Question 1
Regression analysis is a statistical technique applied when determining the extent to which a dependent
variable is related to one or more independent variables. Hence, if researchers manipulate the variables
involved and record corresponding values of Y, they can identify the effects of the independent variables
by fitting a straight line through the observed values. The assessment foresees the determination and
quantification of coefficients denoting the nature and intensity of these connections. In quantitative
research, the coefficients are tested for significance using tests such as the two-tailed t-test for single
coefficients or F-distribution for the overall model. When using regression analysis, the reliability of
results is shown by statistics such as the coefficient of determination (R²), which shows how much of the
variations in the dependent variable are explained by the independent variables. Regression analysis is a
prevalent method used in economic, biological, and other sciences to make predictions and plan
decisions.
Question 2
From the estimated linear regression line (y), if it slopes to the y-intercept of zero, there is no simple
linear correlation between class performance and the amount of time invested per day preparing for
class assignments and mid-term tests. When the slope is equal to zero, it indicates that the independent
variable, in this case, the hours of preparation, does not affect the dependent variable, the academic
performance. However, the time used in preparing does not hurt students within the context of a linear
analysis model. However, this paper suggests that to check this, it only holds for linear correlations. It
might not fit perfectly as there may be interaction effect, quadratic effect or other confounding factors
that were not considered linearly. Research could continue using different models or include more
variables so that the factors that influence academic performance are best understood.
