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Professor Salaries 2018-2019
This data came from the following website: Tenured/Tenure-Track Faculty Salaries

Included are the average salaries for tenured/tenure-track faculty from the 2018-19 Faculty in Higher Education Survey conducted by The College and University Professional Association for Human Resources (CUPA-HR). Findings detailed aggregate salary information from 847 institutions for 171,487 full-time tenure-track faculty in the US. Columns indicate the type of professor, with "All" referring to any type, "Research" being professors who primarily research, "Other Doctoral" being non-research professors with a doctoral degree, and "Master's" being non-research professors with a Master's degree.
statcrunch_featuredNov 8, 20197KB410
Celtics 2014-2015.csv
Pos -- Position height -- Height Wt -- Weight Exp -- Years experience in NBA/ABA (prior to this season) height.cat – categorical variable divided height into two categories Exp.cat – categorical variable divided height into two categories Rk -- Rank Age -- Age of Player at the start of February 1st of that season. G -- Games GS -- Games Started MP -- Minutes Played Variables below are in two forms “_tot” (for total) and “_per.game” (for per game). FG -- Field Goals FGA -- Field Goal Attempts FG% -- Field Goal Percentage 3P -- 3-Point Field Goals 3PA -- 3-Point Field Goal Attempts 3P% -- 3-Point Field Goal Percentage 2P -- 2-Point Field Goals 2PA -- 2-point Field Goal Attempts 2P% -- 2-Point Field Goal Percentage eFG% -- Effective Field Goal Percentage This statistic adjusts for the fact that a 3-point field goal is worth one more point than a 2-point field goal. FT -- Free Throws FTA -- Free Throw Attempts FT% -- Free Throw Percentage ORB -- Offensive Rebounds DRB -- Defensive Rebounds TRB -- Total Rebounds AST -- Assists STL -- Steals BLK -- Blocks TOV -- Turnovers PF -- Personal Fouls PTS -- Points
swhardyJan 31, 20166KB1315
American Vacation Stops
This Pareto graph shows the top vacations spots surveyed from a sample of 3000 people. I used a Pareto because a pie chart does not disciminate the close percentages enough to accurately read the data. The data is categorical because the variables can only be measured in categories The graph illustrates people's choice vacation stop by starting from the America destinations with the highest preference to the lowest. I'm not exactly sure how accurate this data due to the fact that I am unaware of the sample that was surveyed. The link to both the Pareto graph and the pie chart are below http://www.chartgo.com/trans.jsp?filename=chartgo&img=chart1&id=7AA882731C4A22B604345742A8D62B1D_4951 (Pareto graph) http://www.chartgo.com/trans.jsp?filename=chartgo&img=chart1&id=3FAE32B6E8FD6ACE29577C2262E3E142_5112 (Pie Chart)
astra028May 5, 2013431B1385
GunsAndGunDeathsByCountry
This data set contains data regarding gun ownership and gun deaths in various categories for 73 different countries. The data were obtained on 8/28/16 from Wikipedia. The Wikipedia pages have more information about the sources for the data values for each country and the dates on which the original data were collected. A. Variables obtained from https://en.wikipedia.org/wiki/Estimated_number_of_guns_per_capita_by_country Guns/100: total number of guns per 100 population B. Variables obtained from https://en.wikipedia.org/wiki/List_of_countries_by_firearm-related_death_rate The dates on which data was obtained for the various countries range from 1995 to 2016. Country: name of country Total gun deaths/100,000: total number of gun deaths in one year per 100,000 population (sum of gun homicides/100,000, gun suicides/100,000, unintentional gun deaths/100,000, and undetermined gun deaths/100,000). Gun homicides/100,000: number of gun homicides in one year per 100,000 population. Includes justifiable gun homicides as well as unjustified gun homicides. Gun suicides/100,000: number of gun suicides in one year per 100,000 population. Unintentional gun deaths/100,000: number of unintentional gun deaths in one year per 100,000 population. Undetermined gun deaths/100,000: number of gun deaths in one year per 100,000 population that could not be categorized as homicide, suicide, or unintentional. C. Categorical variables with values calculated from the variables above: Relative guns per person higher – Guns/100 is greater than the median of 10.7 guns/100 population lower – Guns/100 is less than or equal to the median of 10.7 guns/100 population Relative total gun death rate higher – Total gun deaths/100,000 is greater than the median of 1.83 total gun deaths/100,000 population lower – Total gun deaths/100,000 is less than or equal to the median of 1.83 total gun deaths/100,000 population Relative gun homicide rate higher – Gun homicides/100,000 is greater than the median of 0.36 gun homicides/100,000 population lower – Gun homicides/100,000 is less than or equal to the median of 0.36 gun homicides/100,000 population Relative gun suicide rate higher – Gun suicides/100,000 is greater than the median of 0.81 gun suicides/100,000 population lower – Gun suicides/100,000 is less than or equal to the median of 0.81 gun suicides/100,000 population Relative unintentional gun death rate higher – Unintentional gun deaths/100,000 is greater than the median of 0.06 unintentional gun deaths/100,000 population lower - Unintentional gun deaths/100,000 is less than or equal to the median of 0.06 unintentional gun deaths/100,000 population
anderson_instructorSep 1, 20175KB3319
Height Difference of Men and Women by country
This data is about the difference in heights of men and women by country all across the world. The quantitative data being observed is the average height of Men and Women by country, The year at which the average heights were obtained, and the population/age ranges. The categorical data being observed is the country and methodology. The reason for why I chose this set of data is because it was interesting and I thought why not do an observation on something I know not much about.
