OLS estimation Assignment Example | Topics and Well Written Essays - 2500 words

OLS estimation - Assignment ExampleThe respective means of these variables atomic number 18 82.38, 80.77 and 44.66 and signifi fecal mattert vari qualification among the values taken by these variables is observed, implying a possibility that plays in attention can potentially cause variations in tag. Other variables that can potentially affect performances in the course commence to be accounted for to ensure a proper evaluation and so, ability, age, hrss, i,e., study hours be as nearly as explored. All these variables reflect strong variability and thus are all potential candidates as controls. (For details, hear table 1 in appendix). Apart from simply looking at individual descriptive statistics, in order to obtain some idea about the interrelationships and potential causations, a table of scatter plots are also explored where smarks is the plotted as the y variable while ability, age, hrss, alevelsa attl as well as squared forms of ability and attl as the x variables. Fro m the plots ( icon 2 in appendix), we find that both ability and its square seem to be arrogantly correlated with marks. The variables age and alevelsa seem to have no associable patterns with marks. For attending, our primary variable of interest, we find that there is evidence of clustering of values greater than the mean marks at the higher values of attl implying that higher lecture attendance rate is associated with better performances on median(a) on the course. Further, it seems that there is some clustering at higher values of the squared lecture attendance rates. No correlation seems to be present between smarks and hrss from the last graph in the table. The interrelationships between these variables are important for regression specifications, since high correlations among independent variables may lead to multicollinearity. So, a scatterplot matrix is presented as figure 2 in the appendix. Therefore, the summary statistics and the scatter plots, show that there is a s trong possibility that coterie attendance influences performance along with other factors such as ability. Further, since some evidence of possible positive correlation between class performance as measured by smarks and the squares of ability and attendance, represented by attl were observed, the possibility of nonlinear dependence cannot be ignored. 2. Basic OLS estimation a) From the simple regression of smarks on an badger and the variable attl, we find that attendance has a significant positive impact on performance1. The coefficient on attendance is close to 0.15 and has a t-stat value of 4.331.96, which is the 5% critical value for the t distribution under the deceitful hypothesis that the coefficient is insignificant, i.e., is not statistically significantly different from zero. Additionally the intercept takes a value of 52.91 implying that the qualified mean of smarks is 52.91 for students who have a zero attendance rate for lectures. This value is significant at the 5 % level as well (t-stat value 19.061.96). However, the adjusted R-squared value is only 0.06 implying that only 6% of the variation of performance can be explained in terms of variations in lecture attendance rates. Therefore, the model assemble is poor. b) Inclusion of ability and hours studied (hrss) leads to the impact of attendance rate falling to approximately 0.13 from 0.15, but the