-
Non-linear regressionAll regression analyses are for finding the
relationship between a dependent variable (y) and one or more
independent variables (x), by estimating the parameters that define
the relationship.Non-linear relationships whose parameters can be
estimated by linear regression: e.g, y = axb, y = abx, y =
aebxNon-linear relationships whose parameters can be estimated by
non-linear regression, e.g,
Non-linear relationships that cannot be represented by a
function: loess
-
Commonly Encountered
Funtions0246810121416135XYy=x1.5y=x0.5y=x105101520250246810XYy=exy=100e-xy=1000.5x
-
Growth curve of E. coliA researcher wishes to estimate the
growth curve of E. coli. He put a very small number of E. coli
cells into a large flask with rich growth medium, and take samples
every half an hour to estimate the density (n/L).14 data points
over 7 hours were obtained.What is the instantaneous rate of growth
(r). What is the initial density (N0)?As the flask is very large,
he assumed that the growth should be exponential, i.e., y = aebx
(Which parameter correspond to r and which to N0?)Three
approachesLog-Transform to linear relationshipDirect least-square
solution (EXCEL solver)Direct least-absolute-difference solution
(EXCEL solver)
-
Scatter plotIn EXCEL: Log-transform D Run linear regression
Obtain D0 and r
Chart1
20.023
39.833
80.571
161.102
317.923
635.672
1284.544
2569.43
5082.654
10220.777
20673.873
40591.439
81374.642
163963.873
Density
Time
Density
Sheet1
TimeDensityPredSSa9.5549148261TimeDensityPredSSa9.5549561681
120.02319.17212548610.7239874384b0.696401763120.02319.17318979330.8498102067b0.6964529479
239.83338.46924879461.8598173504239.83338.47335355421.3596464458
380.57177.189308190611.4358394936380.57177.20149592533.3695040747
4161.102154.881872811438.68998224254161.102154.91425681736.1877431827
5317.923310.773539598551.11478403235317.923310.85442940727.0685705928
6635.672623.5732507716146.37973289116635.672623.767484461211.9045155388
71284.5441251.21205486231111.018566663771284.5441251.665853412332.8781465877
82569.432510.58172282963463.119725920182569.432511.620832483257.8091675168
95082.6545037.53185761942036.007733013395082.6545039.874810810142.7791891899
1010220.77710107.90725741812739.57879053211010220.77710113.1260658979107.6509341021
1120673.87320281.7157314902153787.3232450441120673.87320293.2260550147380.6469449853
1240591.43940695.66355697410862.75827643051240591.43940720.8434895903129.4044895903
1381374.64281656.653424593579530.44360122871381374.64281711.3597418362336.7177418362
14163963.873163845.68933663513967.378286363514163963.873163963.8509100780.0220899223
277747.8323686451118.6484937721
LWPredSSW = a*L^ba19.5266131121
0.31.6571.16500783780.2420562877b2.3414569542
0.42.52.28490170030.0462672785
0.54.683.85281489950.6842351904
0.67.0755.90442518381.3702454002
0.710.078.47092136212.5570524902
0.811.98811.58020633350.1662956744
0.914.83615.25765276760.1777910564
118.31819.52661311211.4607456548
1.123.49624.4087821910.8331713282
1.227.89729.92446541584.1106160121
1.336.79636.09278347680.4945134785
1.444.61142.93183255732.8196033007W2/W1L2/L1
1.550.18350.45881238480.07607247161.124901930.11769585851.07142857140.0689928715
251.859219222515.03866562391.7059133212
Sheet1
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Time
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Sheet2
00
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00
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W
Pred
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
Sheet3
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SE
Pred
MBD089B4B7A.unknown
-
EXCEL solverGet initial value for r:Initial value for D0 is
obtained with t = 0
Sheet1
TimeDensityPredSSa9.5549148261PredSADa9.5549561681
120.02319.1720.724b0.69640176319.1730.850b0.6964529479
239.83338.4691.86038.4731.360
380.57177.18911.43677.2013.370
4161.102154.88238.690154.9146.188
5317.923310.77451.115310.8547.069
6635.672623.573146.380623.76711.905
71284.5441251.2121111.0191251.66632.878
82569.432510.5823463.1202511.62157.809
95082.6545037.5322036.0085039.87542.779
1010220.77710107.90712739.57910113.126107.651
1120673.87320281.716153787.32320293.226380.647
1240591.43940695.66410862.75840720.843129.404
1381374.64281656.65379530.44481711.360336.718
14163963.873163845.68913967.378163963.8510.022
277747.8321118.648
LWPredSSW = a*L^ba19.5266131121
0.31.6571.16500783780.2420562877b2.3414569542
0.42.52.28490170030.0462672785
0.54.683.85281489950.6842351904
0.67.0755.90442518381.3702454002
0.710.078.47092136212.5570524902
0.811.98811.58020633350.1662956744
0.914.83615.25765276760.1777910564
118.31819.52661311211.4607456548
1.123.49624.4087821910.8331713282
1.227.89729.92446541584.1106160121
1.336.79636.09278347680.4945134785
1.444.61142.93183255732.8196033007W2/W1L2/L1
1.550.18350.45881238480.07607247161.124901930.11769585851.07142857140.0689928715
251.859219222515.03866562391.7059133212
Sheet1
0
0
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Density
Time
Density
Sheet2
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W
Pred
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
xxia:Sum of Absolute Difference
Sheet3
00
00
00
00
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SE
Pred
MBD089B4B7A.unknown
-
Body weight of wild elephantA researcher wishes to estimate the
body weight of wild elephants. He measured the body weight of 13
captured elephants of different sizes as well as a number of
