SPSS[ ] WindowsXPSPSS11.0SPSS11.0
SASSPSSS-PLUSMINITABEXCELSAS SPSSSPSS SPSSSPSS
4- -- - 8()12345678SPSSSPSS
SPSS
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
5
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
36
63
=
+-
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
SPSS
5
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
J
J
U
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
3
8
1942.59520.91316.89179.68232.90448.38358.64185.65890.28109.4185.4162.4553.92148.18233.2334.27 4185.641617.15
raddar chart
SPSS
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
SPSS
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.+-
12
=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
descriptive statistics
descriptive statistics
descriptive statistics
Frequencies
Frequencies
Frequencies
Frequencies
Frequencies
Frequencies
Frequencies
Explore
Explore
Explore
Explore
meanmedianmodepercentilep%100-p%50 quartile 255075123425%hinges13rangeinterquartile range,IQR31variancestandard deviationZz-scorechebyshers theoremempirical rule123outlier
five-number summary13box plot1350%31covariancecorrelation coefficientweighted meangrouped dataskewnesskurtosis
SPSS
samplepopulationsamplingpopulation sizeN=45sample sizen=10
100050030
central limit theorem n n30
X n=2n=5n=30
p p
0.050.100.150.200.250.302600340042005000 s2 n-1
51800
XN225152915131600152711112
25%
921.40.1595%
1002695%36
100263495%
n30ssn30
P1-
20014095%
permissible 180000095%500 P 0.05 95%0.50.5=0.25
2
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
SPSS
P
10%5%1%
25031002512500.32502502503
0250.6249.42500.32510.9545
02500.32.00-2.003.33
0Z
2503100251=0.05=0.01=0.0455
H0H0H0H0
/2 + /2 =1-
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
100020100960
12003001001245
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
T T T
T
T
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
T
T
T
T
P=0.1440.1090%T
7 3 [6]T
1212345676573733073567334363726433760
T
T
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
SPSS
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
=
CV
2 222
C1C2R1aba+bR2cdc+da+cb+dn
22
C1C2R1aba+bR2cdc+da+cb+dn
2222
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C1C2R10ba+bR2c0c+da+cb+dn
22
C1C2R1aba+bR2cdc+da+cb+dn
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
SPSS
[7]
1234687557792793245333114110012090110420
12data2weight casesweight cases3weight cases by4Fofrequency variable
1analyze2descriptive statistics3crosstabs
V chi-square
crosstabs cell displaycountobservedexpected percentagesrowcolumntotal
390[8]
C fo r 7070160602010250140 14022030390
X Y
contingency table:chi-square distribution:observed frequency :expected frequency: coefficient of contingency:C22
SPSS
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.
22
: 222
H02H02 2 H02 H021H0 /=25.6152/2.4428=10.486
(25.25)(5.5)(2.1)F0
316F3.2410.486
618
123123456857582767185717573746982596462697567
F
112323
123123456857582767185717573746982596462697567
123123456857582767185717573746982596462697567797466
12312345685758276718571757374698259646269756779746634203273
SSdfMSFSSTRSSESSTr-1nT-rnT-1MSTRMSEMSTTR/MSE
SSdfMSF51643094621517258.0028.679.00
= + = +
215F
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
F
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
416F
SPSS
[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:
SPSS
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
1234567891026881216202022265810588118117137157169149202
10
xyx2y2xy1234567891026881216202022265810588118117137157169149202436646414425640040048467633641102577441392413689187692464928561222014569761166307049441404219231403380327852521401300252860090221040
:
XY
,
Xyx1x2xny1y2yn
3020100220200180160140120100806040
101016000=
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xyx2xy123456789102688121620202226581058811811713715716914920243664641442564004004846761166307049441404219231403380327852521401300252821040
3020100220200180160140120100806040
F
F
3020100220200180160140120100806040F
FF
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%
1234567891026881216202022265810588118117137157169149202
1-=95%101000010000
1234567891026881216202022265810588118117137157169149202
3020100220200180160140120100806040
n-2t
1-=95%10 1000095%(98.585121.415) 95%
1234567891026881216202022265810588118117137157169149202
95%
3020100220200180160140120100806040
n-2t
1-=95%1095%(76.124143.875) 95% 10000
1234567891026881216202022265810588118117137157169149202
95%
i yi i
10
