Introduction to Engineering Computing
Introduction to Engineering Computing GEEN 1300
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This 27 page Class Notes was uploaded by Hudson Weimann Jr. on Friday October 30, 2015. The Class Notes belongs to GEEN 1300 at University of Colorado at Boulder taught by Staff in Fall. Since its upload, it has received 19 views. For similar materials see /class/232201/geen-1300-university-of-colorado-at-boulder in General Engineering at University of Colorado at Boulder.
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Date Created: 10/30/15
1222014 GEEN I300 INTRODUCTION TO ENGINEERING COMPUTING Announcements D No lob this week but yes there is lob next week El Do your homework El Last day to drop without fees is 1 week from today El Prelob for lab 2 is due next Monday by 10am will be posted by Thursday Announcements Previously in GEEN 1300 u Excel syntax and order of evaluation 1222014 Previously in GEEN 1300 V D Exel xymax and order of evalualion rmu o mm a NW or Exprewon are evomored e rvorng r on Exponen ohon Operawrprelte em 9 r Muhwphmnonwaswon AddmonSubvromon Previously in GEEN 1300 D Exel xymax and order of evalualion Expremon are evamared e rvorngm Operawrpreltedenlte Negonon Exponen ohon Muhwphmnonwaswon AddmonSubvromon a Cell name n m r rm m m 1 2L 1zzzn14 Previously in GEEN 1300 Excel symex and order of eveluerlen Formulas mus mm mm or Expressions are evaluated le eioerigm ruler recedence 39v Negu Ion Exponemimion ddilionSubm on Previously in GEEN 1300 A A r a Excel symex ml order of eveluerien Formulas musismnwilh e or Expressions are evaluated le eioerigm Opererer precedence Neg n Exponeniimion MulriprcerienD onA e IonSubtraction a Cell names e Dillerem ways of filling cells a Queslions aboul Iasl week 1zzzn14 CQl What will the value in the cell be after dragging with the fill handle a 3 la 4 CQQ What will the value in the cell be ft d th a er tagging wy the fillrhanrd le 3375 0 39c oil5n39n 1zzzn14 Case Study Plants vs Zombies Case Study Plants vs Zombies D Zombie speed Zsp imph a Regular zombie resistance ZN is 4 peas 1222014 Case Study Plants vs Zombies m Peaxhoolerx xhool pea al a rule of 12 peaxminule PM B Your from door ix 4 melerx from Ihe xlreel Hm Case Study Plants vs Zombies a How many peaxhoolerx P do you need to defend your home agaimn 10m Z 39 ert Zt 39 Prate 1zzzn14 Case Study Plants vsu Zombies a How many peashaoters F do you need to defend your nome against 1 zombie zombie mm ex r name P Z 39 ert Z1 39 Prate Case Study Plants vsu Zombies a How manypeashooters F do you need to defend your nome against 1 zombie Time it take zombie mm the yard 1zzzn14 Case Study Plants vsu Zombies a How many peashaoters F do you need to defend your nome against 1 zombie H Z1 2 dist Zip Case Study Plants vsu Zombies a How manypeashooters P do you need to defend is your nome against 1 zomb name 0 H houxe dis 1 Z Z Zom e 5p Speed 1zzzn14 1zzzn14 Case Study Plants vs Zombies Plants vs Zombies Plants vs Zombies I Additional Case Studies How does P change as zombie resistance varies How does P change as the number of zombies varies How does P change as zombie speed and resistance vary Several possible approaches Using Formula Copy Single formula with only one parameter changing Range of parameter values in adiacent column 1222014 1222014 Types of Graphs l El XY graphs Scatter plots E Used most of The Time 3 Line graphs El Column and pie graphs An interesting graph 12 Formatted more nicely myquot mum Fitting models to data a You have dam a You have or wanno find a model for n ex w hyllml model How to m me domZ How to evo uc e M0 M2 1zzzn14 The simplest model a line M y ax b Model Finding parameters for the best fit D Straightline model parameters are slope and intercept am w El Measure of goodness of fit