predictor variables, such as leg length, trunk length, etc. Through
stepwise regression, he found that the inter-leg distance (shown in
figiure) is the best predictor of body weight.He learned from his
former biology professor that the allometric law governing the body
weight (W) and the length of a body part (L) states that W = aLb
Use the three approaches to fit the equation
-
Scatter plotW = aLb In EXCEL: Log-transform W and L Run linear
regression Obtain a and b
Chart2
1.6571.1650078378
2.52.2849017003
4.683.8528148995
7.0755.9044251838
10.078.4709213621
11.98811.5802063335
14.83615.2576527676
18.31819.5266131121
23.49624.408782191
27.89729.9244654158
36.79636.0927834768
44.61142.9318325573
50.18350.4588123848
W
Pred
L
W
Sheet1
TimeDensityPredSSa9.5549148261PredSADa9.5549561681
120.02319.17212548610.7239874384b0.69640176319.17318979330.8498102067b0.6964529479
239.83338.46924879461.859817350438.47335355421.3596464458
380.57177.189308190611.435839493677.20149592533.3695040747
4161.102154.881872811438.6899822425154.91425681736.1877431827
5317.923310.773539598551.1147840323310.85442940727.0685705928
6635.672623.5732507716146.3797328911623.767484461211.9045155388
71284.5441251.21205486231111.01856666371251.665853412332.8781465877
82569.432510.58172282963463.11972592012511.620832483257.8091675168
95082.6545037.53185761942036.00773301335039.874810810142.7791891899
1010220.77710107.90725741812739.578790532110113.1260658979107.6509341021
1120673.87320281.7157314902153787.32324504420293.2260550147380.6469449853
1240591.43940695.66355697410862.758276430540720.8434895903129.4044895903
1381374.64281656.653424593579530.443601228781711.3597418362336.7177418362
14163963.873163845.68933663513967.37828636352.0149258905163963.8509100780.0220899223
277747.8323686450.70058241581118.6484937721
LWPredSSa19.5266131121
0.31.6571.1650.242b2.3414569542
0.42.5002.2850.046
0.54.6803.8530.684
0.67.0755.9041.370
0.710.0708.4712.557
0.811.98811.5800.166
0.914.83615.2580.178
118.31819.5271.461
1.123.49624.4090.833
1.227.89729.9244.111
1.336.79636.0930.495
1.444.61142.9322.820W2/W1L2/L1
1.550.18350.4590.0761.124901930.11769585851.07142857140.0689928715
251.85915.0391.7059133212
Sheet1
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Density
Time
Density
Sheet2
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W
Pred
L
W
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
xxia:Sum of Absolute Difference
Sheet3
00
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SE
Pred
MBD089B4B7A.unknown
-
EXCEL solverW=aLbInitial values:
Sheet1
TimeDensityPredSSa9.5549148261PredSADa9.5549561681
120.02319.17212548610.7239874384b0.69640176319.17318979330.8498102067b0.6964529479
239.83338.46924879461.859817350438.47335355421.3596464458
380.57177.189308190611.435839493677.20149592533.3695040747
4161.102154.881872811438.6899822425154.91425681736.1877431827
5317.923310.773539598551.1147840323310.85442940727.0685705928
6635.672623.5732507716146.3797328911623.767484461211.9045155388
71284.5441251.21205486231111.01856666371251.665853412332.8781465877
82569.432510.58172282963463.11972592012511.620832483257.8091675168
95082.6545037.53185761942036.00773301335039.874810810142.7791891899
1010220.77710107.90725741812739.578790532110113.1260658979107.6509341021
1120673.87320281.7157314902153787.32324504420293.2260550147380.6469449853
1240591.43940695.66355697410862.758276430540720.8434895903129.4044895903
1381374.64281656.653424593579530.443601228781711.3597418362336.7177418362
14163963.873163845.68933663513967.37828636352.0149258905163963.8509100780.0220899223
277747.8323686450.70058241581118.6484937721
LWPredSSa19.5266131121
0.31.6571.1650.242b2.3414569542
0.42.5002.2850.046
0.54.6803.8530.684
0.67.0755.9041.370
0.710.0708.4712.557
0.811.98811.5800.166
0.914.83615.2580.178
118.31819.5271.461
1.123.49624.4090.833
1.227.89729.9244.111
1.336.79636.0930.495
1.444.61142.9322.820W2/W1L2/L1
1.550.18350.4590.0761.124901930.11769585851.07142857140.0689928715
251.85915.0391.7059133212
Sheet1
Density
Time
Density
Sheet2
W
Pred
L
W
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
xxia:Sum of Absolute Difference
Sheet3
SE
Pred
MBD089B4B7A.unknown
-
DNA and protein gel electrophoresisHow to estimate the molecular
mass of a protein?A ladder: proteins with known molecular
massDeriving a calibration curve relating molecular mass (M) to
migration distance (D): D = F(M)Measure D and obtain MThe
calibration curve is obtained by fitting a regression model
-
Protein molecular massThe equation D=aebM appears to describe
the relationship between D and M quite well. This relationship is
better than some published relationships, e.g., D = a b ln(M)The
data are my measurement of D and M for a subset of secreted
proteins from the gastric pathogen Helicobacter pylori (Bumann et
al., 2002).Homework: use the data and the three approaches to
estimate parameters a and b (You dont need to submit)Bumann, D.,
Aksu, S., Wendland, M., Janek, K., Zimny-Arndt, U., Sabarth, N.,
Meyer, T.F., and Jungblut, P.R., 2002, Proteome analysis of
secreted proteins of the gastric pathogen Helicobacter pylori.
Infect. Immun. 70: 3396-3403.
-
Area and RadiusWhat is the functional relationship between the
area and the radius? Homework (you do not need to submit): Measure
the area A (by counting the squares) and radius r for each circle
and estimate the parameters c and d in the equation A = crd by
using the three approaches.
-
Toxicity study: pesticideWhat transformation to use?
