Data sets shared by StatCrunch members
Showing 1 to 15 of 160 data sets matching regression
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.
|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.
||yes||statcrunchhelp||Apr 5, 2016||254KB||464|
Top 100 Retailers 2015|
This dataset comes from the National Retail Federation and tracks the top retail chains in the US for 2015 based on their 2014 sales. The original data can be found at the webpage listed as the source. Note that these retailer include all sorts of avenues including internet sales.
|yes||statcrunchhelp||Mar 14, 2016||7KB||469|
California Home Prices, 2009|
This dataset is a collection of real estate listings from San Luis Obispo county, California, and some locations around it from 2009. The prices are their list price at the creation of this dataset. For more information about this data, go to the website source listed above.
|yes||statcrunchhelp||Mar 11, 2016||46KB||683|
National Longitudinal Youth Survey|
The Youth survey consists of a nationally representative sample of youths who were 14 to 20 years old as of December 31, 1999.
This dataset tracks the Age, Height (in inches), Weight (in pounds), Gender, and the self reported "How would you describe your weight?" multiple choice answers for the individuals.
|yes||statcrunchhelp||Mar 8, 2016||330KB||362|
Data taken from the Journal of Statistics Education online data archive. That archive in turn got the data from an article in the Journal of the American Medical Association. (Mackowiak, et al., "A Critical Appraisal of 98.6 Degrees F …", vol. 268, pp. 1578-80, 1992).
"Body Temp" is measured in degrees fahrenheit
"Heart rate" is the resting beats per minute
|yes||statcrunchhelp||Mar 8, 2016||2KB||656|
Regression and Correlation worksheet.xlsx|
M4_Regression and Correlation
|yes||hollypet||Feb 3, 2016||139B||385|
Federal Food Assistance Participation|
This primarily comes from the following source: United States Department of Agriculture: Food and Nutrition Service . This dataset also incorporates data from another StatCrunch dataset: US Workforce Participation
|Year||The year for each data value|
|Average Federal Food Assistance Participation in Thousands||Number of individuals in the US who took part in SNAP (Supplemental Nutrition Assistance Program) during the given year.|
|% US Population on Federal Food Assitance||% of US population that is currently in the SNAP program and is receiving aid with food.|
|Change of % (US Population on Federal Food Assistance)||The change in the percentage of the US population that is receiving food assistance from SNAP.|
|Presidential Control||Political party of president.|
|Senate Control||Political party of the Senate majority|
|House Control||Political party of the House of Representatives majority.|
|Legislative Branch (House and Senate)||Combined control of Senate and House of Representativs|
|Male Inactivity Rate Aged 25-54||Defined as the proportion of the male population aged 25-54 that is not in the labour force. Common reasons for leaving labour force: college, retirement, stay at home, can't find work and no longer try.|
|Change of Rate (Male Inactivity Rate Aged 25-54)||The change in the inactivity rate calculated as the current year minus the previous year.|
|Female Inactivity Rate Aged 25-54||Defined as the proportion of the female population aged 25-54 that is not in the labour force.|
|Change of Rate (Female Inactivity Rate Aged 25-54)||The change in the inactivity rate calculated as the current year minus the previous year.|
|Annual Average Workforce Participation Rate||Defined by the Bureau of Labor Statistics as "the percentage of the population [16 years and older] that is either employed or unemployed (that is, either working or actively seeking work). Note that 2015's Annual Average is calculated using the first 11 months."|
|Change of Rate (Annual Workforce Participation Rate)||The change in the workforce participation rate calculated as the current year minus the previous year.||yes||statcrunchhelp||Jan 8, 2016||10KB||490|
Text Messaging Activity||yes||12266555_ecollege_kentmlp||Sep 26, 2015||2KB||1280|
Nonlinear_Regression_world_population (1).xlsx||yes||ealgephantom||Dec 4, 2014||313B||539|
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.
|email@example.com||Nov 4, 2013||3KB||1560|
Regression: Cigarettes Lung Kidney Leukemia Bladder|
"Cigarette smoking and cancers of the urinary tract: Geographic variation in the United States"
Journal of the National Cancer Institute (vol. 41, no. 5, November, 1968), pp. 1205-1211; table from pp. 1206-1207.
Joseph F. Fraumeni, Jr.
Oxford University Press
Units: cigarettes sold per capita, cancer deaths per 100,000
|yes||phil_larson||Sep 22, 2013||2KB||2539|
Low Birth Weight Study|
SOURCE: Hosmer and Lemeshow (2000) Applied Logistic Regression: Second Edition
Data were collected at Baystate
Medical Center, Springfield, Massachusetts during 1986.
