Data sets shared by StatCrunch members
Showing 1 to 15 of 779 data sets matching EXAM
Data Set/Description Owner Last edited Size Views
ejl-final-exam-data-ma181
Edward Lipchus MA181 final exam data. Data are gasoline receipts for my 2006 Ford Focus SXW.
trilsysDec 5, 20192KB53
Average Female Height, by Age
This data used as an example for review of logarithmic regression.
kcramerDec 3, 2019200B176
All MLB Salaries (1985-2015)
This data has all MLB player salaries between 1985-2015 including the team played for, the city, and a unique ID for each player. Total this includes 25,575 salaries for 4,963 different baseball players.
The player ID is the first 5 letters from the last name, followed by the first two letters from the first name, followed by a number in case of duplicate names. For example, bondsba01 stands for Barry Bonds with "01" because he's the first with the "bondsba" name ID.
statcrunch_featuredJun 27, 20171MB5808
Systolic BP Reading vs. Low Cognitive Function
This study tested the claim that hypertension results in low cognitive function. x: systolic blood pressure (mmHg) y: 30th Percentile or lower score on the Korean Mini-Mental Status Exam (K-MMSE)
profddavisNov 13, 2019192B74
StatCrunch Instruction Sheet Linear Corr and Reg Example - S. Lohse
This data set was included in a text book I was using at the time this example sheet was written.
slohse9395Oct 7, 2019179B425
Times World University Rankings (2011-2016)
This data comes from the annual Times magazine rankings of universities across the world. The webpage for the Times 2016 rankings is listed above in the source.
The formula for the 2016 rankings is as follows:
30% for Teaching Rating
7.5% for International Outlook Rating
30% for Research Rating
30% for Citations Rating
2.5% for Industry Income Rating.
The “Total Score” from 2016 can be recreated using this formula.

 Column Description World_Rank University rank for a given year University_Name The name of the university Country Location of university Teaching_Rating Rating from a 0-100 scale of the quality of teaching at the university. This rating is based on the institution’s reputation for teaching, it’s student/staff ratio, it’s PhD’s/ undergraduate degrees awarded ratio, and it’s institutional income/ academic staff ratio. Inter_Outlook_Rating Rating from a 0-100 scale of the international makeup of a university. This rating is based the international student percentage, international staff percentage, and the percentage of research papers from the university that include at least one international author. Research_Rating Rating from a 0-100 scale of quality of research at the university. This rating is based on the university’s reputation, it’s research income/ academic staff ratio, and it’s production of scholarly papers. Citations_Rating Rating from a 0-100 scale of based on the normalized average of citations by other papers per paper from the university (how often the research from the university is cited by other papers). Industry_Income_Rating Rating from a 0-100 scale grading how much companies are willing to invest in the universities research. The rating is calculated based on the research income from businesses per academic staff member. Total_Score The final score used to determine the university ranking based on Teaching_Rating, International_Outlook_Rating, Research_Rating, Citations_Rating, and Industrial_Income_Rating. Num_Students Total number of students in a given year Student/Staff_Ratio Number of students per academic staff member %_Inter_Students Percentage of student body who come from a foreign county %_Female_Students Percentage of student body that is female. Year Academic year that the ranking was released. For example, 2016 denotes the 2015-2016 academic year.
statcrunchhelpApr 5, 2016254KB4196
Baseball2013.xlsx
Stats from the major league baseball teams for 2013. The last column I added denotes AL for American League and NL for National League. One could possibly conduct a two-sample means test, for example, to find out whether the average runs for the two leagues are equal. Or there are of course lots of regressions one could run.
