Research Design Population The population to test the hypothesis is a group of 102 students’ data collected from various universities across the United States. The sampling included a systematic random sampling method to ensure that students from all kinds of universities and economic and academic backgrounds – the focus was to ensure that the sample was free from biases and reflected as closely as possible the true student population of the United States university students. The age range for the collected sample ranged from 18 to 24.

As many as 10 different universities were represented in the sample of 102 students and the economic status of the students was kept anonymous assuming that it was normally distributed. The next step was to gather the number of hours they spent studying. This was done by providing to them an hourly description document of their activities attached in the appendix. From that the number of hours studied was keyed off and stored for corresponding GPA entry. The GPAs were obtained from the career counselors of the universities – to ensure the accuracy and the authenticity of the data.

The methodology used by Stinebrickner and Stinebrickner (2007) is complex; it involves establishing the exogeneity of the factors that might affect the causal relationship of studying to academic average and was unique to the study since the research setting had a more controlled and rigid atmosphere than most colleges that made the experiment possible. Prior to the study, the researchers had gathered longitudinal data about student’s academic and personal life in the campus and used the said data to build a model of the typical college student’s habits and behavior.

Methodology Used Since the relationship between increase in hours of study and GPA increase is proportional (could be either linear or exponential), we will use regression testing to establish the conclusion as to whether the relationship is significantly strong enough (causative) or not. Research Question: “Is there a positive relationship between the number of hours spent by a student studying and the GPA obtained by the student? ” Data Analysis The ordered pairs for the correlation coefficient will be as follows: [Hours spent on Studying, GPA].

The dataset is as follows: Hours Spent GPA Hours Spent GPA Hours Spent GPA 0 3. 00 2 1. 71 2 3. 83 1 1. 67 2 2. 59 0 2. 01 1 1. 30 3 3. 36 1 1. 09 6 1. 34 1 1. 28 5 2. 59 2 1. 25 0 3. 17 0 2. 74 5 3. 85 4 1. 11 1 1. 47 2 2. 36 3 3. 95 1 1. 08 3 2. 93 0 3. 44 5 2. 26 3 1. 74 0 2. 88 4 3. 04 3 3. 55 1 2. 87 4 1. 95 1 1. 22 3 2. 26 0 3. 42 2 3.49 3 3. 09 1 3. 19 3 3. 76 0 1. 03 3 2. 27 5 2. 56 5 3. 76 2 3. 71 6 2. 50 1 1. 87 4 2. 40 6 2. 59 6 3. 68 1 1. 26 2 1. 29 0 1. 00 4 3. 27 2 3. 42 2 2. 45 2 1. 74 5 2. 37 0 2. 66 1 2. 25 4 1. 31 5 1. 13 2 2. 56 3 3. 00 5 3. 05 5 2. 67 0 3. 22 2 2. 73 0 1. 72 5 3. 34 4 3. 71 5 2. 80 4 3. 65 5 1. 52 2 1. 25.5 2. 14 4 1. 98 1 3. 30 0 3. 75 2 3. 09 6 2. 17 0 2. 75 3 2. 62 6 2. 69 0 2. 29 1 3. 42 6 2. 06 5 2. 70 4 1. 51 6 1. 93 0 2. 85 1 3. 36 6 2. 18 0 1. 87 0 2. 90 1 3. 79 4 3. 61 4 3. 13 3 3. 13 0 2. 38 1 3. 14 2 2. 37 5 3. 38 3 2. 38 0 1. 01.

This is based on the assumption that the number of hours spent on studying is the determinant of the GPA obtained by a student.

This leads to the proposition that age is the independent variable and money spent on an automobile is the dependent variable. A total of 80 ordered pairs of age and money spent on an automobile are available in the dataset. The following is the scatter plot obtained for the data: Even a rough glance at the scatter plot will tell a non-statistician that there is a no linear relationship or causative association between the two variables. However, for the purpose of our study, we will complete the steps necessary to form a conclusion.

Using the CORREL function in Excel, the value for the correlation co-efficient was: Analysis of the dataset using linear regression model led to following regression equation: Based on the values of the gradient and slope in the equation above and the correlation coefficient, one can easily conclude that the dataset seems to reflect upon the fact that there is a vague causative relationship between the number of hours spent studying by a student and their GPA. However, it is important to test this hypothesis and make a conclusion on the basis of statistical techniques.

Hypothesis Testing On the basis of the above obtained difference in the calculated and tabulated statistics, we can conclude that there is a positive relationship between the number of hours spent studying by a student and their GPA. Though weak it can be predicted by the following equation: Findings It was found from statistical testing that the calculated statistics (from the dataset) lay outside the region of acceptance. This forced us to reject the hypothesis that there is absolutely no relationship between the two variables.

Though weak (in terms of the gradients), the relationship exists, is incremental (positive) and suggests that the student not putting any hours of study will end up having a GPA of 2. 448 (put x=0). The graphical conclusions cannot be accepted as they, when accompanied by the trend line, seem themselves not enough to undermine a conclusion. The t-statistic testing was the best method to test the data and the conclusion is subtle, assuming that the data collected was free from errors and biases. Conclusion.

The statistical regression testing applied on the dataset suggests that there is a weak positive causative relationship associated with the students’ study hours and GPA. It forces the conclusion drawn from this study to be: the higher the number of hours spent by students studying, the higher will their GPA be. Even though the incremental GPA due to an additional hour of study is not significantly high, we cannot conclude that there is no relationship between the two (although the graph seems to suggest this on visual perception).

Taking the theory into account that slow and steady wins the race, we can assume the fact that students who spent in time studying generally have better GPAs than other students who either rely on their intelligence to get them across or other weaker students who are careless about studying. The scope of this research was limited and the results therefore are limited for interpretation. A better research carried out across more schools and more students definitely would promise better results in future undertaken by any other researcher.

But for the moment, we can conclude that there is a positive causative relationship between the number of hours studied and GPA for university students in the US. Works Cited Anand, V. (2007). A study of time management: The correlation between video game usage and academic performance markers . CyberPsychology & Behavior, 10(4): 552-559. Babbie, E. (2004).

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Newbury Park, CA: Sage, pp. 139-56. Rau, W. & Durand, A. (2000). The academic ethic and college grades: Does hard work help students to ‘make the grade’? Sociology of Education, 73:19-38. Rivkin, S. , Hanushek, E. & Kain, J. (2005). Teachers, school, and academic achievement. Econometrica, 73(2), 417-458. Schuman, H. , Walsh, E. , Olson, C. & Etheridge, B. (1985). Effort and reward: The assumption that college grades are affected by the quantity of study. Social Forces, 63:945-66. Stinebrickner, T. & Stinebrickner, R. (2004). Time-use and college outcomes. Journal of Econometrics, 121(1-2), 243-269.

Stinebrickner, T. & Stinebrickner, R. (2007). The causal effect of studying on academic performance. National Bureau of Economic Research Working Paper, No. 13341. Walpole, R. E. (2002). Introductory Statistics. Los Angeles: Kraft Publishers. Weiss, N. A. (1984). Introductory Statistics, 5th Edition. New York: CRC Press. Appendix Time Period What were you doing? 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 Noon 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 12:00 MN 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM.