Linear Regression Analysis of Graduation and Retention Rates in Online Education

ECONOMICS AND QUANTITATIVE ANALYSIS 1
ECONOMICS AND QUANTITATIVE ANALYSIS
Student Name
Course Name
Professor’s Name
Institution name
State
Date

ECONOMICS AND QUANTITATIVE ANALYSIS 2
Linear Regression Report
Purpose
The main objective of this study is to identify the correlation between the retention
rate (RR) and the graduation rate (GR). These two rates are vital for measuring the
performance of colleges and can assist students gauge the right school to undertake farther
studies (Craig 2015). By identifying the relationship between the rates, one may be able to
use one as a predictor of the other.
Background
Online courses are university programmes that students take through computers
connected to the internet. This is different from the normal learning system where students
have to attend classes at designated locations. Due to the improved technology, spreading of
the global internet coverage and the demand for flexible school schedules by individual’s
online education have recently gone up (Morris 2018). As several institutions crop up to offer
online courses students and parents are thrown in to a dilemma as they struggle to select the
best school to attend. Having removed the physical barriers that may hinder a student from
attending a college far away, the online platform’s biggest challenge is the selection the
college offering best quality education. Two rates have emerged that can be applied in
making this choice simple for the students; that is the graduation rate (RR) and the retention
rate (GR) (Sabbah 2011). The GR is the proportion of an institutions first years who goes
ahead to complete their studies in the institution within 150% of the recommended time for
the course (Anstine 2013). The retention rate RR is the proportion of first year students who
register for their second year to study in the same institution.
The two rates can be applied in making comparisons and hence gauging the quality of
education being offered in an institution in comparison to the other universities offering
online studies. This makes them of interests to economists who are interested in making the
quality comparisons.
Method
The data sample used in the analysis was derived from the Online Educational
Database. This is an organisation which was formulated to independently generate the list of
universities accredited to offer online study programs. This data is a secondary data that has
been collected verified and recorded by previous research activities. To evaluate the

ECONOMICS AND QUANTITATIVE ANALYSIS 3
association between the RR and the GR, the study developed a simple linear regression
model in addition to drawing a scatter plot (Long 2009). Also, the descriptive statistics was
generated o assist describe the numerical features of the data sample.
Results
a. The table below gives the numerical analysis of the data sample
RR(%) GR(%)
Mean 57.4137931 Mean 41.75862
Standard Error 4.315602704 Standard Error 1.832019
Median 60 Median 39
Mode 51 Mode 36
Standard Deviation 23.24023181 Standard Deviation 9.865724
Sample Variance 540.1083744 Sample Variance 97.33251
Kurtosis 0.461757455 Kurtosis -0.8824
Skewness -0.309920645 Skewness 0.176364
Range 96 Range 36
Minimum 4 Minimum 25
Maximum 100 Maximum 61
Sum 1665 Sum 1211
Count 29 Count 29
Largest(1) 100 Largest(1) 61
Smallest(1) 4 Smallest(1) 25
b. To give a visual representation of the association between the two variables
the scatter plot was developed. This is displayed in the chart below (Malakooti 2013).
From the visual presentation of the chart the dots are increasing from the left towards
the right, this is interpreted as a sign of positive correlation between the two rates.