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Showing 1 to 15 of 122 data sets matching GPA
Data Set/Description Owner Last edited Size Views
Graduate Admissions
The dataset contains several parameters which are considered important during the application for Masters Programs. The parameters included are : 1. GRE Scores ( out of 340 ) 2. TOEFL Scores ( out of 120 ) 3. University Rating ( out of 5 ) 4. Statement of Purpose and Letter of Recommendation Strength ( out of 5 ) 5. Undergraduate GPA ( out of 10 ) 6. Research Experience ( either 0 or 1 ) 7. Chance of Admit ( ranging from 0 to 1 )
brmorganteJan 15, 201912KB370
Class Seating vs Grade
From Body Image Data Set: "A student survey was conducted at a major university. Data were collected from a random sample of 239 undergraduate students". Variables: Gender - Male or Female, GPA - Student's cumulative college GPA. GPA is then converted to Grades (where, 4.33 = A+, 4.00 = A, 3.67 = A-, 3.33 = B+, 3.00 = B, 2.67 = B-, 2.33 = C+, 2.00 = C, 1.67 = C-). Seat - Typical classroom seat location (Front & Back)
mallirhea86Oct 26, 20182KB2633
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, 20169KB1994
Seating Choice versus GPA (For 3 rows, with Text and Indicator Columns)
This dataset contains hypothetical (I believe) data on GPA for students who sit in the front, middle, and back rows of a classroom, as well as a hypothetical gender variable. The data are shown using both text variables (e.g., "front" and "middle") and 0/1 indicator variables for the row and gender variables. This dataset is useful for demonstrating the different ways that StatCrunch can compare means based on two factors: (a) the text factor columns can be used in a two-way ANOVA; and (b) the 0/1 indicator columns can be used in multiple regression. (Because of StatCrunch's current limitation on equal cells, the 0/1 variables only use the first and middle rows.) Both procedures gives the same p-value and same conclusion (as long as the interaction term is centered), thus highlighting the similarity of statistical procedures and StatCrunch's flexibility.
bartonpoulsonApr 7, 20101KB5290
Seating Choice versus GPA (Stacked & Split Columns for Front & Back Rows)
This dataset contains hypothetical (I believe) data on GPA for students who sit in the front and back row of a classroom. The data are shown in several ways: (a) two separate columns (one for the front row GPA and another or the back row GPA); (b) stacked with one column to indicate front or back row and another column with the GPAs; and (c) the row column repeated as a 0/1 indicator variable. This dataset is useful for comparing the different ways that StatCrunch can compare the means of two groups: (a) The two columns of scores (front and back) can be used in the 2-sample t-test or a one-way ANOVA; (b) the stacked text column (front/back) with a separate column for GPA can also be used for one-way ANOVA; and (c) the 0/1 indicator column and stacked GPAs can be used with correlation and regression. Every procedure gives the same p-value and same conclusion, thus highlighting the similarity of statistical procedures and StatCrunch's flexibility.
bartonpoulsonApr 7, 2010465B2548
Body Image Data Set
A student survey was conducted at a major university. Data were collected from a random sample of 239 undergraduate students, and the information that was collected included physical characteristics (such as height, handedness, etc.), study habits, academic performance and attitudes, and social behaviors. In this exercise, we will focus on exploring relationships between some of those variables. Note that empty boxes signify that this observation is not available (this is known as a 'missing value').Variables: Variables Gender - Male or Female Height - Self-reported height (in inches) GPA - Student's cumulative college GPA HS_GPA - Student's high school GPA (senior year) Seat - Typical classroom seat location (F = Front, M = Middle, B = Back) WtFeel - Does the student feel that he/she is: Underweight, About Right, Overweight Cheat - Would the tell the instructor if he/she saw somebody cheating on exam? (No or Yes)
stjohn314Apr 8, 201810KB2451
Body Image Data Set
A student survey was conducted at a major university. Data were collected from a random sample of 239 undergraduate students, and the information that was collected included physical characteristics (such as height, handedness, etc.), study habits, academic performance and attitudes, and social behaviors. In this exercise, we will focus on exploring relationships between some of those variables. Note that empty boxes signify that this observation is not available (this is known as a 'missing value').Variables: Variables Gender - Male or Female Height - Self-reported height (in inches) GPA - Student's cumulative college GPA HS_GPA - Student's high school GPA (senior year) Seat - Typical classroom seat location (F = Front, M = Middle, B = Back) WtFeel - Does the student feel that he/she is: Underweight, About Right, Overweight Cheat - Would the tell the instructor if he/she saw somebody cheating on exam? (No or Yes)
33225049_ecollege_sacmlpJul 1, 20157KB7430
Intro Stats GPA
Grade point average for 24 students in intro stats class. Create a histogram for GPA (If not using StatCrunch, you may want to create a frequency distribution first.) Note: May need to try different bin widths (e.g. 0.5, 1). Is the data continuous or discrete? Comment on the shape of the distribution.
smcdaniel04Jan 23, 2012178B1647
TibbyCatt COC Students GPA Seating in Class 4 Row 2017 Fall
Randomly selected student data. Random Drawing plus last digit of year on drawn coin for systematic random sample data. Use 4 row, 3 row and 2 row entries with digits.
clstineNov 10, 20172KB167
GPA.xlsxeingram4Feb 20, 201914KB7
GPA.xlsxeingram4Feb 20, 201914KB1
GPA.xlsxeingram4Feb 20, 201914KB1
GPA.xlsxeingram4Feb 20, 201914KB0
GPA.xlsxeingram4Feb 20, 201914KB0
GPA.xlsxeingram4Feb 20, 201914KB0

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