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Ilya Lyalin

IE 634 Final

1

n

Part

Type M1 M2 M3 M4 X bar X1 X2 X3 X4

Avg.

Dev. Max M

1 A 120 95 100 110 106.25 20 -5 0 10 6.25 20 -

2 A 115 123 99 102 109.75 15 23 -1 2 9.75 23 -

3 A 116 105 114 108 110.75 16 5 14 8 10.75 16

4 A 120 116 100 96 108 20 16 0 -4 8 20 -

5 A 112 100 98 107 104.25 12 0 -2 7 4.25 12 -

6 A 98 110 116 105 107.25 -2 10 16 5 7.25 16 -

7 B 230 210 190 216 211.5 30 10 -10 16 11.5 30 -1

8 B 225 198 236 190 212.25 25 -2 36 -10 12.25 36 -1

9 B 218 230 199 195 210.5 18 30 -1 -5 10.5 30 -

10 B 210 225 200 215 212.5 10 25 0 15 12.5 25

11 B 190 218 212 225 211.25 -10 18 12 25 11.25 25 -1

12 C 2150 2230 1900 1925 2051.3 150 230

-

100 -75 51.25 230 -1

13 C 2200 2116 2000 1950 2066.5 200 116 0 -50 66.5 200 -5

14 C 1900 2000 2115 1990 2001.3 -100 0 115 -10 1.25 115 -1

15 C 1968 2250 2160 2100 2119.5 -32 250 160 100 119.5 250 -316 C 2500 2225 2475 2390 2397.5 500 225 475 390 397.5 500 2

17 C 2000 1900 2230 1960 2022.5 0 -100 230 -40 22.5 230 -1

18 C 1960 1980 2100 2150 2047.5 -40 -20 100 150 47.5 150 -4

19 C 2320 2150 1900 1940 2077.5 320 150

-

100 -60 77.5 320 -1

20 C 2162 1950 2050 2125 2071.8 162 -50 50 125 71.75 162 -5

Ta 100

Tb 200

Tc 2000

Mbar Xi^-s UCL LCL

106.25 0.3233 0.18 -0.18

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109.75 0.5043 0.18 -0.18

110.75 0.556 0.18 -0.18

108 0.4138 0.18 -0.18

104.25 0.2198 0.18 -0.18

107.25 0.375 0.18 -0.18

211.5 0.3177 0.18 -0.18

212.25 0.3384 0.18 -0.18

210.5 0.2901 0.18 -0.18

212.5 0.3453 0.18 -0.18

211.25 0.3108 0.18 -0.18

2051.3 0.1842 0.18 -0.18

2066.5 0.239 0.18 -0.18

2001.3 0.0045 0.18 -0.18

2119.5 0.4295 0.18 -0.18

2397.5 1.4287 0.18 -0.18

2022.5 0.0809 0.18 -0.182047.5 0.1707 0.18 -0.18

2077.5 0.2786 0.18 -0.18

2071.8 0.2579 0.18 -0.18

Part

Type R Ri^s UCL LCL CL

A 25 1.2931 1.585 0.415 1

A 24 1.2414 1.585 0.415 1

A 11 0.569 1.585 0.415 1

A 24 1.2414 1.585 0.415 1

A 14 0.7241 1.585 0.415 1

A 18 0.931 1.585 0.415 1

B 40 1.105 1.585 0.415 1

B 46 1.2707 1.585 0.415 1

B 35 0.9669 1.585 0.415 1

B 25 0.6906 1.585 0.415 1

B 35 0.9669 1.585 0.415 1

C 330 1.1861 1.585 0.415 1

C 250 0.8986 1.585 0.415 1

C 215 0.7728 1.585 0.415 1

C 282 1.0136 1.585 0.415 1

C 275 0.9884 1.585 0.415 1

C 330 1.1861 1.585 0.415 1

C 190 0.6829 1.585 0.415 1

C 420 1.5096 1.585 0.415 1

C 212 0.762 1.585 0.415 1

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Avg.

Ra 19.3333

Avg.

Rb 36.2

Avg. Rc 278.222

X Bar Chart

R Chart

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1 3 5 7 9 11 13 15 17 19

Xi^-s

UCL

LCL

CL

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0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 2 3 4 5 6 7 8 9 1011121314151617181920

Ri^s

UCL

LCL

CL

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2. A

18161412108642

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

Lag

Autocorrelation

Autocorrelation Function for C1(with 5% significance limits for the autocorrelations)

1.0

Partial Autocorrelation Function for C1(with 5% significance limits for the partial autocorrelations)

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Molecular Weight Measurements

