as the slope of the best fit line is positive. Figure 1: Best Fit Line #1. This is further supported by the predicted values obtained from the regression analysis. It could be indicated that the predicted values are increasing over the time series. On the basis of this assessment, it could also be indicated that there is not a perfect linear relationship between time series and duration of time in minutes spent by individuals watching television. x-Value  y-Value  Predicted Value 1        72       111.909         2        90       118.285         3        130      124.661         4        180      131.036         5        75       137.412         6        225      143.788         7        300      150.164         8        84       156.539         9        100      162.915         10       150      169.291          Table #1: Predicted Values of y. Regression Equation The regression equation derived from the analysis is provided in the following: y = 6.37576 x + 105.533 Where y = Predicted mean value of dependent variable i.e. time in minutes spent watching television x = Value of independent variable i.e. time series The regression equation indicates the y-intercept, B 0 has a value of 105.533 whereas the coefficient of slope, B 1 has a value of 6.37576. This affirms the positive relationship between time series and the amount of minutes spent watching television. The mean value of the dependent variable is predicted to increase over the period of time. The higher the x value the higher it can be estimated that the value of y will be. Regression Coefficient From the regression analysis the regression coefficient has been obtained as r = 0.257174 which implies that the regression model has been able to predict only 25.72% of the total variations observed in the dataset and the remaining 74.28% is residual as determined by the sum of square. The ability of the regression model could therefore be considered as weak and on the basis of this it could be suggested that the dataset must be extended to include additional number of observations or the model must include other variables which could have implications for the human behavior of watching the television on a day to day basis. Regression #2: Extended Data The second dataset that has been used for performing the regression anal