Economics And Statistical Quantitative Analysis Of Online Education In US Universities

Background of the study

Discuss about the Economics and Statistical Quantitative Analysis.

The current report is concerned with the growing trend of online mode of education throughout the University of United States. The report highlights that in the current years, higher education sector has experienced a sharp surge in recent years. Several universities in United States has offered the privilege to impart online mode of learning’s. The existing report consists of the brief discussion of the analysis performed by using the statistical tools. The data takes into the consideration the rate of graduation and the rate of students retained in the university.

The main purpose of this study is evaluating the quality of education imparted by the universities located in United States.

Studies suggest that a large number of universities in united states is facing numerous challenges. Currently, online mode is considered as one of the highly sought after mode of education. Ever since the expansion of internet, studies has suggested that there has been a vast expansion in the online mode of learning since numerous industries have adopted the trend of imparting internet based learning. A large number of students are offered with the facilities of online mode of education and programme and utilises adequate instrument to implement such facilities.

Students generally dwelling in far-away places can have the opportunity of gaining access to their study material and materials imperative for study by using the internet. The existing study focuses on the quality of online education provided by the universities in united states. The study also provides the notion regarding the methods of collecting data through making data analysis. The understanding of the outcomes derived from the study lays down an in depth assessment of the methods used.

The report considers the data derived from the 29 universities of United States. In order to assess the data numerous statistical instruments are used to derive the desired outcomes such as measures of central tendency and measures of dispersion. A comparative study is used to evaluate the two variables derived which helps in laying down the notion of superiority of practice concerning the online mode of learning in these universities.

The report emphases on the equation of liner regression in order to assure the sum of association amid the two variables. The relationship between the two variables is generally characterised in the form of rate of retention and graduation rate. This is examined by putting into the use tool of scatter diagram. The statistical assessment undertaken enables in better understanding of the association between the rate of graduation and rate retention in the universities (Afifi and Azen 2014). The statistical measures helps in evaluating the quality of education imparted in these universities. 

Methods used for analysis

The extent of dispersion and central tendency has been calculated with respect to the variables GR and RR. In addition to this, the mean value, maximum and minimum value and standard deviation have been measured for these variables (Zhouet al. 2014). The measurement of mean value provides the location parameters related to the variables. Around twenty-nine universities are served with the average value of the variable with respect to the mean value. In contrast with these facts, the standard deviation is nothing but the measurement of dispersion. The standard deviation provides the scatterness of allocation.  On the other hand, the minimum and maximum values provide understanding of allocation. The following table shows the measures:

RR(%)
Mean	57.41
Standard Error	4.32
Median	60
Mode	51
Standard Deviation	23.24
Sample Variance	540.11
Kurtosis	0.46
Skewness	-0.31
Range	96
Minimum	4
Maximum	100
Sum	1665
Count	29
Inter-quartile range	24
CoV	40.48%

Table 1: Measures of descriptive statistics

(Source: Created by author)

GR(%)
Mean	41.76
Standard Error	1.83
Median	39
Mode	36
Standard Deviation	9.87
Sample Variance	97.33
Kurtosis	-0.88
Skewness	0.18
Range	36
Minimum	25
Maximum	61
Sum	1211
Count	29
Inter-quartile range	14
CoV	23.63%

From the below stated computation it is found that the inter-quartile range for rate of retention stands 24 while the coefficient variation for the rate of retention is 40.48%. The inter-quartile range for the graduation rate is 14% while the coefficient variation is 23.63% for the graduation rate. The mean value for the graduation rate is 41.76 and standard error represented as 1.83.

The above figure is obtained by putting the retention rate in the x axis and the graduation rate along the y axis. The pattern of the graph shows the incremental pattern. Therefore, this can be argued that the variables have positive and direct relationship among each other. Therefore, the graduation rate and retention rate has proportional to each other.

