统计学（含SPSS）

SPSS[ ] WindowsXPSPSS11.0SPSS11.0

SASSPSSS-PLUSMINITABEXCELSAS SPSSSPSS SPSSSPSS

4- -- - 8()12345678SPSSSPSS

200 variable

107736897767994499857546571808488626179986662798668746182659862116658864797879778674867380687889725869927888771038863688881759062897171747074766581756294718584836381627983936165629265837070817772846759587866669477636675687690787110178435967617196756476727774658286668696898171859959926872776087847577514585678780849369768975836872679289829677102749176836668617372767377799463596271816573636389826485926473

npobservation

12kp1X11X12X1kX1P2X21X22X2kX2P jXj1Xj2XjkXjpnXn1Xn2XnkXnp

1010

1113156.047.52113155.037.83114157.949.24115166.057.05114164.544.06214164.744.17213158.057.38213162.047.09214160.553.010215169.051.1

13156.047.5 13155.037.8 14157.949.2 15166.057.0 14164.544.0 14164.744.113158.057.3 13162.047.0 14160.553.0 15169.051.1

1202273194385386537248419351030

1202122272113191214381135381236532137241238412219352121030123

sampling error

xy1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.0164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

30275640303631242325293329223329462534192323442930252360252737242231242627

11

176659374552909581879139751768568471748869735707866908469363798081786918377708838292846997875789194108571867462118155787071

123456789101112131415161101201201644301921752634293182492811601472101204668191112122621111614995

10

1234567891026881216202022265810588118117137157169149202

x1x2y123456789101005010010050807565909043422234329.34.88.96.54.26.27.46.07.66.1

1234687557792793245333114110012090110420

C fo r 7070160602010250140 14022030390

1234527.925.128.524.226.526.528.725.129.127.231.228.330.827.929.630.829.632.431.732.8

2040208030301070507030150

62520683136076311239859691954

152642414026059521713506574189162188150500

nominal scale ordinal scale interval scale ratio scale = +-

12 1234

123 12345

10

1113156.047.52113155.037.83114157.949.24115166.057.05114164.544.06214164.744.17213158.057.38213162.047.09214160.553.010215169.051.1

1202273194385386537248419351030

200

%1234524108934530836311510-300100

43-48248-53153-58258-632163-682868-732873-783378-832683-882188-931993-981098-1036103-1082108-1130113-1181

200 76.1

43-48248-53153-58258-632163-682868-732873-783378-832683-882188-931993-981098-1036103-1082108-1130113-1181

SPSSStatistical package for the social science (spss)spss2080Spss for windows 11.090spss

Spss Spss Spss

Spss for windows Sample data 1MBHelp files 11MBBasic scripting 2MBProduction mode facility 1MBStatistics coach 2MBSyntax guide 16MBSpss

Spss 11.0 EFspss setup.exe SPSS SPSS

Spss- Data view SPSS

Spss- variable view SPSS

File: SPSS

edit: SPSS

view: SPSS

data: SPSS

transform: SPSS

analyze: SPSS

graphs: SPSS

utilities: SPSS

window: SPSS

Help: SPSS

SpssSpss SPSS

[1]

1 2 3 4 5 6 7 8 9 101112131415131313131313131313131314141414156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930141414141415151515151515151515164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

Spssdata viewvariable view

Name: 5NumberSexAgeHeightweight

type: 81.Numeric:2.Comma:3.Dot:4.Scientific:5.Date:6.Dollar:7.Custom currency: 8.String:

width: Number 2Sex 1Age 2Height 5Weight 4

decimals: Number 0Sex 0Age 0Height 1Weight 1

label: NumberSex Age Height Weight

value: sex10

missing:

columns: 8

align: left right center

measure: scale ridinal nominal sex

13245()6

agedata insert variable var00001

3data insert case 3

data sort case sort case weightascending() ok

data transpose transposeok

data split filesplit filecompare groups sort the file by grouping variables sexgroups based onok

data aggregateaggregatesexagebreak variables ok

1=/2216034

transfom computecompute variabletarget variable typelablenumeric expressionok100

transfom countcount occurrences of values within casestarget variable(h) heightnumeric variablesdefine valuescount values within cases:values to countokrangelowest through160.0ok

transfom categorize variablescategorize variablestarget variable(h)4ok

statisticspopulationelementssampledatastatisticparameterconstantdescriptive statistics statistical inference