X Regression (From Highlighted Equation) Residuals

2048 0 2048

2025 2034.9148 -9.9148

2017 2018.6515 -1.6515

1995 2012.9947 -17.9947

1963 1997.4385 -34.4385

1943 1974.8113 -31.8113

1940 1960.6693 -20.6693

1947 1958.548 -11.5481972 1963.4977 8.5023

1983 1981.1752 1.8248

1935 1988.9533 -53.9533

1948 1955.0125 -7.0125

1966 1964.2048 1.7952

1954 1976.9326 -22.9326

1970 1968.4474 1.5526

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2039 1979.761 59.239

2015 2028.5509 -13.5509

2021 2011.5805 9.4195

2010 2015.8231 -5.8231

2012 2008.045 3.955

2003 2009.4592 -6.4592

1979 2003.0953 -24.0953

2006 1986.1249 19.8751

2042 2005.2166 36.7834

2000 2030.6722 -30.6722

2002 2000.974 1.026

2010 2002.3882 7.6118

1975 2008.045 -33.045

1983 1983.2965 -0.2965

2021 1988.9533 32.0467

2051 2015.8231 35.17692056 2037.0361 18.9639

2018 2040.5716 -22.5716

2030 2013.7018 16.2982

2023 2022.187 0.813

2036 2017.2373 18.7627

2019 2026.4296 -7.4296

2000 2014.4089 -14.4089

1986 2000.974 -14.974

1952 1991.0746 -39.0746

1988 1967.0332 20.96682016 1992.4888 23.5112

2002 2012.2876 -10.2876

2004 2002.3882 1.6118

2018 2003.8024 14.1976

2002 2013.7018 -11.7018

1967 2002.3882 -35.3882

1994 1977.6397 16.3603

2001 1996.7314 4.2686

2013 2001.6811 11.3189

2016 2010.1663 5.8337

2019 2012.2876 6.7124

2036 2014.4089 21.5911

2015 2026.4296 -11.4296

2032 2011.5805 20.4195

2016 2023.6012 -7.6012

2000 2012.2876 -12.2876

1988 2000.974 -12.974

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2010 1992.4888 17.5112

2015 2008.045 6.955

2029 2011.5805 17.4195

2019 2021.4799 -2.4799

2016 2014.4089 1.5911

2010 2012.2876 -2.2876

2000 2008.045 -8.045

2009 2000.974 8.026

1990 2007.3379 -17.3379

1986 1993.903 -7.903

1947 1991.0746 -44.0746

1958 1963.4977 -5.4977

1983 1971.2758 11.7242

2010 1988.9533 21.0467

2000 2008.045 -8.045

2015 2000.974 14.0262032 2011.5805 20.4195

(Regression Equation(Minitab) =586.774+.7071Xt-1

The data is serially correlated, because the largest value (2048) is also the first value and the rest of the

data is dependent on this lag point.

B. The regression equation accurately predicts what the true values, because they are similar to the

original values, therefore it is an appropriate model.

C.

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81736557494133251791

50

25

0

-25

-50

Observation

Individu

alValue

_

X=-0.9

UC L=57.4

LCL=-59.3

81736557494133251791

80

60

40

20

0

Observation

MovingRange

__

MR=21.93

UC L=71.66

LCL=0

1

1

1

I-MR Chart of C1

According to the residual control chart, the process is in control. There is one point that falls out of

control, but this can be overlooked since it is only 1 out of 75 observations.

3. A

Run P I L W F T C M y

1 -1 -1 -1 -1 -1 -1 -1 -1 236

2 1 -1 -1 -1 -1 1 1 1 185

3 -1 1 -1 -1 1 -1 1 1 259

4 1 1 -1 -1 1 1 -1 -1 318

5 -1 -1 1 -1 1 1 1 -1 180

6 1 -1 1 -1 1 -1 -1 1 195

7 -1 1 1 -1 -1 1 -1 1 246

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8 1 1 1 -1 -1 -1 1 -1 229

9 -1 -1 -1 1 1 1 -1 1 196

10 1 -1 -1 1 1 -1 1 -1 203

11 -1 1 -1 1 -1 1 1 -1 230

12 1 1 -1 1 -1 -1 -1 1 261

13 -1 -1 1 1 -1 -1 1 1 168

14 1 -1 1 1 -1 1 -1 -1 197

15 -1 1 1 1 1 -1 -1 -1 220

16 1 1 1 1 1 1 1 1 241

PxY IxY LxY WxY FxY TxY CxY MxY

-236 -236 -236 -236 -236 -236 -236 -236

185 -185 -185 -185 -185 185 185 185

-259 259 -259 -259 259 -259 259 259

318 318 -318 -318 318 318 -318 -318

-180 -180 180 -180 180 180 180 -180195 -195 195 -195 195 -195 -195 195

-246 246 246 -246 -246 246 -246 246

229 229 229 -229 -229 -229 229 -229

-196 -196 -196 196 196 196 -196 196

203 -203 -203 203 203 -203 203 -203

-230 230 -230 230 -230 230 230 -230

261 261 -261 261 -261 -261 -261 261

-168 -168 168 168 -168 -168 168 168

197 -197 197 197 -197 197 -197 -197

-220 220 220 220 220 -220 -220 -220241 241 241 241 241 241 241 241

94 444 -212 -132 60 22 -174 -62

11.75 55.5 -26.5 -16.5 7.5 2.75 -21.75 -7.75

Graph of Effects

99

95

90

80

70

60

50

40

30

20

10

Percent

Mean 0.625

StDev 26.15

N 8

AD 0.409

P-Value 0.258

Probability Plot of C1Normal - 95% CI

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Flight Time

Avg 222.75

1 .5*effectP 5.875

2 .5*effectI 27.75

3 .5*effectL -13.25

4 .5*effectW -8.25

5 .5*effectF 3.75

6 .5*effectT 1.375

7 .5*effectC -10.875

8 .5*effectM -3.875

From the probability plot, it shows that Wing length is the most significant effect, with bodylength being the second most significant. Therefore, these are used in the regression equation.

Therefore the regression equation is y=222.275+27.75(I)-13.25(L)

B. Three experimental points in the path of steepest ascent are: Wing Length(I), Body Length(L),

and Taped Body (T). In order to increase flight time, Wing length should be increased and body lengthshould be decreased, because they are significant and will increase the flight time. Taped body is not

significant and therefore it is irrelevant in increasing flight time.