is the sum of the squares of the errors Sum of squares of actual value expected value 71 A 2 SSE Zul Yr i1 I Find the minimum of SSE by adiusting the parameters 1222014 14 Fitting a straightline model a Minimizing SSE an be solved by solving for bexr ex of a and b 1 2 xi 2quot 90 unlian m2 n n a on Exel an do Ihix wiih buiIiin loolx Doio Anoiysis Regression iiTrendime Case Study Total Carbon Emissions uaaimn msmiuin u rquot n mi n m lm in in iquot m iii W q m i i n w w m39 n m in m 1zzzn14 Transfer data to Excel u MW mumquot lm coz Emissions fonhe us Calculating slope amp intercept with Excel formulas a 7 71211196134 211196061111 3 1 7 Z n21xiz 211 759 89 8amp1me Xi n n 5847a 2113 1 2139 71113119 b tl39 n n 1222014 Calculating slope amp intercept with Excel formulas 19 A I 379818u Yi a XL b 1999 1059035 Sumy quot n n 15 vgy 219634 Zi1xizi1yi 5924589 811me 5847a 711131194b Calculating slope amp intercept with Excel formulas 75924589 8amp1me 5847a 711131194b 1222014 Calculating slope amp intercept with Excel formulas 19 n A 379813umx Yi a XL b 712111 X134 211 9606111 3 1 5924589 811me 5847a 711131194b Calculating slope amp intercept with Excel formulas 37931Sumx Yi a xi b 1999 Avgx 105908 5 Sumv 557415 Avgy n i1 i i 211745020 4 Sumxv 1 Z n z n 75924589 Sumx2 1121 xi Ziqxl 5547 a n 7111311945 2 lyl b a n 1222014 Calculating slope amp intercept with Excel formulas 19 n 37981 Sumx 59245 9 811me 5847a 711131194b Calculating slope amp intercept with Excel formulas 37931 Sumx Yi a xi b 1999 Avgx in1xi37i 21119606 3 1 Z n 2111 xiz 2111 75924589 Su 5847a 711131194b 3913 z n 1222014 Calculating slope amp intercept with Excel formulas 19 n 37981 Sumx 1999 Avgx 105908 8 Sumy 211745020 4 Sume 1 5924589 Sume rrziquoti 5847a 711131194b Calculating slope amp intercept with Excel formulas 37981Sumx Yi a xi b 1999 Avgx 105908 8 Sumv quot n 5 Avgy 7 7121496134 21519606 3 1 55741 1 211745020 4 Sumxv 2 75924589 Sumx2 112111 xiz 21li 58 47 3 7111311945 lyl n 1222014 a 1999 1 5847 c 557415 11 41131194 37iaxib CQ3 Which of value is the slope 19 1 37981 Sum 55 211745020 4 Sumxy 75924589 Sumx2 5847a 7111311945 a 1999 b 5847 c 557415 d 41131194 C04 Which of value is the intercept 19 1 37981 Sumx 211745020 4 Sumxy 75924589 Sumx2 5847a 7111311945 1222014 1222014 VNNAAAAAQJAAA uaommwmwAQNAUQGVG mbu N A 5 c D E fSIralthme LmearF coz Vear Emlssr 005 1990 5007 COUNTx 1991 4960 esuwx Sumx 1992 5053 AVERAGEX Avgx 1993 5164 Sumy 1994 5242 Avgy 1995 5296 Surnxy 1990 5484 sumsmx Sume 1097 5566 1990 5003 In SumxySumx39Surnyn Surnx2SumxA2 a 1999 5665 gyra39Avgx b 2000 50445 2001 57496 2002 58111 39 2003 58644 the formulas behind 2004 59632 2005 59717 2006 5885 the numbers 2007 59569 2005 58016 redlded C02 a A3b C02 Emissions for the US Add the line as a series to your plot 1zzzn14 co high 5 m us Data with the line we calculated A Better Way Excel s Trendline m2 mm mm L 5 The Add Trendline dialog box Defaull ix linear whkh i whal we wan Dixplay equalion oplion Iniiiul form of graph Wiquot1 lrendline added co smigim mm us 1zzzn14 Initial form of graph with trendline added 1 CO2 Emissions for the US Initial form of graph with trendline added I l CO2 Emissions for the US 5000 5847x 1 1 131 194 m 5500 mm c 5250 Yntnl co Emiss 5000 4750 4500 1222014 25 But how is the fit Goodnexx of filquot melrk Need to force cm intercept Chedlt Se Inleneplquot 1zzzn14 CQ5 If we forced the regression line to go Through the origin what would happen to the Rsquared value 6250 6000 3 5750 5500 Ynhl co Emis 5 H m 4750 4500 1990 i992 i994 1990 200a 2010 a No change b It would be higher a better fit c It would be lower worse fit Next time D Data Analysis Regression toolkit D Reading regression output El Judging fit D Fitting and transforming nonlinear models 1222014 27
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