01020 bar chartpie chart 38%10,0%10,0%16,0%26,0%38,0%

38%10,0%10,0%16,0%26,0%38,0%

0102001020 01020

%%2410893453083631151021997864387332621.312.7 300100300100

10,0%15,0%31,0%36,0%8,0%

%%%241089345308.036.031.015.010.0241322252703008.044.075.090.0100.03002761687530100.092.056.025.010.0 300100----

19 22 22 23 23 2323 24 24 24 25 2525 25 26 27 27 2729 29 29 29 30 3030 31 31 33 33 3436 37 40 44 46 5660

30275640303631242325293329223329462534192323442930252360252737242231242627

191312222332234341243371254401262441273461294561303601

line plot

stem plot

605+654+640 43+730 0 0 1 1 3 3 42+5 5 5 5 6 6 7 7 7 9 9 9 922 2 3 3 3 3 4 4 41+9

n =37706050403020101424box plot

111111111111n =110100908070605040 11

176659374552909581879139751768568471748869735707866908469363798081786918377708838292846997875789194108571867462118155787071

histogram

107736897767994499857546571808488626179986662798668746182659862116658864797879778674867380687889725869927888771038863688881759062897171747074766581756294718584836381627983936165629265837070817772846759587866669477636675687690787110178435967617196756476727774658286668696898171859959926872776087847577514585678780849369768975836872679289829677102749176836668617372767377799463596271816573636389826485926473

43-48248-53153-58258-632163-682868-732873-783378-832683-882188-931993-981098-1036103-1082108-1130113-1181

7878-83SPSS15 55=-

113.3105.096.788.380.071.763.355.046.76050403020100 9

xyxy1 2 3 4 5 6 7 8 9 101112131415156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

18017016015014060504030scater

8

1942.59520.91316.89179.68232.90448.38358.64185.65890.28109.4185.4162.4553.92148.18233.2334.27 4185.641617.15

raddar chart

1 5 [2]

2[3]

107736897767994499857546571808488626179986662798668746182659862116658864797879778674867380687889725869927888771038863688881759062897171747074766581756294718584836381627983936165629265837070817772846759587866669477636675687690787110178435967617196756476727774658286668696898171859959926872776087847577514585678780849369768975836872679289829677102749176836668617372767377799463596271816573636389826485926473

3 11[4]

176659374552909581879139751768568471748869735707866908469363798081786918377708838292846997875789194108571867462118155787071

[5]4 12

/kg/L/kg/L422.55503.41422.20503.10462.75523.46462.40522.85462.80583.50502.81583.00

[1]5

1 2 3 4 5 6 7 8 9 101112131415131313131313131313131314141414156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930141414141415151515151515151515164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

frequency distribution relative frequency distributionpercent frequency distributionbar graphpie charthistogramcumulative frequency distributionclass midpointstem and leaf displayscatter diagrambox plotthe face of chernoff

01020 Mo= 50 Mo=Mo=

%1938 1326816510 510 50100

%8.83.2100.0

200200

43-48248-53153-58258-632163-682868-732873-783378-832683-882188-931993-981098-1036103-1082108-1130113-1181

Me=

%24108934530836311510241322252703003002761687530 300100--

200200

43-482248-531353-582558-63212663-68285468-73288273-783311578-832614183-882116288-931918193-981019198-1036197103-1082199108-1130199113-1181200

low quartileupper quartile50%50%50%

QL= QU= Me=

%24108934530836311510241322252703003002761687530 300100--

Me=75.5QU=85QL=6750%67-85200

43-482248-531353-582558-63212663-68285468-73288273-783311578-832614183-882116288-931918193-981019198-1036197103-1082199108-1130199113-1181200

x Arithmetic mean=30

30275640303631242325293329223329462534192323442930252360252737242231242627

200

fx43-48245.548-53150.553-58255.258-632160.563-682865.568-732870.573-783375.578-832680.583-882185.588-931990.593-981095.598-1036100.5103-1082105.5108-1130110.5113-1181115.5

1.02.+-

=19.2

27.023.941.633.140.618.812.728.913.214.527.034.828.93.250.16028.815.07.25.116.713.719.111.115.610.05.61.533.98.3

Mo=Mo= 01020 01020

Me= Me=

50

%1938 1326816510 510 50100

QL= QU=12345 50%

%%2410893453083631151021997864387332621.312.7 300100300100

QU=85QL=6718200 50% QU=85QL=67

43-482248-531353-582558-63212663-68285468-73288273-783311578-832614183-882116288-931918193-981019198-1036197103-1082199108-1130199113-1181200