Chart2
0.9015846774
1.3894834313
2.4000405932
2.4943883966
6.4189661989
7.7805400881
9.1591986512
10.2088586684
11.7123811193
16.2364724554
16.9015777646
22.9397871198
27.3508823737
27.4484332825
28.1445939985
28.9651842358
29.9621346594
30.5035832592
34.3026952415
35.3893511641
35.6507263057
37.5474145035
38.4624320734
40.9673433197
44.3652504785
45.7144556528
46.6642356009
47.3824035673
49.855106855
52.2636914406
55.1161614968
56.1171111305
57.67771129
59.9949422082
60.3001873715
60.506031969
61.8217874273
61.9994461769
62.9219346398
66.055763247
67.1443846938
70.5845441333
71.5745760482
74.1110193003
74.1163029714
76.7729650311
77.0137766632
78.5649593318
79.0137718209
83.5348412807
83.9629189436
84.4029129847
87.9535989003
88.7425381996
91.1315802873
92.6449336198
92.6719843541
95.4908779602
96.9960598293
97.1534482984
Percentage
Dosage
Percentage killed
Sheet1
These are actually my species and the order that they should be
in - and the real gtRNA data.. just done manually and not including
the repeatmasked numbers
The initial files that I need to input are species specific - so
this eg would be a compilation from 24 txt files
PheAsnAspHisSerTyrCysGly
GAAAAAGTTATTGTCATCGTGATGGCTACTGTAATAGCAACAGCCACC
H sapiensall12321191181413015
masked
P troglo.all1130113109213127111
masked
M musculusall7141610181057141
masked
C familiarisall102111329892110
masked
F catusall822157113619128191
masked
B taurusall2874048622318438103531576220
masked
G gallusall91987166105
masked
T rubripesall2024241202161411218
masked
D rerioall19710104524150138710424142365136983611
masked150099610750350540032002500400(eg)
D melan.all81214569714
masked
C elegansall1420271919191316
masked
S cerevisall111116828416
masked
I would also like an output from each species for all tRNA
comparing repeat masked and not
Species: H sapiensSpecies: P troglodytes
number of tRNAnumber of tRNA
anticodongtRNAdbRM+RM-anticodongtRNAdbRM+RM-
AlaANN302010AlaANN302010
GNN25205GNN25205
CNN15610056CNN15610056
UNN12310023UNN12310023
GlyANNGlyANN
GNNGNN
CNNCNN
UNNUNN
ProANNProANN
GNNGNN
CNNCNN
UNNUNN
ThrANNThrANN
GNNGNN
CNNCNN
UNNUNN
ValANNValANN
GNNGNN
CNNCNN
UNNUNN
SerANNSerANN
GNNGNN
CNNCNN
UNNUNN
ANNANN
GNNGNN
ArgANNArgANN
GNNGNN
CNNCNN
UNNUNN
CNNCNN
GNNGNN
LeuANNLeuANN
GNNGNN
CNNCNN
UNNUNN
CNNCNN
GNNGNN
PheANNPheANN
GNNGNN
AsnANNAsnANN
GNNGNN
AspANNAspANN
GNNGNN
HisANNHisANN
GNNGNN
(etc)(etc)
Sheet2
B
FavorOpposeMarginal
AF4352953.76120011573.95124371863.95483935133.757604483-8.326487131310.0692402518
M6134954.11087386423.52636052463.91723462863.719999760211.8119933723-6.5837340108
1048619013.942024964
52433.95124371863.7612001157
52433.95124371863.7612001157
1.55769230771.8837209302-8.17187492429.8822673502
1.55769230771.88372093029.7374388811-7.9845460966
6.88282647586.9265704209
DF
u3.83741955581
uA(F)0.01880236141601018030280
uA(M)-0.0188023614Disease PresentDisease absent1202036060560
uB(F)0.09861743421Loc1Loc2Loc1Loc21803054090840
uB(O)-0.0986174342Race144123810104
uAB(F,F)-0.19363923561Race22822201888245.660673733323.0258509299934.7322331602102.03592144991577.7410888874
uAB(F,O)0.193639235672345828192574.499009133859.91464547112118.9974513221245.66067373333543.6445988884
uAB(M,F)0.193639235613062934.7322331602102.03592144993397.4473353615404.98287032975656.0575891434
uAB(M,O)-0.193639235610686
166.504343892429.8188797975138.228274069623.0258509299483.01665351070.0000000
93.301726284968.002933973959.914645471152.0266916421394.0056396741
1009.4391114293601018030280
|BlackBlondBrownRedTotal632.7794785592255.88233187282012060360560
Female55646516200494.3245439759383.073867477880130240390840
Male3216439100
878010825300245.660673733323.0258509299934.7322331602102.03592144991577.7410888874
220.4033251878266.168517335271.335172543244.361419555813.238107699259.9146454711574.4990091338245.66067373332118.99745132213543.6445988884
110.903548889644.3614195558161.731604974819.7750211961139.0400292381Collapsed
over
disease350.5621307739632.77947855921315.35334160212326.79722825825656.0575891434
388.5340063229350.5621307739505.670172529480.47189562178222104
1059.6634733096484088427.4124
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Sheet3
IndXX2Dosagey1y2CDF1CDF2Probit1Probit2PercentageDosagePercentage
10.1-2.265902295527-1.6891650862-2.36496667180.04559389890.0090158468-1.6891650862-2.36496667180.90270.90
20.2-2.105149337528-1.6319052528-2.2002428640.05134972370.0138948343-1.6319052528-2.2002428641.39281.39
30.3-1.887640522331-1.5746454194-1.97736124060.05766915640.0240004059-1.5746454194-1.97736124062.40312.40
40.4-1.871600533831-1.5173855859-1.96092503930.06458468050.024943884-1.5173855859-1.96092503932.49312.49
50.5-1.441815878535-1.4601257525-1.52052403010.07212775810.064189662-1.4601257525-1.52052403016.42356.42
60.6-1.343704630636-1.4028659191-1.41998928070.08032841270.0778054009-1.4028659191-1.41998928077.78367.78
70.7-1.256875476937-1.3456060856-1.3310153110.0892147940.0915919865-1.3456060856-1.3310153119.16379.16
80.8-1.197077510838-1.2883462522-1.26974024150.09881272970.1020885867-1.2883462522-1.269740241510.213810.21
90.9-1.118760007138-1.2310864188-1.18948817280.10914527220.1171238112-1.2310864188-1.189488172811.713811.71
101-0.918991909740-1.1738265853-0.98478549060.12023224650.1623647246-1.1738265853-0.984785490616.244016.24
111.1-0.89291250341-1.1165667519-0.95806188180.13208980710.1690157776-1.1165667519-0.958061881816.904116.90
121.2-0.680918492143-1.0593069185-0.74083128690.14473001260.2293978712-1.0593069185-0.740831286922.944322.94