The goal of this study was to identify risk factors associated with
giving birth to a low birth weight baby (weighing less than 2500 grams).
Data were collected on 189 women, 59 of which had low birth weight babies and 130 of which had normal birth weight babies. Four variables which were thought to be of importance were age, weight of the subject at her last menstrual period, race, and the number of physician visits during the first trimester of pregnancy.
LIST OF VARIABLES:
Columns Variable Abbreviation
2-4 Identification Code ID
10 Low Birth Weight (0 = Birth Weight >= 2500g, LOW
1 = Birth Weight < 2500g)
17-18 Age of the Mother in Years AGE
23-25 Weight in Pounds at the Last Menstrual Period LWT
32 Race (1 = White, 2 = Black, 3 = Other) RACE
40 Smoking Status During Pregnancy (1 = Yes, 0 = No) SMOKE
48 History of Premature Labor (0 = None 1 = One, etc.) PTL
55 History of Hypertension (1 = Yes, 0 = No) HT
61 Presence of Uterine Irritability (1 = Yes, 0 = No) UI
67 Number of Physician Visits During the First Trimester FTV
(0 = None, 1 = One, 2 = Two, etc.)
73-76 Birth Weight in Grams BWT
These data have been used as an example of fitting a multiple
logistic regression model.
STORY BEHIND THE DATA:
Low birth weight is an outcome that has been of concern to physicians
for years. This is due to the fact that infant mortality rates and birth
defect rates are very high for low birth weight babies. A woman's behavior
during pregnancy (including diet, smoking habits, and receiving prenatal care)
can greatly alter the chances of carrying the baby to term and, consequently,
of delivering a baby of normal birth weight.
The variables identified in the code sheet given in the table have been
shown to be associated with low birth weight in the obstetrical literature. The
goal of the current study was to ascertain if these variables were important
in the population being served by the medical center where the data were
1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).
|yes||wikipeterson||Jul 23, 2012||6KB||4260|
Baseball data for correlation and regression|
This table shows the total number of runs scored, at bats, hits, etc for each of the 30 MLB teams for the 2009-2011 seasons.
Correlations and linear regression models can be calculated between the different numeric variables. A good exercise is to see which variables correlate most strongly with runs_scored.
As emphasized in the movie Moneyball, some of the classic metrics such as batting_avg is not as good as the newer metrics like OBP (on base percentage), SLG (slugging percentage), or OPS (on base plus slugging).
A guide to a few of the variables that may not be self explanatory.
Runs_Scored: The total of all runs (points) the baseball team scored by the end of the season.
Batting_avg: This is equal to the number of hits divided by at_bats
OBP: On Base Percentage. Similar to batting average, except that it takes into account walks and hit-by-pitch. Some players who don't have high batting averages, manage to get walked quite frequently.
SLG: Slugging - This weights hits to first base as 1 point, hits to second base as 2 points, third as 3, homeruns as 4, and divides the total by the number of at bats.
OPS - On Base Plus Slugging - this is just OBP added to the SLG numbers.
|yes||mileschen||Apr 17, 2012||6KB||1845|
Cigarette Consumption vs CHD Mortality|
Now that cigarette smoking has been clearly tied to lung cancer, researchers are focusing on possible links to other diseases. The data below show annual rates of cigarette consumption and deaths from coronary heart disease for several nations. Some public health officials are urging that the US adopt a national goal of cutting cigarette consumption in half over the next decade.
Examine these data and write a report. In your report you should:
1. Include appropriate graphs (e.g. scatterplot, residual plot) and statistics (e.g. mean and SD);
2. Describe the association between cigarette smoking and coronary heart disease;
3. Create a linear model;
4. Evaluate the strength and appropriateness of your model;
5. Interpret the slope and y-intercept of the line;
6. Use your model to estimate the potential benefits of reaching the national goal proposed for the US. That is, based on your linear model, if the US were to cut its cigarette consumption in half (from 3900 to 1950), what does the linear model predict would happen to the CHD rate.
7. You should use Statcrunch to generate nice looking graphs and output as needed. Be sure to size them appropriately. No need for a 8x10 scatterplot; Make your graphs about 3x3. You should scale them in Statcrunch first, then copy and paste into Word.
|yes||smcdaniel04||Sep 29, 2011||267B||3503|
Anscombe's 4 data sets for regression. They are very different, yet have the same correlation and regression coefficients.
|firstname.lastname@example.org||May 31, 2011||360B||596|