The Relationship Between Top Universities and Their Features In The World

Document Type:Research Paper

Subject Area:Economics

Document 1

Features that distinguish various universities include the quality of education, the number of employee alumni, the quality of faculty with a degree and can teach the students perfectly using the knowledge he or she has, publication in relation to academic research, the score of acceptance, citations which are the process of making papers or documentation for academic research, and not forgetting the university environment which is of great consideration before one joins any learning institution. In addition, the number of patents and the years between 2012-2015 is to be considered, and when these conditions are at their maximum, the university will be at the top in the long run (Fan, et. al, 2018). Each of the features above has variables, and these variables are deemed to change each year, and they may either increase or decrease.

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Taking Harvard University as an example, it has been at the top worldly between 2012-2015 since these factors did not change. There was a wide range of resources that I used in the course of my research which included; newspaper articles examination, literature books in the library, conducting one on one interviews and watching clips on the uprising of the university (Aldridge, et. al, 2018). Besides, we had questionnaires around the institution that helped us collect the students' idea on the information they have about the university. The research took a total of two weeks where I began by looking at the facts that I had in mind and then proceeded to relate the ideas I had about the university to the immediate environment and other competing universities.

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The interviews were done both one on one and also indirectly where we could send the questions to those far from the institution via email. 10 Holding all other input elements constant, the average product is the amount of total output produced per unit input variable. When determining the average of any element therefore, there was need for us to assume all other factors constant and only deal with a single element at a time. I conducted reviews by distributing questionnaires around the university on the various levels of the elements which consisted a rate gauge of ten. I later compiled these answers and came up with the graph according to the reviews given by the students. The average amount of publication of the year 2012 was seen to be the highest and the average quality of faculty was recorded as the lowest.

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5 were the same and the highest. The lowest was on the quality of faculty. Getting the values obtained and arranging them in ascending order, I was able to obtain the middle value. This is the median and that is what I applied in coming up with the graph. These median values are in addition the ones that I applied in determining the R-squared value. 8 Sum of square error=1058801. 91 R square=(176466. 322 Regression Model Regression is a method of developing a target value from independent variables. Variations in the regression are due to the relationship between the independent and dependent variables. For simple linear regression, there exists a linear relationship between the independent(x) and the dependent (y) variables. Comparable interpretation is given for inference on β1, using the row that begins with intercept.

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The column "Coefficient" gives the least squares estimates of β1 and β2. The column "Standard error" gives the standard errors of the least squares estimate of β1 and β2. The second row of the column "t Stat" gives the computed t-statistic for H0: β2 = 0 against Ha: β2 ≠ 0. This is the coefficient divided by the standard error: here 0. When the difference is divided by SST, R-squared is obtained. This will designate the goodness of fit of the model. R-squared is intuitive as it ranges from zero to one. When at zero, the model does not advance prediction over the mean model whereas one indicates perfect prediction. One drawback of the R-squared is that it can only increase as predictors are added to the model. Works Cited Aldridge, Judith, Alex Stevens, and Monica J.

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