Econometric Analysis of Retention and Graduation Rates: A Report

Desklib provides past papers and solved assignments for students. This report analyzes the correlation between retention and graduation rates.

Economics and quantitative analysis
1

Table of Contents
Purpose............................................................................................................................................3
Background......................................................................................................................................3
Method.............................................................................................................................................3
Results..............................................................................................................................................3
Discussions......................................................................................................................................5
References........................................................................................................................................7
2

Purpose
The purpose of preparing this report is to determine the nature of relationship between the given
two variables namely, retention and graduation. For the easy calculation and application of
statistical tools and techniques, both the variables have been defined in a single unit i.e., rate
(expressed in %). Since, before applying any tools, it is imperative to properly apply the various
applicable statistical measures.
Background
For facilitation of easy calculation, it has been already given that retention rate is an independent
variable. On the other hand, graduation rate is a dependent variable. This will help to reduce the
complexities involved in drawing interpretation and analysing the given set of data. As such,
determining which variables are independent and which variables are dependent makes the
computation easy. It also facilitates the application of statistical tools and techniques in the
smooth and functioning manner.
Method
Some statistical tools have been applied in order to determine or identify nature and degree of
relationship between the given two set of variables in this case. The report has been divided into
appropriate headings for the easy understanding of the concept. In other words, the report has
been prepared in such a manner that anyone can understand the conclusions mentioned therein
easily.
Results
(a)
RR (%)
Mean 57.41
Standard Error 4.32
Median 60.00
Mode 51.00
Standard Deviation 23.24
Sample Variance 540.11
3

Kurtosis 0.46
Skewness -0.31
Range 96.00
Minimum 4.00
Maximum 100.00
Sum 1665.00
Count 29.00
GR (%)
Mean 41.76
Standard Error 1.83
Median 39.00
Mode 36.00
Standard Deviation 9.87
Sample Variance 97.33
Kurtosis -0.88
Skewness 0.18
Range 36.00
Minimum 25.00
Maximum 61.00
Sum 1211.00
Count 29.00
(b)
0 20 40 60 80 100 120
0
20
40
60
80
100
120
f(x) = x
R² = 1
RR(%)
Linear (RR(%))
Linear (RR(%))
GR(%)
Linear (GR(%))
Axis Title
Axis Title
4

(c)
From the above scatter diagram, regression equation can be formed and accordingly, value of R2
comes out to be 1 indicating the fact that the extent of dependency is higher in this case.
Accordingly, it can bas said that in this particular case, graduation rate is
(d)
Coefficient
s
Standar
d Error
t Stat P-value Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercep
t
-8.52 14.42 -0.59 0.56 -38.11 21.08 -38.11 21.08
GR (%) 1.58 0.34 4.69 0.00 0.89 2.27 0.89 2.27
Estimated regression equation will be obtained from the above table. This will help to insert
variables in such a manner that extent and degree of relationship can be determined accurately
and that too with ease.
Determination of such relationship helps to various
Discussions
Some basic tools relating to statistics have been applied in order to better understand the given
set of variables. It has come to the observation that all the concerned outputs are telling the same
and one thing. In other words, there is no ambiguity in relation to the degree of correlation that
exists between that is likely to exist given variables.
5

References
 Ayinde K., Lukman A. F. and Arowolo O.T., 2015, COMBINED PARAMETERS
ESTIMATION METHODS OF LINEAR REGRESSION MODEL WITH
MULTICOLLINEARITY AND AUTOCORRELATION, Journal of Asian Scientific Research,
ISSN(e): 2223-1331/ISSN(p): 2226-5724
 Harvard Business Review, 2015, A Refresher on Regression Analysis, [Online], Harvard
Business Review, Available at https://hbr.org/2015/11/a-refresher-on-regression-analysis
[Accessed on 11.02.2019]
 Richter, S., 2015, Regression Analysis, UNCG Quantitative Methodology Series
 Texas gateway, 2019, Introduction to Scatterplots, [Online], Texas gateway, Available at
https://www.texasgateway.org/resource/interpreting-scatterplots [Accessed on 11.02.2019]
6