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Online Rating of Professors - Statistics Project Example

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This paper aims to answer the question if differences in underlying factors affect the overall quality rating of professors? In order to answer this question, the study used a quantitative research methodology using data taken from the Rate My Professor website…
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Online Rating of Professors
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"Online Rating of Professors"

Download file to see previous pages Online rating of professors has grown popularity with the site RateMyProfessor.com. In the website, students are able to rate their professors in terms of the overall quality by which they are able to deliver their lessons, from the categories easiness, helpfulness, clarity, and prior interest in the subject. Professors are also given a chili-pepper icon when the reviewer thinks that the professor is “hot” in terms of physical looks. Some controversy has risen from the website as students and even professors devour the results as if it were an official ranking method. Some students even go to the extent of basing the schools and classes that they will take on the reviews made at the website.
To make the ranking process “fair” to the professors, these members of the academe are allowed to respond to the feedbacks given to them through the “Professors Strike Back” portion of the website. Understandably, heated exchanges can occur as both students and professors defend themselves and their views. Some professors tend to be sensitive about the issues hurled against them while others choose to dismiss the ranking site entirely.
All these aside, this paper wants to investigate if there are underlying factors affecting the overall quality rating of professors at Rate My Professor. As such, this study poses the following research question:
RQ: Do differences in underlying factors affect the overall quality rating of professors?...
A summary of these descriptive statistics are given in Table 1. Box plots reflecting the behavior of the data are also provided in Figures 1 to 5. Table 1. Summary of Descriptive Statistics. Descriptive Statistics Dept Division num easiness overall count 730 730 730 730 730 mean 46.24 2.2 34.27 3.327 3.712 sample variance 598.07 0.96 683.18 0.541 0.693 sample standard deviation 24.46 0.98 26.14 0.735 0.832 minimum 2 1 10 1.2 1.4 maximum 96 4 168 5 5 range 94 3 158 3.8 3.6 Figure 1 shows that most of the data from the graph is concentrated in the second quarter. Then to the third quarter, then evenly spread to the first quarter, and the fourth quarter. There does not seem to be any outlier in the graph. Figure 1. Box plot for the variable “Department” Based on Figure 2, there is no first quarter data from the graph. Either that it is insufficient to generate a first quarter, or the first quarter is so concentraated that the data could not show. It is most likely that the data is insufficient to generate a first quarter. Figure 2. Box plot for the variable “Division” Figure 3 shows that most of the data is concentrated on the first quarter. Then on the second quarter, then the third, then the fourth. The one thing that makes this graph interesting is the high amount of outliers in the graph, which is not seen from the other graphs. Figure 3. Box plot for the variable “Num” Figure 4 shows that most data are concentrated on the third quarter, followed by the last and third, and a really spreaded first quarter. There is a possibility that there is an outlier in the first quarter. Figure 4. Box plot for the variable “Overall” Figure 5 shows that data are mostly concentrated in the second quarter. And ...Download file to see next pagesRead More
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