pv31283923Feb 11, 20117KB1392
J Pribe auto-mpg.xlsx
The data set covers 12 years of vehicles and contains 398 individual entries. The data describes popular consumer vehicle’s miles per gallon (MPG), the number of engine cylinders, total engine size (displacement), engine horsepower, the vehicle weight, a measure of acceleration (0-60 MPH time), the model year of the vehicle (1970-1982), a coded identifier for the place of origin, and the make and model of the vehicle. MPG, number of cylinders, engine size, horsepower, weight, acceleration time, and model year are all numerical values. The vehicle origin, full name, make, and model are categorical. This data was chosen to meet the assignment requirements, and because cars are cool. *Origin data code: 1=USA, 2=Europe, 3=Japan. The "car name" variable was broken into additional make and model variables to ease analysis, a change from the original data set.
jpribeFeb 16, 201932KB509
2018 Vehicles Dataset
This data is taken from the website https://www.fueleconomy.gov/feg/download.shtml. It's vehicles that were made in 2018. The variables include FE Rating, Tailpipe CO2 emissions, annual fuel cost, etc. Both categorical and quantitative variables are present.
habarkerFeb 12, 2019133KB672
cell_phones.xls
Q1. Based on a recent study, roughly 80% of college students in the U.S. own a cell phone. Do the data provide evidence that the proportion of students who own cell phones in this university is lower than the national figure? Answer. Most likely not. Ownership of cellphones and ratios do not depend on anything. Relevant Variables - The cell is the relevant variable and it is categorical. Analyze Data - The formal analysis of Q1 will pinpoint on searching the population proportion. The correct statistical test is the one sample z-test for the proportion. Null Hypothesis - Ho: p = 8 Alternative Hypothesis - Ha: p < .8 Outcomes: Cell Success: yes Test stat z = -.71, p-value is .239 > .05, so Ho cannot be rejected. Roughly 78% of the students sampled own a cellphone. Even though 78% percent is less than 80%, there is not enough support to conclude that the exact data holds right for the whole college or that it would be different from the national proportion.
faithnwanneMay 3, 20198KB551
Guatemalan Family Planning Data v2
BEST TO USE THIS VERSION BECAUSE SOME PROBLEMS HAVE BEEN FIXED. DO NOT CHOOSE age.cat - IT HAS TOO MANY CATEGORIES. This demographic and family planning data was collected by University of Maine at Farmington students in Comalapa, Guatemala in 2007 in partnership with Women’s International Network for Guatemalan Solutions (WINGS). WINGS is non-governmental organization whose mission is to create opportunities for Guatemalan families to improve their lives through family planning education and access to reproductive health services. Demographic data: gender, age.cat, civil.status, occ.place (occupation place), occ.type (occupation type), edu (education), religion, numchildren, pregtimes (pregnancy times – numeric), preg.times.cat (pregnancy times – categorical ),Amenities: car,television,phone,bicycle, Sexual Activity: firstsex (age of first sexual experience – numeric), firstsex.cat(age of first sexual experience – categorical) , everbeenpregnant, fagefirstpreg (age at first pregnancy, females only), mageatfirstchild (age at first child, males only), current.partner, Birth Control Method Awareness: awarebirthcontrol, awareCondoms, awareFemalesterilization, awareMalesterilization, awarePill, awareIUD, awareInjection, awareRhythm, agefirstlearn (age when first learned about birth control), Birth Control Information Obtained from: Healthpromoter, School, Media, Other.obtained (information obtained from another source), Birth Control Usage: everusedbirthcontrol, currusingbirthcontrol, Other Family Planning Data: comfortable (comfortable visiting family planning clinic) , delaying (opinion on delaying a first pregnancy) , spacing.pregnancies (opinion on spacing pregnancies), howlongwaitbetween (how long would they like to wait between pregnancies), limiting (opinion on limiting family size), howmanychildren (how many children would they like to have), pregplans12 (plan to become pregnant within the next 12 months).
