![The graph of the simple linear regression equation](https://knopkazmeya.com/26.png)
![the graph of the simple linear regression equation the graph of the simple linear regression equation](https://reader020.documents.pub/reader020/slide/20190819/56649d0c5503460f949e0f08/document-5.png)
This implies that the ability of regression can be improved if the size of the dataset is increased. The regression coefficient has improved from 0.257174 to 0.311247 by adding to the number of observations. This suggests that the regression model is still able to predict small proportion of the total variations observed in the dataset and there are still 68.88% of variations still unexplained by the regression model. The regression coefficient that has been obtained from performing the regression analysis is r = 0.311247 which implies that the revised regression model is able to predict 31.12% of the total variations observed in 15 entries into the dataset. The relationship between times and the amount of time spent watching television remains positive which implies that the mean value of the dependent variable increases over the period of time. The value of coefficient of slope has reduced as the number of observations is increased. Also from the table an outlier could be indicated at observation number 7.įrom this equation, it can be indicated that the coefficient of intercept, B 0 is 111.133 and the coefficient of slope, B 1 has a value of 4.825.
![the graph of the simple linear regression equation the graph of the simple linear regression equation](http://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1526911465/1_afqiyx.png)
The table provided below indicates the predicted values using least square method.
The graph of the simple linear regression equation series#
The relationship between two variables included in the regression analysis that the number of minutes spent watching television and time series still appears as positive which implies that the time spent into front of television increases over a period of time. The best fit line overlapping the actual values has improved by increasing the number of observations. From analysis the graph it could be suggested that by increasing the number of observation the difference between the predicted values and actual values included in the dataset has improved. Using the same regression model available the following graph has been obtained which indicates the best fit line. It is expected on the basis of the results of the previously performed regression that by increasing the number of observations in a dataset the ability of the regression model to predict the relationship between the dependent and independent variables is likely to improve. The second dataset that has been used for performing the regression analysis comprises of additional observations over an extended time series. 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.
![the graph of the simple linear regression equation the graph of the simple linear regression equation](https://www.statext.com/practipic/SimpleRegressionLinear03b.jpg)
Regression Coefficientįrom 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 higher the x value the higher it can be estimated that the value of y will be. The mean value of the dependent variable is predicted to increase over the period of time. This affirms the positive relationship between time series and the amount of minutes spent watching television. 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. time in minutes spent watching television Where y = Predicted mean value of dependent variable i.e.
![The graph of the simple linear regression equation](https://knopkazmeya.com/26.png)