November 30, 2012
This research paper discusses the effects of several different factors on a student’s success in a Business Statistics course. The different variables include areas related to the student’s academic factors as well as factors related to the student’s personal life. The academic related variables are: course of study, study hours per week, semester credit hours, GPA, class year, semester and class time. The personal life variable is: work hours per week. All of the above listed variables are highly related to a students’ ability to succeed in a Business Statistics course.
The main purpose of this research paper is to determine which factors show the greatest significance in predicting a students’ success in a Business Statistics course. This study will provide valuable information to both students and professors by helping both to modify certain factors to produce a higher success rate in this course. This information could be used by students to decide what the best time of day would be to take a statistics course, how many study hours are needed or to decide which school year is best to enroll in the course. Professors could use this information to schedule their statistics courses at the peak hours of the day that are the best times to take a statistics course or to utilize as an advising tool to inform students that statistics should be take with a smaller overall course load.
The following section of this paper will discuss some of the results and information that has been found by other researchers on this topic. Then we will discuss the methodology that was used for this study, disclose the results that were found and discuss the effects of this information.
In 2012, there have been numerous studies conducted to analyze student performance in Business Statistics courses relating to varying factors in student’s lives. Two important papers involved in this research are Dotterweich and Rochelle  and Williams . Dotterweich and Rochelle conducted the study of student success by comparing the effects of different course delivery methods: online, instructional television and traditional classroom. They tested a sample of approximately 50 students, all with varying characteristics, per class to collect three data sets of information. They discovered that although online students were typically older and have earned a higher number of credits before taking statistics, they had a higher number of students repeating the course. The study also revealed that the factors most significantly impacting a student’s success were GPA and class absences.
Similar to that of Dotterweich and Rochelle, Williams studied online homework vs. traditional homework for a business statistics course and whether or not that would have an impact on student success. Williams results of the study showed that student’s performance was not significantly impacted with the traditional pencil-and-paper homework method vs. the online method. The results of the study indicated that whether or not the student had immediate feedback from an online system or delayed feedback with a traditional system, there was not a significant difference in overall performance and overall final grade in the course. METHODOLOGY
The Correlation analysis was used to determine if any of the independent variables were highly correlated with each other. The Minitab output showed that there was a strong correlation between the independent variables which is indicated by the correlation coefficient or a small p-value listed on the output. The closer the correlation coefficient is to 1 or -1, the higher the correlation of the variables.
Checking for multicollinearity is important when using a regression analysis with multiple independent variables. Multicollinearity occurs when one or more of the...