30275640303631242325293329223329462534192323442930252360252737242231242627

50

xf105-110110-115115-120120-125125-130130-135135-140107.5112.5117.5122.5127.5132.5137.535814106415.710.75.70.74.39.314.347.153.545.69.843.055.857.2 -50-312

n s22 n-1 12 nn-1112

50

xf105-110110-115115-120120-125125-130130-135135-140107.5112.5117.5122.5127.5132.5137.5358141064246.49114.4932.490.4918.4986.49204.49739.47572.45259.926.86184.90518.94817.96 -50-3100.5

=6.00S=3.00=6.00S=2.71=6.00S=0.82S=0.00=6.00

34.4-2s=20.634.4X-s=27.534.4+2s=48.22730=34.4=6.9 4 22421273033363942454824685134.4+s=41.3

standard score 10030.09.032.410.01919

xi 1221303948Zi -2.00-1.0001.002.00

Tchebysheff1-1/z2 z z1

68%95%100% 68%195%23

8

1234567817022039043048065095010008.112.518.022.026.540.064.069.0

---------

55-1010-1515-2020-2525-3030-3535-4040-4545-50502.57.512.517.522.527.532.537.542.547.552.52.2812.4520.3519.5214.9310.356.564.132.681.814.91-154.64-336.46-144.87-11.840.1823.1689.02171.43250.72320.741481.81-1001689.25

43 4 =3 43

55-1010-1515-2020-2525-3030-3535-4040-4545-50502.57.512.517.522.527.532.537.542.547.552.52.2812.4520.3519.5214.9310.356.564.132.681.814.912927.154686.511293.5346.520.20140.62985.492755.005282.948361.9846041.33-10072521.25

Frequencies descriptive statistics Explore SPSS

[1] descriptive statistics

1 2 3 4 5 6 7 8 9 101112131415131313131313131313131314141414156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930141414141415151515151515151515164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

descriptive statistics

Frequencies

Explore

meanmedianmodepercentilep%100-p%50 quartile 255075123425%hinges13rangeinterquartile range,IQR31variancestandard deviationZz-scorechebyshers theoremempirical rule123outlier

five-number summary13box plot1350%31covariancecorrelation coefficientweighted meangrouped dataskewnesskurtosis

samplepopulationsamplingpopulation sizeN=45sample sizen=10

100050030

central limit theorem n n30

X n=2n=5n=30

0.050.100.150.200.250.302600340042005000 s2 n-1

XN225152915131600152711112

921.40.1595%

1002695%36

100263495%

n30ssn30

20014095%

permissible 180000095%500 P 0.05 95%0.50.5=0.25

0=8.90655=32.85230.0250.025192 200.002595%

0=2.7044=19.02280.0250.02592 1042 95%

sampling without replacementsampling with replacementsampling distributionpoint estimatepoint estimatorstandard errorcentral limit theoreminterval estimatesample errorconfidence levelmargin errortt distribution degrees of freedomt t n-1n

10%5%1%

25031002512500.32502502503

0250.6249.42500.32510.9545

02500.32.00-2.003.33

2503100251=0.05=0.01=0.0455

H0H0H0H0

/2 + /2 =1-

0.20.2

12H00.2H1 0.2H0 0.2H1 0.2 0.2H00.2H0 0.20.2H00.2H0

0.081mm0.025mm2000.076mm

100020100960

12003001001245

P PP /2H0

P P H0P

0.081mm0.025mm2000.076mm

100020100960

12003001001245

5cm105.3cm0.3cm0.01

40000km12041000km5000km=0.05

=0

2

2

20%400300100=0.05

n130n230 11 22 n11 n221 2

s11 s22 n11 n221 2n130n230

n1+n2-2ts11 s22 n11 n221 21 =2

8kg10kgn1=32n2=40 =50kg =44kg=0.05

1026.112817.610.51=2

p1-p2p1-p2

60184014=0.05

112212

()didiH0=0.05n-1=5tt0.025=2.571t2.571t2.571H0

12di1234566.05.07.06.26.06.45.45.26.55.96.05.80.6-0.20.50.30.00.6

TTTSPSS

T 1215160.0 0.051 [1]

1 2 3 4 5 6 7 8 9 101112131415131313131313131313131314141414156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930141414141415151515151515151515164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

P 0.653 0.05 160.0cmT

0.10T2 [1]

1 2 3 4 5 6 7 8 9 101112131415131313131313131313131314141414156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930141414141415151515151515151515164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