131.3-0.5456633344-1.002047085-0.60223511050.15816042630.2735088237-1.002047085-0.602235110527.354427.35
141.4-0.542805095144-0.9447872516-0.59930627280.17238375080.2744843328-0.9447872516-0.599306272827.454427.45
151.5-0.522550578744-0.8875274182-0.57855143810.18739750570.28144594-0.8875274182-0.578551438128.144428.14
161.6-0.498983393645-0.8302675848-0.55440210660.20319375550.2896518424-0.8302675848-0.554402106628.974528.97
171.7-0.470768118945-0.7730077513-0.52548987050.21975889660.2996213466-0.7730077513-0.525489870529.964529.96
181.8-0.455623506445-0.7157479179-0.50997116230.23707350850.3050358326-0.7157479179-0.509971162330.504530.50
191.9-0.352417668446-0.6584880845-0.40421597850.25511227560.3430269524-0.6584880845-0.404215978534.304634.30
202-0.323739911546-0.601228251-0.37482983610.27384398520.3538935116-0.601228251-0.374829836135.394635.39
212.1-0.316889801346-0.5439684176-0.36781051750.29323160310.3565072631-0.5439684176-0.367810517535.654635.65
222.2-0.267683959647-0.4867085842-0.31738921450.31323243050.375474145-0.4867085842-0.317389214537.554737.55
232.3-0.244231895547-0.4294487507-0.29335784760.33379834120.3846243207-0.4294487507-0.293357847638.464738.46
242.4-0.180825394748-0.3721889173-0.2283851070.35487609850.4096734332-0.3721889173-0.22838510740.974840.97
252.5-9.62E-0249-0.3149290839-0.14171514770.37640774930.4436525048-0.3149290839-0.141715147744.374944.37
262.6-6.30E-0249-0.2576692504-0.10763010790.39833108980.4571445565-0.2576692504-0.107630107945.714945.71
272.7-0.039640657249-0.200409417-0.08371288540.42058019760.466642356-0.200409417-0.083712885446.664946.66
282.8-2.20E-0249-0.1431495836-0.06566056230.44308602310.4738240357-0.1431495836-0.065660562347.384947.38
292.93.85E-0250-0.0858897501-0.00363194050.46577702980.4985510686-0.0858897501-0.003631940549.865049.86
3030.097458477850-0.02862991670.05677281290.48857987590.5226369144-0.02862991670.056772812952.265052.26
313.10.16755098510.02862991670.12859670960.51142012410.5511616150.02862991670.128596709655.125155.12
323.20.1922824353510.08588975010.15393907060.53422297020.56117111130.08588975010.153939070656.125156.12
333.30.2310412865520.14314958360.1936553260.55691397690.57677711290.14314958360.19365532657.685257.68
343.40.2891663705520.2004094170.25321619060.57941980240.59994942210.2004094170.253216190659.995259.99
353.50.2968843528520.25766925040.26112481910.60166891020.60300187370.25766925040.261124819160.305260.30
363.60.3020980212530.31492908390.26646727330.62359225070.60506031970.31492908390.266467273360.515360.51
373.70.3356066476530.37218891730.30080361520.64512390150.61821787430.37218891730.300803615261.825361.82
383.80.3401568773530.42944875070.30546624280.66620165880.61999446180.42944875070.305466242862.005362.00
393.90.3638908439530.48670858420.32978647550.68676756950.62921934640.48670858420.329786475562.925362.92
4040.4460601456540.54396841760.41398548770.70676839690.66055763250.54396841760.413985487766.065466.06
414.10.4752570158540.6012282510.44390356620.72615601480.67144384690.6012282510.443903566267.145467.14
424.20.5702938518550.65848808450.54128796080.74488772440.70584544130.65848808450.541287960870.585570.58
434.30.5985572526550.71574791790.57024951190.76292649150.71574576050.71574791790.570249511971.575571.57
444.40.6732349345560.77300775130.64677184950.78024110340.7411101930.77300775130.646771849574.115674.11
454.50.6733942606560.83026758480.64693511120.79680624450.74116302970.83026758480.646935111274.125674.12
464.60.7558137059570.88752741820.73139044580.81260249430.76772965030.88752741820.731390445876.775776.77
474.70.7635332065570.94478725160.73930063020.82761624920.77013776660.94478725160.739300630277.015777.01
484.80.814393052581.0020470850.79141679350.84183957370.78564959331.0020470850.791416793578.565878.56
494.90.8295022315581.05930691850.80689919340.85526998740.79013771821.05930691850.806899193479.015879.01
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515.11.0110534606601.17382658530.99293502210.87976775350.83962918941.17382658530.992935022183.966083.96
525.21.0288352647601.23108641881.01115606460.89085472780.84402912981.23108641881.011156064684.406084.40
535.31.186456079611.28834625221.17267035980.90118727030.8795359891.28834625221.172670359887.956187.95
545.41.225763972621.34560608561.21294921930.9107852060.8874253821.34560608561.212949219388.746288.74
555.51.3584396169631.40286591911.34890216030.91967158730.91131580291.40286591911.348902160391.136391.13
565.61.456950587641.46012575251.44984650560.92787224190.92644933621.46012575251.449846505692.646492.64
575.71.4588461519641.51738558591.4517888940.93541531950.92671984351.51738558591.45178889492.676492.67
585.81.6956440977661.57464541941.69443611980.94233084360.95490877961.57464541941.694436119895.496695.49
595.91.8769444197681.63190525281.88021484360.94865027630.96996059831.63190525281.880214843697.006897.00
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Percentage
Dosage
Percentage killed
-
Probit and probit transformationProbit has two
names/definitions, both associated with standard normal
distribution: the inverse cumulative distribution function
(CDF)quantile functionCDF is denoted by (z), which is a continuous,
monotone increasing sigmoid function in the range of (0,1), e.g.,
(z) = p (-1.96) = 0.025 = 1 - (1.96)The probit function gives the
'inverse' computation, formally denoted -1(p), i.e., probit(p) =
-1(p) probit(0.025) = -1.96 = -probit(0.975)[probit(p)] = p, and
probit[(z)] = z.