swhardyMar 31, 201687KB452
edit#gid=0
As part of the Math Leadership Corp (MLC) collaborative process, Teachers continually use student formative and summative data to improve their instructional practice and influence their colleagues through research informed coaching, co-planning, classroom observations, demonstrations and critical reflection of practice. Being part of the MLC program, Ms. Garcia’s has researched the role that questioning plays in the classroom from a students’ perspective. Using research she read from Make One Change, teach students to ask questions, she has decided to teach “students, rather than teachers, assume responsibility for posing questions”. She will gather categorical data to see if 80% of 8th grade students feel that asking questions in a math classroom is helpful and whether their is a relationship between asking questions and higher performance on math assessments. The first and second column: I found that posing and asking questions is helpful Helpful (1) just another thing to do (2) not helpful (0) I found that taking notes after discussing the questions with my peers was... Helpful (1) just another thing to do (2) not helpful (0) Third column are Interim scores Fourth column Interim scale Fifth column-- students who asked questions on Interim Sixth column-- students grades on in class assessment Seventh column -- students who asked questions on class assessment
ninibb1Jun 21, 201662KB306
Guatemala Family Planning Data
This demographic and family planning data was collected by University of Maine at Farmington students in Comalapa, Guatemala in 2007 in partnership with Women’s International Network for Guatemalan Solutions (WINGS). WINGS is non-governmental organization whose mission is to create opportunities for Guatemalan families to improve their lives through family planning education and access to reproductive health services. Demographic data: gender, age.cat, civil.status, occ.place (occupation place), occ.type (occupation type), edu (education), religion, numchildren, pregtimes (pregnancy times – numeric), preg.times.cat (pregnancy times – categorical ),Amenities: car,television,phone,bicycle, Sexual Activity: firstsex (age of first sexual experience – numeric), firstsex.cat(age of first sexual experience – categorical) , everbeenpregnant, fagefirstpreg (age at first pregnancy, females only), mageatfirstchild (age at first child, males only), current.partner, Birth Control Method Awareness: awarebirthcontrol, awareCondoms, awareFemalesterilization, awareMalesterilization, awarePill, awareIUD, awareInjection, awareRhythm, agefirstlearn (age when first learned about birth control), Birth Control Information Obtained from: Healthpromoter, School, Media, Other.obtained (information obtained from another source), Birth Control Usage: everusedbirthcontrol, currusingbirthcontrol, Other Family Planning Data: comfortable (comfortable visiting family planning clinic) , delaying (opinion on delaying a first pregnancy) , spacing.pregnancies (opinion on spacing pregnancies), howlongwaitbetween (how long would they like to wait between pregnancies), limiting (opinion on limiting family size), howmanychildren (how many children would they like to have), pregplans12 (plan to become pregnant within the next 12 months).
swhardyMar 9, 201692KB301
Top US DVD Sales for Week Ending 8.16.09
The data is of the top US DVD sales for the week ending August 16, 2009. The categorical data for this chart is each movie's title and rank and previous, while the quantitative data is each movie's units this week, percent change, total unit sales this week, total sales, and weeks in release. Wow, don't we like buying movies.
helloausttinAug 24, 20092KB275
Housing_Prices_categorical_factors.xlsx
Price � Selling price of the house Lot Size in acres Waterfront (0 = No, 1 = Yes) Age in Years Land Value � Assessed value of the property without the structures New Construction (0 = No, 1 = Yes) Central Air (0 = No, 1 = Yes) Fuel Type (1 = None, 2 = Gas, 3 = Electric, 4 = Oil, 5 = Wood, 6 = Solar, 7 = Unknown/Other) Heat Type (1 = None, 2 = Forced Hot Air, 3 = Hot Water, 4 = Electric) Sewer Type (1 = None/Unknown, 2 = Private (Septic System), 3 = Commercial/Public Living Area � Size in square feet Pct College � The percent of the residents of the zip code that attended four-year college Bathrooms � Number of bathrooms Half Baths � Number of half bathrooms Bedrooms � Number of bedrooms Fireplaces � Number of fireplaces Rooms � Number of rooms.
erounderauNov 20, 201584KB13868
CPS wage data from 1985
These data consist of a random sample of 534 persons from the Current Population Survey, with information on wages and other characteristics of the workers. Source: Berndt, ER. The Practice of Econometrics. 1991. NY: Addison-Wesley.
ColumnDescription
EducationNumber of years of education
SouthIndicator variable for Southern Region: (1=Person lives in South, 0=Person lives elsewhere)
SexIndicator variable for sex (1=Female, 0=Male)
ExperienceNumber of years of work experience
UnionIndicator variable for union membership (1=Union member,0=Not union member)
Wage Wage in dollars per hour
AgeAge in years
RaceCategorical variable for race (1=Other, 2=Hispanic, 3=White)
OccupationCategorical variable for occupation (1=Management,2=Sales, 3=Clerical, 4=Service, 5=Professional, 6=Other)
SectorCategorical variable for sector (0=Other, 1=Manufacturing, 2=Construction)
MarrIndicator variable for marital status (0=Unmarried, 1=Married)
sampleuserMay 25, 200714KB1385
Exercise Data
Whitney Fraleigh, Britney McLeod, Tyler Ward This data set represents the results of StatCrunch survey administered between September 22nd, 2009 and October 2nd, 2009. Respondents provided the number of hours per week (Hours) that they exercise each week and the number of days per week (Days) that they exercise. Respondents also stated whether or not they were gym members (Gym), the type of exercise they do (Type - Cardio, Strength, Both or Neither), their age (Age) and their gender (Gender). Hours of exercise per week, Quantitative Days they exercise per week, Quantitative Whether or not gym members, Categorical Type of Exercise, Categorical Age, Quantitative Gender, Categorical We chose to use this data to better understand the exercise patterns of all ages of males and females.
whitneymarie19Feb 2, 20124KB697

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