P=0.1440.1090%T

7 3 [6]T

1212345676573733073567334363726433760

0.624T

P= 0.002 0.05 P 0.002T

null hypothesisalternative hypothesistypeerror type error critical valuelevel of significance one-tailed testtwo-tailed testP-p-value

420

1234687557792793245333114110012090110420

RC

C1C2C3C4R1f11f12f13f14RT1R2f21f22f23f24RT2R3f31f32f33f34RT3CT1CT2CT3CT4n

123468755779279%68.062.563.371.866.432453331141%32.037.536.728.233.610012090110420%100100100100100

observed frequency f0 expected frequency fe

1234687557792793245333114110012090110420

1234668060732793440303714110012090110420

04260.000.050.100.150.250.208100.30 3 1 10 20

=R-1C-1=2-12-1

C1C2R1f11f12RT1R2f21f22RT2CT1CT2n

=

1234687557792793245333114110012090110420

3426

C1C2C3C4R1f11f12f13f14RT1R2f21f22f23f24RT2R3f31f32f33f34RT3CT1CT2CT3CT4

C1C2C3C4R1f11f12f13f14RT1R2f21f22f23f24RT2R3f31f32f33f34RT3CT1CT2CT3CT4

C1C2C3C4C5C6R1f11f12f13f14f15f16RT1R2f21f22f23f24f25f26RT2CT1CT2CT3CT4CT5CT6

687557793245333166806073344030372-5-36-253-64259364259360.06060.31250.15000.49320.11760.62500.30000.9730

3.0319

6.2513.0319 3 =0

2040208030301070507030150

%%%%2025.04050.02025.0801003042.93042.91014.2701005033.37046.73020.0150100

H0observed frequency f0expected frequency fe

26.6737.3316.008023.3332.6714.0070507030150

2040208030301070507030150

555R-1C-1

20402030301026.6737.3316.0023.3332.6714.00-6.672.674.006.67-2.67-4.0044.497.1316.0044.497.1316.001.670.191.001.910.221.14

6.13

2 222

C1C2R1aba+bR2cdc+da+cb+dn

22


2222

C1C2R1a0a+bR20dc+da+cb+dn

C1C2R10ba+bR2c0c+da+cb+dn

22


22222 1

62520683136076311239859691954

2 2C

223344C0.70710.81650.87

500C

152642414026059521713506574189162188150500

C

11122233312312312352642460595250657445.3652.6442.0055.4064.3051.3061.2471.0656.706.6411.36-18.004.60 5.300. 70-11.24 -6.0617.3044.09129.05324.0021.1628. 090.49126.3436.72299.290.972.457.710.380.440.012.060.525.2819.82

2 2VC1V=0V=1V

[7]

1234687557792793245333114110012090110420

12data2weight casesweight cases3weight cases by4Fofrequency variable

1analyze2descriptive statistics3crosstabs

V chi-square

crosstabs cell displaycountobservedexpected percentagesrowcolumntotal

390[8]

C fo r 7070160602010250140 14022030390

contingency table:chi-square distribution:observed frequency :expected frequency: coefficient of contingency:C22

5

1234527.925.128.524.226.526.528.725.129.127.231.228.330.827.929.630.829.632.431.732.8 =26.44 =3.298 =27.32=2.672 =29.56=2.143=31.46=1.658 =28.695

1.:2.:3.

H02H02 2 H02 H021H0 /=25.6152/2.4428=10.486

(25.25)(5.5)(2.1)F0

316F3.2410.486

618

123123456857582767185717573746982596462697567

112323

123123456857582767185717573746982596462697567

123123456857582767185717573746982596462697567797466

12312345685758276718571757374698259646269756779746634203273

SSdfMSFSSTRSSESSTr-1nT-rnT-1MSTRMSEMSTTR/MSE

SSdfMSF51643094621517258.0028.679.00

= + = +

tnT-rt-least significant difference LSD

Fisher LSDMSEnT-rt

1234527.925.128.524.226.526.528.725.129.127.231.228.330.827.929.630.829.632.431.732.826.4427.3229.5631.46

trbl12345123452022241626121014422202018816101218620146101810

trbl1234512345202224162612101442220201881610121862014610181015.214.016.810.418.821.612.416.413.211.615.04

SSdfMSFSSTRSSBLSSESSTr-1k-r(r-1)(k-1)nT-1MSTRMSBLMSEFtrFbl

SSdfMSF335.36199.36346.24880.9644162483.8449.8421.643.8743072.303142l

[9]

1234527.925.128.524.226.526.528.725.129.127.231.228.330.827.929.630.829.632.431.732.8