Chart3
0.0090158468
0.0138948343
0.0240004059
0.024943884
0.064189662
0.0778054009
0.0915919865
0.1020885867
0.1171238112
0.1623647246
0.1690157776
0.2293978712
0.2735088237
0.2744843328
0.28144594
0.2896518424
0.2996213466
0.3050358326
0.3430269524
0.3538935116
0.3565072631
0.375474145
0.3846243207
0.4096734332
0.4436525048
0.4571445565
0.466642356
0.4738240357
0.4985510686
0.5226369144
0.551161615
0.5611711113
0.5767771129
0.5999494221
0.6030018737
0.6050603197
0.6182178743
0.6199944618
0.6292193464
0.6605576325
0.6714438469
0.7058454413
0.7157457605
0.741110193
0.7411630297
0.7677296503
0.7701377666
0.7856495933
0.7901377182
0.8353484128
0.8396291894
0.8440291298
0.879535989
0.887425382
0.9113158029
0.9264493362
0.9267198435
0.9549087796
0.9699605983
0.971534483
0
1
CDF2
z
CDF
Sheet1
These are actually my species and the order that they should be
in - and the real gtRNA data.. just done manually and not including
the repeatmasked numbers
The initial files that I need to input are species specific - so
this eg would be a compilation from 24 txt files
PheAsnAspHisSerTyrCysGly
GAAAAAGTTATTGTCATCGTGATGGCTACTGTAATAGCAACAGCCACC
H sapiensall12321191181413015
masked
P troglo.all1130113109213127111
masked
M musculusall7141610181057141
masked
C familiarisall102111329892110
masked
F catusall822157113619128191
masked
B taurusall2874048622318438103531576220
masked
G gallusall91987166105
masked
T rubripesall2024241202161411218
masked
D rerioall19710104524150138710424142365136983611
masked150099610750350540032002500400(eg)
D melan.all81214569714
masked
C elegansall1420271919191316
masked
S cerevisall111116828416
masked
I would also like an output from each species for all tRNA
comparing repeat masked and not
Species: H sapiensSpecies: P troglodytes
number of tRNAnumber of tRNA
anticodongtRNAdbRM+RM-anticodongtRNAdbRM+RM-
AlaANN302010AlaANN302010
GNN25205GNN25205
CNN15610056CNN15610056
UNN12310023UNN12310023
GlyANNGlyANN
GNNGNN
CNNCNN
UNNUNN
ProANNProANN
GNNGNN
CNNCNN
UNNUNN
ThrANNThrANN
GNNGNN
CNNCNN
UNNUNN
ValANNValANN
GNNGNN
CNNCNN
UNNUNN
SerANNSerANN
GNNGNN
CNNCNN
UNNUNN
ANNANN
GNNGNN
ArgANNArgANN
GNNGNN
CNNCNN
UNNUNN
CNNCNN
GNNGNN
LeuANNLeuANN
GNNGNN
CNNCNN
UNNUNN
CNNCNN
GNNGNN
PheANNPheANN
GNNGNN
AsnANNAsnANN
GNNGNN
AspANNAspANN
GNNGNN
HisANNHisANN
GNNGNN
(etc)(etc)
Sheet2
B
FavorOpposeMarginal
AF4352953.76120011573.95124371863.95483935133.757604483-8.326487131310.0692402518
M6134954.11087386423.52636052463.91723462863.719999760211.8119933723-6.5837340108
1048619013.942024964
52433.95124371863.7612001157
52433.95124371863.7612001157
1.55769230771.8837209302-8.17187492429.8822673502
1.55769230771.88372093029.7374388811-7.9845460966
6.88282647586.9265704209
DF
u3.83741955581
uA(F)0.01880236141601018030280
uA(M)-0.0188023614Disease PresentDisease absent1202036060560
uB(F)0.09861743421Loc1Loc2Loc1Loc21803054090840
uB(O)-0.0986174342Race144123810104
uAB(F,F)-0.19363923561Race22822201888245.660673733323.0258509299934.7322331602102.03592144991577.7410888874
uAB(F,O)0.193639235672345828192574.499009133859.91464547112118.9974513221245.66067373333543.6445988884
uAB(M,F)0.193639235613062934.7322331602102.03592144993397.4473353615404.98287032975656.0575891434
uAB(M,O)-0.193639235610686
166.504343892429.8188797975138.228274069623.0258509299483.01665351070.0000000
93.301726284968.002933973959.914645471152.0266916421394.0056396741
1009.4391114293601018030280
|BlackBlondBrownRedTotal632.7794785592255.88233187282012060360560
Female55646516200494.3245439759383.073867477880130240390840
Male3216439100
878010825300245.660673733323.0258509299934.7322331602102.03592144991577.7410888874
220.4033251878266.168517335271.335172543244.361419555813.238107699259.9146454711574.4990091338245.66067373332118.99745132213543.6445988884
110.903548889644.3614195558161.731604974819.7750211961139.0400292381Collapsed
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1315.3533416021147.55517816461577.7410888874
12.96349350040.2746141987350.56213077392963.41732987293543.6445988884
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297.39466694351122.89258145071577.7410888874
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Sheet3
IndXX2Dosagey1y2CDF1CDF2Probit1Probit2PercentageDosagePercentage
10.1-2.265902295527-1.6891650862-2.36496667180.04559389890.0090158468-1.6891650862-2.36496667180.90270.90
20.2-2.105149337528-1.6319052528-2.2002428640.05134972370.0138948343-1.6319052528-2.2002428641.39281.39
30.3-1.887640522331-1.5746454194-1.97736124060.05766915640.0240004059-1.5746454194-1.97736124062.40312.40
40.4-1.871600533831-1.5173855859-1.96092503930.06458468050.024943884-1.5173855859-1.96092503932.49312.49
50.5-1.441815878535-1.4601257525-1.52052403010.07212775810.064189662-1.4601257525-1.52052403016.42356.42
60.6-1.343704630636-1.4028659191-1.41998928070.08032841270.0778054009-1.4028659191-1.41998928077.78367.78
70.7-1.256875476937-1.3456060856-1.3310153110.0892147940.0915919865-1.3456060856-1.3310153119.16379.16
80.8-1.197077510838-1.2883462522-1.26974024150.09881272970.1020885867-1.2883462522-1.269740241510.213810.21
90.9-1.118760007138-1.2310864188-1.18948817280.10914527220.1171238112-1.2310864188-1.189488172811.713811.71
101-0.918991909740-1.1738265853-0.98478549060.12023224650.1623647246-1.1738265853-0.984785490616.244016.24
111.1-0.89291250341-1.1165667519-0.95806188180.13208980710.1690157776-1.1165667519-0.958061881816.904116.90
121.2-0.680918492143-1.0593069185-0.74083128690.14473001260.2293978712-1.0593069185-0.740831286922.944322.94