eykoloNov 4, 20133KB2096
RegisteredNursesSurvey.xlsx
For what survey produced it, see http://www.statcrunch.com/5.0/survey.php?surveyid=8178&code=YINVQ and inputs of all team mates. Towards the end, some validation was done, deleting data where working hours was less than a work day, or outliers to legally admissible work days. Finally arbitarily long chains which were less likely to be encountered in draws of simulated data (M/F, Degrees etc.. were discarded). A total of 12 observations were thus thrown out. All Credit goes to Team 3,the Instructor, our unnamed Friends in the Nursing profession who enthusiastically did a last minute push through over their extended social media groups for data and the respondents who kindly took out time for the survey. Another thought is about the distribution of hours worked. Wven if random, it "should be" "centered on" certain hours a day* number of days, with deviations from centre penalised, while picking a sample.. The observations 38 appear many times for example, however without an explainable reason (we are talking of work-distribution among nursing staff sample) So do "primes" "47, 37, 29" It is not to argue that they "shouldn't occur", but there has to be some reason for their being so significant/vibrant. At this stage we may conclude that most of the respondents may not have been under full-time nursing employments in strict sense of the term. 42, 48,72,60, 50,40 appearing more often would give us less variation but more regularity in the data. Since we haven't tried stratification, we do not know "how often they should occur". We thus do not re-draw observations.
ugoagwuJun 14, 20142KB1149
All MLB Salaries (1985-2015)
This data has all MLB player salaries between 1985-2015 including the team played for, the city, and a unique ID for each player. Total this includes 25,575 salaries for 4,963 different baseball players.
The player ID is the first 5 letters from the last name, followed by the first two letters from the first name, followed by a number in case of duplicate names. For example, bondsba01 stands for Barry Bonds with "01" because he's the first with the "bondsba" name ID.
statcrunchhelpMar 15, 20161MB1743
rosesegeJun 21, 20129KB5437
AP Statistics Predictions 2013-16
GPA = Student's Weighted GPA before beginning AP Statistics PrevMath = The highest math course the student completed at our school prior to AP Stats AP.Ave = The student's average score on the AP exams taken (if available) MathGPA = Unweighted GPA of student's work in math courses MT.MC = Students number correct (out of 40) on the multiple choice section of their midterm (MT) MT.Raw = Student's raw score (out of 100) on the multiple choice and free response sections of a previously released AP exam Locus.Aug = Student's score (out of 100) on the LOCUS diagnostic test in the beginning of the school year S1P = Student's first semester grade as a percentage S1G = Student's first semester letter grade S1F = Student's (scaled) first semester final exam grade (a.k.a. midterm test grade) S2P = Student's second semester grade as a percentage S2G = Student's second semester letter grade Ch 1-4 = Student's raw test average on ch. 1-4 Ch 1-6 = Student's raw test average on ch. 1-6 Ch 1-8 = Students raw test average on ch. 1-8 MT = Student's raw test average on the midterm Ch 1-12=Student's raw test average on ch. 1-12 (entire textbook) Mock 1 = Student's raw score on first mock exam (mid-March) Mock 2 = Student's raw score on second mock exam (late April) Mock 1&2 = Student's average on two mock exams MT&Mock1&2 = Student's average on midterm and two mock exams MT.AP = Student's converted score (1-5) on midterm Mocks.AP = Student's converted score (1-5) on average of two mock exams MT&Mocks.AP = Student's converted score (1-5) on average of MT and two mock exams ACTUAL = student's actual performance on AP exam (blank means student opted out of taking exam) MT.Resid = Actual score - Midterm score Mocks.Resid = Actual score - average Mock exam score MT&Mocks.Resid = Actual score - average midterm and mock exam score
je175Jul 5, 20169KB2321
Ex_ChocolateAndNobelPrizes
Messerli (2012) examined the relationship between the number of Nobel Prize winners in a country and chocolate consumption in that country. Chocolate consumption was measured in kg eaten per person per year, and Nobel Prize winners were measured in number of prizes won per 100 million citizens. The data are given below:
williamsjl8Sep 12, 2019463B257
Housing Price Data
This is an example of the relationship between housing prices with the square footage of the house, the age of the house and if the house has a finished basement.
jpalmateerNov 7, 20133KB2183
Treatment Effects of a Drug on Cognitive Functioning in Children with Mental Retardation and ADHD