268812162020222658105881181171371571691492027090100100120140160160170190-1215-1218-3-3-392112
yx
x x x
10x x
y x x y
10y 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
45045
xiyi112333445645555075404530352515
2
4375330
xiyi112333445645555030404530352515
xy
x xy
xiyi10101520202570125130120115120110100
YX1X2YX1X2
yX1Xpy1y2ynx11x21xn1x1px2pxnp
10
x1y12345678910100501001005080756590909.34.88.96.54.26.27.46.07.66.1
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
F
r2 r2 r2
F0Pn-p-1
95% 1 2
X1X2505050100100100234234
x1x2y123456789101005010010050807565909043422234329.34.88.96.54.26.27.46.07.66.1
95%
X1X25050501001001002342343.1464.1274.8156.2587.3858.1354.9245.7896.9487.9268.6459.7422.4143.3684.1575.5006.5207.3625.6566.5487.6078.6839.51010.515
/%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
S
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
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
FF
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
cubic
ss
exponential
inverse
power
logisticlogistic
observation(
Dependent variable.. Y Method.. LINEAR
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
-------------------- Variables in the Equation --------------------
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
Multiple R .95539R Square .91277Adjusted R Square .89532Standard Error 4.79374
Analysis of Variance:
DF Sum of Squares Mean Square
Regression 1 1202.2604 1202.2604Residuals 5 114.8996 22.9799
F = 52.31786 Signif F = .0008
-------------------- Variables in the Equation --------------------
Variable B SE B Beta T Sig T
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
Multiple R .98501R Square .97024Adjusted R Square .95536Standard Error 3.13044
Analysis of Variance:
DF Sum of Squares Mean Square
Regression 2 1277.9614 638.98071Residuals 4 39.1986 9.79964
F = 65.20449 Signif F = .0009
-------------------- Variables in the Equation --------------------
Variable B SE B Beta T Sig T
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
Dependent variable.. Y Method.. LINEAR
Listwise Deletion of Missing Data
Multiple R .94121R Square .88587Adjusted R Square .86305Standard Error 1.00521
Analysis of Variance:
DF Sum of Squares Mean Square
Regression 1 39.216356 39.216356Residuals 5 5.052216 1.010443
F = 38.81105 Signif F = .0016
-------------------- Variables in the Equation --------------------
Variable B SE B Beta T Sig T
X .001813 .000291 .941209 6.230 .0016(Constant) 2.628144 1.029003 2.554 .0510
Dependent variable.. Y Method.. QUADRATI
Listwise Deletion of Missing Data
Multiple R .98064R Square .96166Adjusted R Square .94248Standard Error .65142
Analysis of Variance:
DF Sum of Squares Mean Square
Regression 2 42.571164 21.285582Residuals 4 1.697407 .424352
F = 50.16022 Signif F = .0015
-------------------- Variables in the Equation --------------------
Variable B SE B Beta T Sig T
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
Listwise Deletion of Missing Data
Multiple R .96998R Square .94086Adjusted R Square .92903Standard Error .08484
Analysis of Variance:
DF Sum of Squares Mean Square
Regression 1 .57255978 .57255978Residuals 5 .03599281 .00719856
F = 79.53807 Signif F = .0003
-------------------- Variables in the Equation --------------------
Variable B SE B Beta T Sig T
X .000219 2.4566E-05 .969977 8.918 .0003(Constant) 4.003242 .347693 11.514 .0001
6000500040003000200010000141210864ObservedLinearQuadraticExponential
SPSS
N-1/N
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
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ASPSSA3284020071228\data4-1.sav 20VAR1VAR2VAR3VAR4AVAR5BVAR6VAR7VAR8VAR9VAR10VAR11VAR12VAR13VAR14VAR15VAR16VAR17VAR18VAR19VAR20VAR1820071228VAR19 20
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T 4020401 NUMSCORECLASS34012\data7-1.sav
T 3535 (hcq)hch35\data8-1.sav
100100 rowscolumns200rows0=1=columns0=1=\data9-1.sav
24884488448844324 promotservicesales324promotservicepromot0=1=2=service0=1=data10-1.sav
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
[] 121 SPSSGraphs2 Graphs3 AnalyzeDescriptive statisticsFrequencies34[]
[] 12AnalyzeRegressionLinear3[] 12AnalyzeRegressionLinear3[] 12 AnalyzeRegressionLinear3
[] 1 AnalyzeRegressionLinear2345
195%40 00032 EnterStepwise3436
SPSS12345678
2 993141 1A 2B 3C 4D2 1 23 4 1 23 45 1 23 45 661 2
7
8 1 2
9
10
11
12 1 2 3 4 5 6 7 8
131 2
141 2
[] 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
5
SPSS12345678
XYYEYn-212
1
1066.4%10
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