131.3-0.5456633344-1.002047085-0.60223511050.15816042630.2735088237-1.002047085-0.602235110527.354427.35
141.4-0.542805095144-0.9447872516-0.59930627280.17238375080.2744843328-0.9447872516-0.599306272827.454427.45
151.5-0.522550578744-0.8875274182-0.57855143810.18739750570.28144594-0.8875274182-0.578551438128.144428.14
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212.1-0.316889801346-0.5439684176-0.36781051750.29323160310.3565072631-0.5439684176-0.367810517535.654635.65
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252.5-9.62E-0249-0.3149290839-0.14171514770.37640774930.4436525048-0.3149290839-0.141715147744.374944.37
262.6-6.30E-0249-0.2576692504-0.10763010790.39833108980.4571445565-0.2576692504-0.107630107945.714945.71
272.7-0.039640657249-0.200409417-0.08371288540.42058019760.466642356-0.200409417-0.083712885446.664946.66
282.8-2.20E-0249-0.1431495836-0.06566056230.44308602310.4738240357-0.1431495836-0.065660562347.384947.38
292.93.85E-0250-0.0858897501-0.00363194050.46577702980.4985510686-0.0858897501-0.003631940549.865049.86
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313.10.16755098510.02862991670.12859670960.51142012410.5511616150.02862991670.128596709655.125155.12
323.20.1922824353510.08588975010.15393907060.53422297020.56117111130.08588975010.153939070656.125156.12
333.30.2310412865520.14314958360.1936553260.55691397690.57677711290.14314958360.19365532657.685257.68
343.40.2891663705520.2004094170.25321619060.57941980240.59994942210.2004094170.253216190659.995259.99
353.50.2968843528520.25766925040.26112481910.60166891020.60300187370.25766925040.261124819160.305260.30
363.60.3020980212530.31492908390.26646727330.62359225070.60506031970.31492908390.266467273360.515360.51
373.70.3356066476530.37218891730.30080361520.64512390150.61821787430.37218891730.300803615261.825361.82
383.80.3401568773530.42944875070.30546624280.66620165880.61999446180.42944875070.305466242862.005362.00
393.90.3638908439530.48670858420.32978647550.68676756950.62921934640.48670858420.329786475562.925362.92
4040.4460601456540.54396841760.41398548770.70676839690.66055763250.54396841760.413985487766.065466.06
414.10.4752570158540.6012282510.44390356620.72615601480.67144384690.6012282510.443903566267.145467.14
424.20.5702938518550.65848808450.54128796080.74488772440.70584544130.65848808450.541287960870.585570.58
434.30.5985572526550.71574791790.57024951190.76292649150.71574576050.71574791790.570249511971.575571.57
444.40.6732349345560.77300775130.64677184950.78024110340.7411101930.77300775130.646771849574.115674.11
454.50.6733942606560.83026758480.64693511120.79680624450.74116302970.83026758480.646935111274.125674.12
464.60.7558137059570.88752741820.73139044580.81260249430.76772965030.88752741820.731390445876.775776.77
474.70.7635332065570.94478725160.73930063020.82761624920.77013776660.94478725160.739300630277.015777.01
484.80.814393052581.0020470850.79141679350.84183957370.78564959331.0020470850.791416793578.565878.56
494.90.8295022315581.05930691850.80689919340.85526998740.79013771821.05930691850.806899193479.015879.01
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515.11.0110534606601.17382658530.99293502210.87976775350.83962918941.17382658530.992935022183.966083.96
525.21.0288352647601.23108641881.01115606460.89085472780.84402912981.23108641881.011156064684.406084.40
535.31.186456079611.28834625221.17267035980.90118727030.8795359891.28834625221.172670359887.956187.95
545.41.225763972621.34560608561.21294921930.9107852060.8874253821.34560608561.212949219388.746288.74
555.51.3584396169631.40286591911.34890216030.91967158730.91131580291.40286591911.348902160391.136391.13
565.61.456950587641.46012575251.44984650560.92787224190.92644933621.46012575251.449846505692.646492.64
575.71.4588461519641.51738558591.4517888940.93541531950.92671984351.51738558591.45178889492.676492.67
585.81.6956440977661.57464541941.69443611980.94233084360.95490877961.57464541941.694436119895.496695.49
595.91.8769444197681.63190525281.88021484360.94865027630.96996059831.63190525281.880214843697.006897.00
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CDF2
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CDF
-
Data
Sheet1
TimeDensityPredSSa9.5549148261PredSADa9.5549561681
120.02319.17212548610.7239874384b0.69640176319.17318979330.8498102067b0.6964529479
239.83338.46924879461.859817350438.47335355421.3596464458
380.57177.189308190611.435839493677.20149592533.3695040747
4161.102154.881872811438.6899822425154.91425681736.1877431827
5317.923310.773539598551.1147840323310.85442940727.0685705928
6635.672623.5732507716146.3797328911623.767484461211.9045155388
71284.5441251.21205486231111.01856666371251.665853412332.8781465877
82569.432510.58172282963463.11972592012511.620832483257.8091675168
95082.6545037.53185761942036.00773301335039.874810810142.7791891899
1010220.77710107.90725741812739.578790532110113.1260658979107.6509341021
1120673.87320281.7157314902153787.32324504420293.2260550147380.6469449853
1240591.43940695.66355697410862.758276430540720.8434895903129.4044895903
1381374.64281656.653424593579530.443601228781711.3597418362336.7177418362
14163963.873163845.68933663513967.37828636352.0149258905163963.8509100780.0220899223
277747.8323686450.70058241581118.6484937721
LWPredSSa19.5266131121
0.31.6571.1650.242b2.3414569542
0.42.5002.2850.046
0.54.6803.8530.684
0.67.0755.9041.370
0.710.0708.4712.557
0.811.98811.5800.166
0.914.83615.2580.178
118.31819.5271.461
1.123.49624.4090.833
1.227.89729.9244.111
