MTE.1 CSE 4701 CSE4701 Midterm Exam Statistics (Spr15) Worry a lot! Notes: Final Exam 120 points MT - range from 40-50%; FE - range from 60-50% Track Performance from MT to Final Exam Differential from MT to Final If Increase - Weighted Average If Stay Same - that is your Performance If Decrease - that is your Performance Worry

MTE.2 CSE 4701 CSE4701 Grade Guesstimates (Spr15) Notes: All Subject to Change Used 20%HW, 80%E From Intro Overheads Must Pass Both Projects And Exams to PASS!!!

MTE.3 CSE 4701 Problem 1a - Relational Algebra (Fa15)  List the full names of all mens players that have/had the uniform number 5. 3pts PL =  PLName, PFName (  UniformNumber=5 ( PLAYER )) MT =  TeamID (  Squad=‘Mens’ ( TEAM )) ANS1a =  PLName, PFName ( PL * ( MT * ROSTERS )) ½ pt 1 pt

MTE.4 CSE 4701 Problem 1b - Relational Algebra (Fa15)  List the full names and points per game of all mens players that averaged more than 20 points per game and at least 5 rebounds per game in one year on teams with winning regular season records. 4pts MT =  TeamID (  Squad=‘Mens’ ( TEAM )) WR =  TeamID (  Wins > Losses ( RSRECORD * MT )) ANS1b =  PLName, PFName, PPG ( PT * PLAYER ) PL = (  PPG > 20 and RPG  5 ( STATISTICS * WR )) 1 pt

MTE.5 CSE 4701 Problem 1c - Relational Algebra (Fa15)  List the full names and uniform numbers of all womens players that played for Geno Auriemma in pts ANS1c =  PLName, PFName, UniformNumber ( PLAYER*GAP ) GAP = (  CLName=‘Auriemma’ ( ROSTERS * WT )) WT =  TeamID (  Squad=‘Womens’ and Year =1996 ( TEAM )) 1 pt 1.5 pts

MTE.6 CSE 4701 Problem 1d - Relational Algebra (Fa15)  List the full names of all mens players who played on a team with a losing regular season record coached by Jim Calhoun. 4pts ANS1d =  PLName, PFName ( PLAYER*MP ) MP = (  PlayerPersonID  CLName=‘Calhoun’ ( ROSTERS * MLT )) MT =  Squad=‘Mens’ and Wins < Losses ( TEAM * RSRECORD )) 1 pt 1.5 pts

MTE.7 CSE 4701 Problem 3 – Relation to EER Person PersonId Lname Fname StartYr Player NumYrs Uniform# Coach EndYear   o Record Wins Losses RSRecord PORecord   d Team TeamID Year Squad Titles TitleType Statistics RPG PPG APG Rosters 1 n m k 1 n n Team – 2 pts Person Hier – 3 pts Record Hier – 3 pts Rosters Relat – 3pts Titles Relat – 2pts Stats Relat – 3pts has 1 1 Deductions for Misplaced attributes Missing Attributes Missing 1, n, m, k Wrong d and o

MTE.8 CSE 4701 Problem 3 - Update Anomalies (Fa15) No Modify Anomaly - only one entry per player/coach (unique LNames) Insert Anomaly - Player in past can’t be coach in future - Player or coach leaves for 1 (or more years) and then returns - no way to store his/her return. No Delete Anomaly-only one entry per player/coach (unique LNames) Note: Lots of null values due to capturing two types of people. 4pts 2pts if mention guideline 1 (not single entity) 2pts if mention null values 2pts if player/coach insert anomaly Up to at most 4pts

MTE.9 CSE 4701 Problem 3 - Update Anomalies (Fa15) There are numerous problems for this table, since many values are replicated. For Example, teams that win multiple titles (NCAA and BigEastRS) must have each player and coach listed twice to capture this data. If you insert a player for a past team (that was omitted), you would have to make sure you inserted the player for all Titles of TeamID. Specifically: Insert: Can’t have a Team without having a player. Can’t have TitleType unless there is a Team with that title. Delete: Last Player on a team - loose the team. Modify: Change PLName, impact all TeamIDs for the player Basically - I looked for reasoning and a solid argument for this table. 6pts 2pts per issue – up to three issues – including others if the argument is OK.

MTE.10 CSE 4701 Problem 3 - Update Anomalies (Fa15) Modify Anomaly - Whenever RSWins or RSLosses is modified (for a win or a loss), TTLWins or TTLLosses must be incremented. No Insert Anomaly - only one entry per team. No Delete Anomaly - no information is lost on anything but the team. No Modify Anomaly - PPG, RPG, and APG are independent of one another and no values in common across different players. No Insert Anomaly - only one entry per player. No Delete Anomaly- no information is lost on anything but the player. 2pts 3pts Looking for the key anomaly re. dependencies among totals Accepted other answers for partial credit Need to give at least a reason or only 1pt if just say NONE.

MTE.11 CSE 4701 Problem 4 - Functional Dependencies (Fa15) LName  FName, StartYear LName, PFlag  NumYears, UniformNumber LName, CFlag  EndYear Year, Squad  CLName TeamID  Year, Squad TeamID, Year, Squad  TitleType TeamID, Year, Squad  PLName CLName  TitleType CLName  TeamId, Year, Squad PLName  TitleType a. 4pts a. 2pts b. 2pts b. 3pts

MTE.12 CSE 4701 Problem 4 - Functional Dependencies (Fa15) TeamID  RSWins, RSLosses, TTLWins, TTLLosses PLName, TeamID  PPG, RPG, APG PLName  PPG, RPG, APG PLName  TeamID TeamID  PLName a. 1 pts b. 3pts

MTE.13 CSE NF – Look at NBAPLAYER2 TABLE For Team  State – State depends on Team which is NOT part of a key Transitive from Name,Year to Team to State 3pts NBAPLAYER2a(Name, Year, Team) 3pts NBAPLAYER2b(Team, State) Problem 5 (Fa15) - Normalization NBAPLAYER( Name, Year, Coach, Team, State, Salary) 2NF Team Depends on {Year, Name} which is part of key Name, Year, Coach Salary Depend on Coach which is part of key Name, Year, Coach 5pts NBAPLAYER1(Name, Year, Coach, Salary) 4pts NBAPLAYER2(Name, Year, Team, State) Need to Not Break out Coach, Salary into one table, since Coach maps to different salaries (Clinton has 800,000, 700,000, and 1,000,000) Name, Year  Team; Team  State Name, Year, Coach  Team, State, Salary Coach  Salary Name, Year  Team; Team  State