Statistical Analysis Online Mode of Education

Question: Discuss about the Statistical Analysis Online Mode of Education. Answer: Introduction: The current study outlines the current trends in America on the online mode of education. In the recent years the higher education sectors has witnessed upward surge in growth and expansion. There is large number of universities in United States that offers online mode of education. Numerous statistical tools has been undertaken to evaluate the quality of the education imparted in these universities and colleges (Afifi and Azen 2014). The existing report provides a brief description of the analysis undertaken through using several statistical tools. The data obtained represents the graduation rates and rate of retention of students in the universities. Objectives of this study: The major objective of this study is to assess the quality of the education imparted by the universities in United States. Background of the study: Currently the higher education sector in United States is undergoing several challenges over the last few years. The online mode of education is considered as the most sought after mode of imparting lessons in the recent days. Several universities have adopted online mode of education across United States. It is noteworthy to denote that the techniques adopted to impart learnings are also regarded as highly efficient in those universities. The online mode of education saves time as student living in distant places can gain access to study material and other materials associated with course through the help of internet (Deshpandeet al. 2016). The report highlights the quality of online mode of study in Universities in being understood. The report lays down the notion concerning the method of data collection along with the analysis of data as well. Following the analysis interpretation is also performed in the given report. Methods implemented for Analysis: The current report uses the data collected from 29 universities of United States. The collected represents that rate of retention along with the rate of graduation for the two universities. Statistical tools such as measures of central tendency and measures of dispersion have been used to assess the data obtained. Comparative study has been conducted to evaluate and measure both the variables, as this will help in gaining notion regarding the quality of the techniques used to impart online mode of study in these universities. The concept of linear regression equation is put into use to gain an understanding concerning the sum of association amid the two variables (Heiberger and Holland 2015). Scatter diagram has been used to understand the relationship amid the two variables such as Rate of retention (RR) and Graduation rate (GR). The statistical measures enables in better understanding of the association amid the Graduation Rate (GR) and the (RR) prevailing in the universities. Results: The rate of retention and graduation rate has been computed for the variables through using measures of central tendency and measures of dispersion. To compute the variables, mean value, standard deviation, minimum and maximum values are used. The mean value lays down the location of parameters for the variables. The average value obtained from the variable of 29 universities is illustrated by using mean value. Scatter distribution is computed through using standard deviation. The minimum and maximum values is calculated to provide a notion of the spread of distribution. Below listed table lays down the measurement, Table 1: Table representing measurement of descriptive statistics (Source: Created by author) The scatter diagram is obtained by considering the rate of retention (RR) in the form of independent variable. The scatter diagram is listed below; Figure 1: Figure representing scatter diagram of GR and RR (Source: As created by author) The above stated scatter diagram is derived by undertaking the rate of retention (RR) in the X axis whereas the rate of graduation (GR) in the Y axis. The graphs represent an upward rising trend and states that there remains positive yet direct relationship amongst the variables. The scatter diagram helps in reflecting an increase in the value of rate of retention and simultaneously the value of graduation rate increases (Kahraman and Sar? 2016). A regression equation is put into the use by undertaking the Graduation rate in the form of x variable and the Rate of Retention as Y variable. The outcome of regression analysis is illustrated below; Table 2: Table illustrating the outcomes of regression analysis (Source: As created by author) The regression equation derived from the above stated equation lays down that regression coefficient for rate of retention is 0.284526. Below stated equation lays down the regression equation; Y = 25.4229 + 0.284526*x + e. From the above stated equation, Y illustrates the rate of graduation in the universities and X illustrates the rate of retention. Variable E illustrates the components of random error. The coefficient slope represents the p-value of 6.59*10^-5. It should be noted that the P-value is lower than the level of 0.05. hence, the slope of coefficient is relatively different from 0. The p-value of the interception test represents the value of 0. The p-value is lower than the significance level of = 0.05. It can be concluded that the slope of coefficient is different from zero. Along with this, the value of regression co-efficient derived from the above stated equation represents a positive value. The equation represents positive association amid the two variables such as GR and RR. It can be said that the when the value of graduation rate increases the value of RR also simultaneously increases. The graduation rate and the rate of retention signify an uninterrupted variable. Statistical tool such as correlation co-efficient is used to study the association between the constant variable. A positive value of correlation reflects direct relation whereas the negative value illustrates negative association. Below stated table lays down correlation amid the two variables, Graduation Rate (%) Rate of Retention (%) GR(%) 1 RR(%) 0.670245 1 Table 3: Table illustrating correlation between retention rate and graduation rate (Source: As created by author) The value of correlation coefficient represents 0.670245 and it can be regarded that the variable represents direct relationship between them. The goodness of regression fit model is studied by using the adjusted R-square framework. Table 4: Table representing Adjusted R squared for the regression model (Source As created by author) The adjusted R-Square derived from the framework represents 0.428829. However, it is not regarded as one of the best model in terms of reducing the incidence of errors. As the president of South University, there are concerns associated with the part time online mode of courses. The major responsibility is to improve the online mode of learning in university by working towards improving the part time education for those scholars who does not have the opportunity of attending full time campus facilities. On the other hand, being the president of the Phoenix it is understood that undergraduates of distant learners must be offered elasticity with certification programme, which helps in keeping in stay with the interested course associated with work and some occasionally even easier to impart learnings under innovative programmes. Discussion: The data analysis provides the idea that the two variables namely the graduation rate (GR) and retention rate (RR). This represents huge difference between the mean values of the two rates. The computation also represents that the maximum value of rate of retention (RR) is also higher. Thus, the retention rate is higher than the graduation rate (Ko?acz and Grzegorzewski 2016). The results obtained from regression analysis states that there exists a direct relationship between the two variables. This represents that as the value of rate of retention increases the graduation rate also simultaneously increases. Conclusion and recommendations: The report assesses the status of online education in the universities of United States. The outcomes of analysis reflect that the rate of graduation is relatively higher in contrast to the rate of retention. Below listed are the recommendations derived from this study; 1.From the analysis, it is found that the adjusted R-Square derived from the regression analysis is relatively small. Thus, the regression model is not the best fitting model and the results derived from the regression analysis provides raw measurement of the dataset. 2.The sample size of research is small with only 29-sample size. Thus, the research could have been more effective if better procedure of sampling is considered. References Afifi, A.A. and Azen, S.P., 2014.Statistical analysis: a computer oriented approach. Academic press. Deshpande, S., Gogtay, N.J. and Thatte, U.M., 2016. Measures of Central Tendency and Dispersion.Journal of The Association of Physicians of India,64, p.64. Heiberger, R.M. and Holland, B., 2015.Statistical analysis and data display: an intermediate course with examples in R. Springer. Kahraman, C. and Sar?, ?.U., 2016. Fuzzy Central Tendency Measures. InFuzzy Statistical Decision-Making(pp. 65-83). Springer International Publishing. Ko?acz, A. and Grzegorzewski, P., 2016. Measures of dispersion for multidimensional data.European Journal of Operational Research,251(3), pp.930-937.