C207 Task 2

1. Describe the relevant data you collected.

The relevant data that was collected was from public 4-year universities from the school year 2017-2018. Also, the tuition that was collected was from in-state tuition that included on campus costs. All this data was collected from the National Center for Education Statistics ( https://nces.ed.gov/ipeds/use-the-data ). It was created using the summary table option and was chosen to only produce the public 4-year universities. After finding the data and inputting one university from each state with their respective tuition cost and graduation rate from the school year 2017-2018. The averages were found from all 50 states. The average tuition cost is $26,518.12 and the average graduation rate is 65.5%.

2. Create an appropriate graphical display (e.g., bar chart, scatter plot, line chart, or histogram) of the data you collected.

C. Report how you analyzed the data using an analysis technique from the given list by doing the following:

1. Describe an appropriate analysis technique that you used to analyze the data.

The appropriate analysis technique that was used to analyze the data was Linear Regression. Linear Regression is the “relationship between two variables can be measured by its strength” (MingEdge). This analysis technique will help determine what type of relationship there is between tuition costs and graduation rates.

2. Include the output and any calculations of the analysis you performed.

3. Justify why you chose this analysis technique.

This data analysis technique is appropriate to use on the data collected because with the use of the plotting of data, it will make it clear to understand what the relationship between tuition costs and graduation rates are. The independent variable is the tuition cost and the dependent variable is the graduation rate.

D. Summarize the implications of your data analysis by doing the following:

1. Discuss the results of your data analysis.

R-square equals 0.28 which because the value is closer to 0, it indicates a weaker correlation between the two variables. While looking at the graphical display, the viewer can see that there may be a low R-squared because of the points in the scatter graph. Another result of the data analysis was that p = 0.000075 which means that there is a significant relationship between tuition cost and graduation rates. This is because p is less than 0.05. The summary regression output above in C2 indicates that the standard error is 0.14. Also, based off of the graphical display in B2, there is a positive weak relationship between tuition cost and graduation rates.

2. Discuss the limitation(s) of your data analysis.

We do not know if geographic data of the students, geographic data of the location of the university, and the types of degrees offered could possibly be relevant to the business situation that is being reviewed. These limitations could unknowingly affect the results of the data analysis. Also, the variables that weren’t measured but could also be relevant are the rankings of the universities in relation to one another. This could possibly tell us which schools offer a higher quality of education than others and that would possibly have an effect of the graduation rate.

3. Recommend a course of action based on your results.

Based off the data analysis, there is a positive weak relationship between tuition cost and graduation rates. This shows that there is not a strong enough relationship between the two variables and that there may be other variables that have a stronger correlation than tuition cost on graduation rates. The recommendation in the scope of what was discovered was that tuition cost does not have a strong enough effect on graduation rates. This would mean that it should not matter how much tuition costs are in a simple sense. There may be other variables that need to be looked at in order to see which has the strongest effect on graduation rates. Another recommendation would be to go forward with expanding the research to include all thought of variables that are relevant to graduation rates in order to identify which variable has the strongest effect of graduation rates.

E. Sources

MindEdge (2017). Data-driven decision making. Waltham, MA: MindEdge, Inc

National Center for Education Statistics, nces.ed.gov/ipeds/use-the-data.