Factors Affecting Students’ Final Grade

Factors Affecting Students’ Final Grade
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Factors Affecting Students’ Final Grade
Factors Affecting Students’ Final Grade in University
When students are admitted to the university they are brilliant about their future and most of
them if not all desire to excel in the field of academia by getting excellent grades before their
final year graduation. Usually, in university, the grade one is awarded is based on the aggregate
performance from the first year of study all through to their last year of study depending on the
programme one is taking in the university. It is surprising that despite the zeal on had before
being admitted to the university students perform differently in their final grade.
We formulated a problem in which we wanted to determine factors affecting students’ final
grade. We collected a population of 1002 individuals recorded their final grade along with other
factors which were; class attendance, the age of the student, years spent in a mathematics class
and the student's high school performance. We wanted to investigate whether the student final
grade was related to class attendance, the age of the student, years spent in a mathematics class
and the student's high school performance. From the population, we generated a random sample
of size 30 using simple random sampling. The sample was obtained with the use of random
numbers. The analysis of the sample population was done using the R package. (Solis et al,
2014).
We began by analyzing the relationship between variables by using scatterplot matrix. That is the
scatterplot representing the relationship between all the variables in the dataset that we were
using. The output of the scatterplot matrix is give below;

Factors Affecting Students’ Final Grade
From the scatterplot above its clear that all the variables are linearly independent. This was a
good indication that when using the linear regression model to fit the data most of the linear
regression assumptions will hold. We also revealed that the relationship between class
attendance was a negative linear relationship.
We then used a linear regression equation to examine the relationship between the dependent
variables and the explanatory variables which were of the form; Y=B0+ B1W+B2X+B3Y+B4Z.
Where Y represents the final grade, W attendance, X is the students OP, Y is the number of years
spent in math classes, and Z is the age of the students. The B0, B1, B2, B3, and B4 represents the
regression coefficients (Phillips and Moon, 2009). We used ordinary least squares method to