1.336.79636.0930.495
1.444.61142.9322.820W2/W1L2/L1
1.550.18350.4590.0761.124901930.11769585851.07142857140.0689928715
251.85915.0391.7059133212
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Density
Sheet2
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W
Pred
L
W
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
0.0249978951
-1.96
xxia:Sum of Absolute Difference
Sheet3
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SE
Pred
GE
SE
Dosage%KilledProbitPredPredOriginalSUMMARY OUTPUT
270.9-2.3656181269-2.3543306070.9278049506
281.39-2.200097193-2.25152388041.2176188064Regression
Statistics
312.4-1.9773684282-1.94310370072.6001814992Multiple
R0.999559043
312.49-1.9616778691-1.94310370072.6001814992R
Square0.9991182804
356.42-1.520441703-1.53187679456.2776420452Adjusted R
Square0.9991030783
367.78-1.4200263842-1.42907006797.6492047772Standard
Error0.0299488552
379.16-1.3309666035-1.32626334149.2376242462Observations60
3810.21-1.2696761829-1.223456614811.0578640589
3811.71-1.1896092717-1.223456614811.0578640589ANOVA
4016.24-0.9846419015-1.017843161715.4376248266dfSSMSFSignificance
F
4116.9-0.9581244654-0.915036435118.0086251455Regression158.948784508858.948784508865722.54953839780
4322.94-0.7408242659-0.70942298223.9031015105Residual580.05202216780.0008969339
4427.35-0.6022616262-0.606616255427.2052804974Total5959.0008066766
4427.45-0.5992592761-0.606616255427.2052804974
4428.14-0.5786875769-0.606616255427.2052804974CoefficientsStandard
Errort StatP-value
4528.97-0.554261346-0.503809528930.7197615472Intercept-5.13011222420.0203809175-251.71154475850
4529.96-0.5255513016-0.503809528930.7197615472Dosage0.10280672660.0004010184256.36409564990
4530.5-0.510073457-0.503809528930.7197615472
4634.3-0.4042892903-0.401002802334.4209030368
4635.39-0.3748123887-0.401002802334.4209030368
4635.65-0.3678299974-0.401002802334.4209030368
4737.55-0.3173210586-0.298196075738.2776758372
4738.46-0.2934214917-0.298196075738.2776758372
4840.97-0.2283167549-0.195389349242.254407703
4944.37-0.1415948944-0.092582622646.31175748
4945.71-0.107742444-0.092582622646.31175748
4946.66-0.0838194293-0.092582622646.31175748
4947.38-0.0657209409-0.092582622646.31175748
5049.86-0.00350928680.01022410450.4078756289
5052.260.05668013320.01022410450.4078756289
5155.120.12869372610.113030830554.4996943695
5156.120.15401234730.113030830554.4996943695
5257.680.19371378170.215837557158.5442814043
5259.990.2530882740.215837557158.5442814043
5260.30.26111995950.215837557158.5442814043
5360.510.26657033320.318644283762.500186552
5361.820.30075673790.318644283762.500186552
53620.30548078810.318644283762.500186552
5362.920.32973527160.318644283762.500186552
5466.060.41410119180.421451010266.328711137
5467.140.44378228140.421451010266.328711137
5570.580.54115609020.524257736869.9950356005
5571.570.57011456070.524257736869.9950356005
5674.110.64674035580.627064463373.4691509878
5674.120.6470493570.627064463373.4691509878
5776.770.73129333610.729871189976.7265537952
5777.010.73917621790.729871189976.7265537952
5878.560.79124677660.832677916579.7486798784
5879.010.80676827530.832677916579.7486798784
5983.530.97532313130.93548464382.5230703027
6083.960.99281524341.038291369685.0432787278
6084.41.01103432811.038291369685.0432787278
6187.951.17249095861.141098096287.3085448262
6288.741.21281646221.243904822789.32327029
6391.131.34880378081.346711549391.0963424414
6492.641.44949283441.449518275892.6403549858
6492.671.45164622041.449518275892.6403549858
6695.491.69434365311.65513172995.1051109996
68971.88079360821.860745182196.8609914974
6997.151.90331081871.963551908797.520896044
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%Killed
PredOriginal
MBD089B4B7A.unknown
-
Commonly Encountered Funtions
-
Non-linear regressionIn rapidly replicating unicellular
eukaryotes such as the yeast, highly expressed intron-containing
genes requires more efficient splicing sites than lowly expressed
genes.Natural selection will operate on the mutations at the
slicing sites to optimize splicing efficiency.Designate splicing
efficiency as SE and gene expression as GE.Certain biochemical
reasoning suggests that SE and GE will follow the following
relationships:
-
Scatter plotInitial values: 0.4 (inferred when GE = 0) / 1 or
(inferred when GE is very large) When GE = 8, we have (0.4+8 )/(1+8
) = 0.78
Chart3
0.460.4366551746
0.470.5102556856
0.570.5652935603
0.610.6080048725
0.620.6421136729
0.680.6699811347
0.690.6931767527
0.780.7127844846
0.70.7295770286
0.740.7441200634
0.770.7568372265
0.780.7680520635
0.740.7780159401
0.80.7780159401
0.80.7949440259
0.780.8021948737
SE
Pred
GE
SE
Sheet1
TimeDensityPredSSa9.5549148261PredSADa9.5549561681
120.02319.17212548610.7239874384b0.69640176319.17318979330.8498102067b0.6964529479
239.83338.46924879461.859817350438.47335355421.3596464458
380.57177.189308190611.435839493677.20149592533.3695040747
4161.102154.881872811438.6899822425154.91425681736.1877431827
5317.923310.773539598551.1147840323310.85442940727.0685705928
6635.672623.5732507716146.3797328911623.767484461211.9045155388
71284.5441251.21205486231111.01856666371251.665853412332.8781465877
82569.432510.58172282963463.11972592012511.620832483257.8091675168
95082.6545037.53185761942036.00773301335039.874810810142.7791891899
1010220.77710107.90725741812739.578790532110113.1260658979107.6509341021
1120673.87320281.7157314902153787.32324504420293.2260550147380.6469449853
1240591.43940695.66355697410862.758276430540720.8434895903129.4044895903
