Identify Patterns In Data: Student Rate

Identify Patterns in Data Student’s Name Institutional Affiliation Identify Patterns in Data Identify the dataset and variables and provide context to the research used to collect the data (in an introduction section). On 12th September of 2015, the US Department of Education launched an online tool as an assistant for consumers (most likely students) to compare higher learning institutions based on their costs and value. The administration of the former US President (Barack Obama) spearheaded the launching of the website. The name of the website is College Scorecard. The website consolidates vital information about colleges and universities, such as yearly cost, average student debt, completion or graduation rate, expected job earnings, and employment rate. The College Scorecard is structured to improve transparency by bestowing the power of choice on the public to check out how various schools are serving their students. As diversification of people’s way of life is largely felt in terms of choices and preferences, different families and students may often choose certain schools based on yearly costs, average student rate, employment rate, etc. Over the years, the initial version of the scorecard was discredited since it lacked qualitative information e.g. location of the schools listed. The limitation was misleading and less reliable in guiding a student to their best-fit college. Additionally, criticism arose upon the revelation of inaccurate loan repayment rates for most colleges published on the site (Hanley, 2017, p. 504). The department quickly aims to fix the mistake because it was a ‘coding error’. In 2017, Donald Trump’s administration released an updated version of the site. Today, prospective scholars can create a customized search for their dream college, compare colleges on the framed search and make a decision on which school to choose. The Data.gov website contains datasets accessible and ready for use by the public. In reference to that, the dataset featured in this paper is also accessible and in RDF, CSV, JSON, and XML form on data.gov. The Most Recent Cohort Scorecard Elements dataset has 102 variables both categorical, such as map location and numerical e.g. the OPEID variable. There are several blank entries, NULL and 0 values. Specifically, the selected variable (SAV_ATG) has both numeric and null values. Identify the analysis performed, pros and cons of calculation, and why they are used (in a method section) Data distribution is a fundamental concept in statistics. By definition, a function explaining how all possible data values and their frequency is known as data distribution. Two measures of distribution; one of central tendency (mean and median) and the other measure of spread (standard deviation) describe a distribution. The most common distribution is the standard normal or Z or Gaussian that is defined by a nice bell-shaped curve. Other distributions include Poisson, binomial, Bernoulli, chi-squared, log-norma, etc. Distributions can as well be symmetric like the Z distribution or asymmetric like skewed distributions. Right-skewed distributions have tails to the right with mean greater than the median and often common in biological data. The mean tends to be more susceptible to affect ...