F=10.544P =0.000 0.05

=0.255P=0.856 0.054

ANOVAANOVA table:multiple comparison procedures:factor:treatment:mean square: Fleast significant difference LSD:

235325

1234527.925.128.524.226.526.528.725.129.127.231.228.330.827.929.630.829.632.431.732.826.4427.3229.5631.46

123456789101112131415161101201201644301921752634293182492811601472101204668191112122621111614995

103020100220200180160140120100806040

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10

xyx2y2xy1234567891026881216202022265810588118117137157169149202436646414425640040048467633641102577441392413689187692464928561222014569761166307049441404219231403380327852521401300252860090221040

Xyx1x2xny1y2yn

3020100220200180160140120100806040

101016000=

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xyx2xy123456789102688121620202226581058811811713715716914920243664641442564004004846761166307049441404219231403380327852521401300252821040

3020100220200180160140120100806040

3020100220200180160140120100806040F

ANOVA10ANOVAF=74.2511.26 1010F

FSSS1n-2n-1S/1S/n-2-S/1 S/n-2

F1420015301573018914200/11530/814200191.25=74.25F0.01=11.26

1234567891026881216202022265810588118117137157169149202

3020100220200180160140120100806040 SS

10 90.27%

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1-=95%101000010000

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1-=95%10 1000095%(98.585121.415) 95%

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3020100220200180160140120100806040

1-=95%1095%(76.124143.875) 95% 10000

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i yi i

10

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y x x y

95%-2+2 I = i i

10x -2+2

10y2202001801601401201008060401.51.0.50.0-.5-1.0-1.5-2.0 -2+2

011010 10-1.55101010nnnn=10

12345-1.55-1.00-0.65-0.37-0.126789100.120.370.651.001.55

10 45

-1.55-1.00-0.65-0.37-0.12-1.7114-1.0792-0.9487-0.2372-0.22960.120.370.651.001.55-0.22960.71151.07921.22241.4230

xiyi112333445645555075404530352515

4375330

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x xy

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10

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66.4%66.4%F=15.81P0.004yx1 b0=1.274 b1=0.0678

66.4%66.4%

x1x2y123456789101005010010050807565909043422234329.34.88.96.54.26.27.46.07.66.1

90.4%90.4%F=32.878P0.0000.05yx1x2b0=-8.69 b1=0.06113 b2 =0.923

b1=0.06780.0678 b1=0.061130.06113

x1x2y123456789101005010010050807565909043422234329.34.88.96.54.26.27.46.07.66.1

r2 r2 r2

F0Pn-p-1

95% 1 2

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95%

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/%10002000300035004000450050005.26.56.88.110.210.313.0

Dependent variable.. Y Method.. LINEAR

F =38,81105 Signif F = ,0016

-------------------- Variables in the Equation -------------------- Variable B T Sig T

X ,001813 6,230 ,0016(Constant) 2,628144 2,554 ,0510

Dependent variable.. Y Method.. COMPOUND

F =79,53807 Signif F = ,0003

-------------------- Variables in the Equation --------------------

Variable B T Sig T

X 1,000219 40707,209 ,0000(Constant) 4,003242 11,514 ,00016000500040003000200010000141210864ObservedLinearCompoundR Square=,88587R Square=,94086

[1]

1 2 3 4 5 6 7 8 9 101112131415131313131313131313131314141414156.0155.0144.6161.5161.3158.0161.0162.0164.3144.0157.9176.1168.0164.5153.047.537.838.641.643.347.347.147.033.833.849.254.550.044.058.0161718192021222324252627282930141414141415151515151515151515164.7160.5147.0153.2157.9166.0169.0170.0165.1172.0159.4161.3158.0158.6169.044.153.036.430.140.457.058.551.058.055.044.745.444.342.851.1

Bivariate

0.618P0.000

[10]

numberareaheightweight15.38288.011.025.29987.611.835.35888.512.045.29289.012.355.60287.713.166.01489.513.775.83088.814.486.10290.414.996.07590.615.2106.41191.216.0

Linear

95%-ANOVA-

DfBetaDfFit

0.9500.9020.87487.4%

P0.000

0.184P0.014,= -2.856 +0.06870+0.184

SPSS[11]

xy157.1276.0390.9493.0596.7695.6796.2

8765432101101009080706050

S logistic

Linear

quadratic

compound

growth

logarithmic

exponential

inverse

logisticlogistic

observation(


List wise Deletion of Missing Data

Multiple R .84512R Square .71423Adjusted R Square .65708Standard Error 8.67640

Analysis of Variance:

DF Sum of Squares Mean Square

Regression 1 940.76036 940.76036Residuals 5 376.39964 75.27993

F = 12.49683 Signif F = .0166


Variable B SE B Beta T Sig T

X 5.796429 1.639686 .845124 3.535 .0166(Constant) 63.314286 7.332897 8.634 .0003

Dependent variable.. Y Method.. LOGARITH

List wise Deletion of Missing Data





F = 52.31786 Signif F = .0008



X 20.670405 2.857749 .955388 7.233 .0008(Constant) 61.325923 3.923774 15.629 .0000

Dependent variable.. Y Method.. QUADRATIC

Listwise Deletion of Missing Data





F = 65.20449 Signif F = .0009



X 21.825000 2.795779 3.182101 7.806 .0015X**2 -2.003571 .341559 -2.391123 -5.866 .0042(Constant) 39.271429 4.878435 8.050 .0013

8765432101101009080706050

[12]

/%10002000300035004000450050005.26.56.88.110.210.313.0







F = 38.81105 Signif F = .0016



X .001813 .000291 .941209 6.230 .0016(Constant) 2.628144 1.029003 2.554 .0510

Dependent variable.. Y Method.. QUADRATI


Multiple R .98064R Square .96166Adjusted R Square .94248Standard Error .65142



Regression 2 42.571164 21.285582Residuals 4 1.697407 .424352

F = 50.16022 Signif F = .0015



X -.000865 .000971 -.449073 -.891 .4233X**2 4.46826139E-07 1.5892E-07 1.417274 2.812 .0482(Constant) 5.842884 1.323595 4.414 .0116

Dependent variable.. Y Method.. EXPONENT


Multiple R .96998R Square .94086Adjusted R Square .92903Standard Error .08484



Regression 1 .57255978 .57255978Residuals 5 .03599281 .00719856

F = 79.53807 Signif F = .0003



X .000219 2.4566E-05 .969977 8.918 .0003(Constant) 4.003242 .347693 11.514 .0001

6000500040003000200010000141210864ObservedLinearQuadraticExponential

independent variable:xdependent variable:ysimple linear regression:regression model: regression epuation :estimated regression equation:scatter diagram:least squares method:coefficient of determination:residual:correlation coefficient:mean square error:standard error of the estimate:ANOVAANOVA table:Fconfidence interval estimate:xyprediction interval estimate:xyresidual analysis:

residual plots:standardized residual :normal probability plotoutlier:influential observation:high leverage points:multiple regression:multiple regression model:estimate multiple regression equation:multiple coefficient of determination:adjusted multiple coefficient of determination :multicollinearity:

66120 1 2A B C D A B C D

3 4A B C D A B

6 SPSS\data1-1.sav

61204Exceldata2-1xls51201205numberEnglishmatheconomicsstatistics ExcelSPSSSPSSdata1-1savSPSSdata2-2.sav SPSS

1.13002007 2007122831724V1V24V1V2V3V4V5V615V7V8V9V10V11V12V13V14V15V16V17V18V19V20V21V22V23V24\data3-1.sav 24V24V5V6

ASPSSA3284020071228\data4-1.sav 20VAR1VAR2VAR3VAR4AVAR5BVAR6VAR7VAR8VAR9VAR10VAR11VAR12VAR13VAR14VAR15VAR16VAR17VAR18VAR19VAR20VAR1820071228VAR19 20

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T 2006495 QYZYZTRKSJZMJ46 952QYZYZT\data6-1.sav 20103030

T 4020401 NUMSCORECLASS34012\data7-1.sav

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20061998199819992000 10000 8071\data11-1.savefficiencylegalworkwayservicedeciplinetotal68 071612

2090Y=AF(KHN)(K)(N)(A) 19852002GDPxGDPyGDP 1985GDP\data12-1.savnGDPyx321GDPyx

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1985200420052006 ny22019852004201220data14-1.sav

502-1-1.sav

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[] 12AnalyzeRegressionLinear3[] 12AnalyzeRegressionLinear3[] 12 AnalyzeRegressionLinear3

[] 1 AnalyzeRegressionLinear2345

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[] 2 99341 902\data2-2-1.sav []1 2 1 23 12

[] 1 2 AnalyzeDescriptive StatisticsCrosstabs34 0.05[] 1

2 AnalyzeCompare MeansMeans

3 AnalyzeCompare MeansOne way ANOVA 4 FP

1

2

3F0.01

4SPSS

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统计学（含SPSS）

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