1381374.64281656.653424593579530.443601228781711.3597418362336.7177418362
14163963.873163845.68933663513967.37828636352.0149258905163963.8509100780.0220899223
277747.8323686450.70058241581118.6484937721
LWPredSSa19.5266131121
0.31.6571.1650.242b2.3414569542
0.42.5002.2850.046
0.54.6803.8530.684
0.67.0755.9041.370
0.710.0708.4712.557
0.811.98811.5800.166
0.914.83615.2580.178
118.31819.5271.461
1.123.49624.4090.833
1.227.89729.9244.111
1.336.79636.0930.495
1.444.61142.9322.820W2/W1L2/L1
1.550.18350.4590.0761.124901930.11769585851.07142857140.0689928715
251.85915.0391.7059133212
Sheet1
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0
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Density
Time
Density
Sheet2
00
00
00
00
00
00
00
00
00
00
00
00
00
W
Pred
L
W
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
0.0249978951
-1.96
xxia:Sum of Absolute Difference
Sheet3
00
00
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SE
Pred
GE
SE
MBD089B4B7A.unknown
-
EXCEL: Solver
Sheet1
TimeDensityPredSSa9.5549148261PredSADa9.5549561681
120.02319.17212548610.7239874384b0.69640176319.17318979330.8498102067b0.6964529479
239.83338.46924879461.859817350438.47335355421.3596464458
380.57177.189308190611.435839493677.20149592533.3695040747
4161.102154.881872811438.6899822425154.91425681736.1877431827
5317.923310.773539598551.1147840323310.85442940727.0685705928
6635.672623.5732507716146.3797328911623.767484461211.9045155388
71284.5441251.21205486231111.01856666371251.665853412332.8781465877
82569.432510.58172282963463.11972592012511.620832483257.8091675168
95082.6545037.53185761942036.00773301335039.874810810142.7791891899
1010220.77710107.90725741812739.578790532110113.1260658979107.6509341021
1120673.87320281.7157314902153787.32324504420293.2260550147380.6469449853
1240591.43940695.66355697410862.758276430540720.8434895903129.4044895903
1381374.64281656.653424593579530.443601228781711.3597418362336.7177418362
14163963.873163845.68933663513967.37828636352.0149258905163963.8509100780.0220899223
277747.8323686450.70058241581118.6484937721
LWPredSSa19.5266131121
0.31.6571.1650.242b2.3414569542
0.42.5002.2850.046
0.54.6803.8530.684
0.67.0755.9041.370
0.710.0708.4712.557
0.811.98811.5800.166
0.914.83615.2580.178
118.31819.5271.461
1.123.49624.4090.833
1.227.89729.9244.111
1.336.79636.0930.495
1.444.61142.9322.820W2/W1L2/L1
1.550.18350.4590.0761.124901930.11769585851.07142857140.0689928715
251.85915.0391.7059133212
Sheet1
0
0
0
0
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0
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0
0
Density
Time
Density
Sheet2
00
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00
00
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00
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00
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00
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W
Pred
L
W
Sheet3
GESEPredSSAlpha0.3331962207
10.460.43665517460.0005449809Beta0.1920306455
20.470.51025568560.0016205202Gamma0.2028412734
30.570.56529356030.0000221506
40.610.60800487250.0000039805
50.620.64211367290.0004890145
60.680.66998113470.0001003777
70.690.69317675270.0000100918
80.780.71278448460.0045179255
90.70.72957702860.0008748006
100.740.74412006340.0000169749
110.770.75683722650.0001732586
120.780.76805206350.0001427532
130.740.77801594010.0014452117
130.80.77801594010.0004832989
150.80.79494402590.0000255629
160.780.80219487370.0004926124
0.0109635149
0.0249978951
-1.96
xxia:Sum of Absolute Difference
Sheet3
00
00
00
00
00
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00
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SE
Pred
GE
SE
MBD089B4B7A.unknown
/* Fictitious data */data Ecoli;Input Density @@;Time = _N_;lnD
= log(Density);datalines;20.023 39.833 80.571 161.102 317.923
635.672 1284.544 2569.430 5082.654 10220.777 20673.873 40591.439
81374.642 163963.873;proc reg; var Density lnD Time; model lnD =
Time; plot Density*Time/ symbol='.'; plot lnD*Time/
symbol='.';run;
/*Fitcitious data */data Elephant;Input L W @@;lnL = log(L);lnW
= log(W);datalines;0.3 1.657 0.4 2.500 0.5 4.680 0.6 7.075 0.7
10.0700.8 11.988 0.9 14.836 1 18.318 1.1 23.496 1.2 27.8971.3
36.796 1.4 44.611 1.5 50.183;proc reg; var L W lnL lnW; model lnW =
lnL; plot W*L/ symbol='.'; plot lnW*lnL/ symbol='.';run;
The SAS output will include Cooks D, which measures the effect
of deleting a given observation. Points with a large Cook's
distance are considered to merit closer examination in the
analysis.
Di = sum[Yj-Yj(i)]^2/(p*MSE)]
where Yj is the prediction for observation j with all
observations includedYj(i) is the prediction for observation j with
observation i excludedp is the number of fitted parameters
Data Pesticide;Input Dosage Percent @@;NuProbit =
probit(Percent/100);Cards;27 0.90 28 1.39 31 2.40 31 2.49 35 6.42
36 7.78 37 9.1638 10.21 38 11.71 40 16.24 41 16.90 43 22.94 44
27.35 44 27.45 44 28.14 45 28.97 45 29.96 45 30.50 46 34.30 46
35.39 46 35.65 47 37.55 47 38.46 48 40.97 49 44.37 49 45.71 49
46.66 49 47.38 50 49.86 50 52.26 51 55.12 51 56.12 52 57.68 52
59.99 52 60.30 53 60.51 53 61.8253 62.00 53 62.92 54 66.06 54 67.14
55 70.58 55 71.57 56 74.11 56 74.12 57 76.77 57 77.01 58 78.56 58
79.01 59 83.53 60 83.96 60 84.40 61 87.95 62 88.74 63 91.13 64
92.64 64 92.67 66 95.49 68 97.00 69 97.15; Proc reg; Model Percent
= Dosage / R CLM alpha = 0.01 CLI; Plot Percent*Dosage / symbol =
'.'; Model NuProbit = Dosage / R CLM alpha = 0.01 CLI; Plot
NuProbit*Dosage / symbol = '.';run;