A regression equation is formed by putting GR along x axis and RR along the y axis. The outcomes of regression analysis are given as follows:

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	25.4229	3.746284	6.786166	2.74E-07	17.73616	33.10964	17.73616	33.10964
RR(%)	0.284526	0.060631	4.692772	6.95E-05	0.160122	0.40893	0.160122	0.40893

Table 2: Results of regression analysis

(Source: Created by author)

The regression equation formed from the above values shows that the regression coefficient with respect to regression coefficient is 0.284526. The formed regression equation is as follows:

In the above equation, the x is representing the retention rate and y is representing the graduation rate in the universities. In addition to this, the variable e represents the erroneous components (McIntosh and Miši? 2013). The p-value of the coefficient is 6.59 * 10^-5. The p-value has less in quantity in comparison with the significance level 0.05. Therefore, coefficient of slope is not 0. The test considered for the intercept is 2.47 * 10^-7. In contrast with this, the p-value has lesser value than the significance level α = 0.05.  Therefore, coefficient of slope is not 0. In addition to this, the coefficient of regression has positive value in this equation. Therefore, there is positive associative relation within the variables GR and RR. Therefore, this can be argued that the variables have positive and direct relationship among each other. Therefore, the graduation rate and retention rate has proportional to each other.

Outcomes

The graduation rate and retention rate are continuous variables. The associative relationships between these two variables are measures with the help of correlation co efficient (Parkset al. 2014). The direct and indirect relations are measures with the help of positive and negative values of this coefficient respectively. The following table is showing the correlation between these two variables:

	GR(%)	RR(%)
GR(%)	1
RR(%)	0.670245	1

Table 3: Correlation between retention rate and graduation rate

(Source: Created by author)

The obtained value for the correlation coefficient is 0.670245. Therefore, it is proven that the variables have direct relationship with each other.

In addition to this, the integrity of the regression model is evaluated with the help of adjusted R-Squared for the model.

Regression Statistics
Multiple R	0.670245
R Square	0.449228
Adjusted R Square	0.428829
Standard Error	7.456105
Observations	29

Table: Adjusted R squared for the regression model

(Source: Created by author)

The value of adjusted R- Squared in this model is 0.428829. In contrast with this fact, this model is perfect in reducing the errors.

The major objective of the analysis of data is to generate an idea of variables such as graduation rate and rate of retention. Upon conducting the analysis, it is found that there is large degree of difference between the mean values obtained from the above mentioned two rates (Heiberger and Holland 2015). The maximum value concerning the rate of retention is based on the higher side. Therefore, the rate of retention is higher than the rate of graduation. The outcome derived from the analysis portrays that there is prevailing circumstances of direct relationship between the two variables. It is worth mentioning that the value of retention rate increases with the rate of graduation. Being the president of South University there are concerns relating to the part time courses. The university should work towards improving the part time education for those students who does not have full time campus facilities. On the other hand, being the president of the Phoenix it is found that students of distant learners needs to be offered flexibility with certification programme which helps in keeping in stay with the interested course related work and some sometimes even easier to impart learnings under innovative programmes. 

Conclusion:

Upon concluding the report, it is evident from the analysis that online mode of learnings is important in United States. Outcomes of result obtained represent that graduation rate is superior to the rate of retention. The study also lays down few recommendations, which are as follows;

The adjusted R-square lays down relatively smaller value under the regression analysis. Thus, the model of regression is not a good fit model. The outcomes of the regression analysis only signifies simple measurement of data.

The sample size is very small having only 29 universities. It is recommended that the result would have been effective if other methods of sampling would have been considered for analysis.

Reference list:

Afifi, A.A. and Azen, S.P., 2014. Statistical analysis: a computer oriented approach. Academic press.

Heiberger, R.M. and Holland, B., 2015. Statistical analysis and data display: an intermediate course with examples in R. Springer.

McIntosh, A.R. and Miši?, B., 2013. Multivariate statistical analyses for neuroimaging data. Annual review of psychology, 64, pp.499-525.

Parks, D.H., Tyson, G.W., Hugenholtz, P. and Beiko, R.G., 2014. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics, 30(21), pp.3123-3124.

Zhou, L., Ye, S., Pearce, P.L. and Wu, M.Y., 2014. Refreshing hotel satisfaction studies by reconfiguring customer review data. International Journal of Hospitality Management, 38, pp.1-10.