ƱřźƿřżƿřƺŤǀŤƀƳř. SPSS : Statistical Package For Social Science. żĩźưţɔųśƃ

SPSS : Statistical Package For Social Science íëæææí±µáyä m ƶǀƫƹřƞƿźśƙţ ΪηΎΑϲϣήϳΩΎϘϣϩΪϨϫΩϥΎθϧέϮϧϭέΰΑέϮϧˬϮϨϣΖγήϬϓˬϥϮϨϋέϮϧϞϣΎηέΰϓϡήϧϦϳ Ζ γrecord ϳήτγήϫϭΪηΎΑϲϣΡήτϣvariableΎϳήϴϐΘϣϥϮϨϋϪΑϪ Ζγfield ϳϥϮΘγήϫ ΪηΎΑϲϣΡήτϣcace ΎϳΩέϮϣ ϳϥϮϨϋϪΑϪ ή ϫζ γϫτϔ λϧ ϳΕΎ ϋϼσζϳή ϳϭSPSSϪ ϣύϧήαέύ ϭϊ ϨϳϮ ϲϣdatasheetϩϊηίύαϩήπϩ ϪΑ ΩέΩΖϤδϗϭΩDatasheet ϢϴϨ ϲϣωέϭϥέωέήϳωύϙϣϫ Data view ϢϴϨ ϲϣϒϳήόηέύϫήθϐθϣϫ Variable view ή ϴϐΘϣϪ έύ Αή ϫϭζ γύϭϧαύ ϴϘϣΎ ϬϧΖϴ λύχϧϳή ΘϤϬϣϪ ΪϧέΩϲΗΎϴλϮμΧΎϫήϴϐΘϣSPSSέΩ ϢϴϨ ϒϳήόΗέϥαΎϴϘϣΪϳΎΑΩϮηϲϣϒϳήόΗ ŚƷŽŚǀƤƯƕřƺƳř Nominal ϱέϯλϲϥγαύθϙϣ έωωϯ ηϲϥ γαύ ϴϘϣϪ ΑϞϳΪΒΗϭϩΩΩΪ ϥϫαϣθϧϯθθϣϫ ϢϴϨ ϲϣϲ ΪϧίϥέΩϪ ϱήϭηϊϩϧύϣ ϩϭή ΐ γύϩϣϭωή ϱέά Ϊ ϥϯηϲϣέϯσήϫϭωέϊϧϲηϭύϔηωϊϋϥωϯα έΰαϭ Ϯ αύθϙϣϧϳ ΩέΪϧϲϨόϣϥέΩϊϤΟϭϦϴ ϧύθϣϭζγϱϊϩα OrdinalϲΒϴΗήΗ ϱϫβηέαύθϙϣ Ύ ΠΑΎΟΎ ϫϊ ή Ϊ ϫωϲ ϣϥύθ ϧέΐ ϴΗήΗΎ ϳΖϳϮϟϭϭΪηΎΑϲϣΖϴϤϫϪΟέΩαΎγήΑϱέά Ϊ ΩέΪϧΩϮΟϭΎϬϫϭή ϦϴΑ ήθ ϩίϊϧϟαύϗϫϡλύϓϲϟύϋϭϊαˬώϯχεέύϭϣϊϩϧύϣϊϩ ϲϣϕήϓϊϧϯη ScaleϲΒδϧαΎϴϘϣ. ΩήϓΪϗϞΜϣΩέΩϲΒδϧαΎϴϘϣΪϳΎϴΑΖγΪΑϱήϴ ϩίϊϧϖϳήσίϫ ϱωϊϋήϫ żĩźưţɔųśƃ έύ ΑϲΒδ ϧαύ ϴϘϣϱή Ας ϘϓϪ ΖγMeanϦϴ ϧύθϣϥϧϳήθϓϭήόϣϭζγύϫϩωωΰ ήϥηϫϧύθϧ Ύ ϫϩωωή ϲϟϭωϭέϲϣϥϑήσϫαϊηύα έΰαήϳωύϙϣή ϭζγαύδσύϫϩωωϫαέύθδαωϭέϲϣ ΩϮΑΪϫϮΨϧΎϳϮ Ϧϴ ϧύθϣϊϩηύαϩϊϩ ή ϲϡθχ ή Ϊ ϳϲ ϣζ γϊαϫ ΒΗέϱϭέίϭΖ γύ ϫϩωω ϧύ ϴϣϪ ΒΗέϪ ΖγMedianϪϧΎϴϣή ϳΩκΧΎη n 1 έΰ ΑϩΩΩϪΑϪϧΎϴϣΖγϪϧΎϴϣϲτγϭΪηΎΑΩήϓή ϭζγϫϧύθϣ ϪϴΗέΪηΎΑΝϭίΎϫϩΩΩΩΪόΗ ΪηΎΑϲϤϧαΎδΣ Ϯ Ύϳ ϲϧϭήϓϥΰθϣωϭέϲϣέύ ΑϢϫNominalΎϳϲϤγϱΎϫϩΩΩϱήΑϪ ΖγModeΪϣή ϳΩκΧΎη ΩέΩέΕΪϫΎθϣϦϳήΘθϴΑϭΪϫΩϲϣϥΎθϧέΎϫϩΩΩ 1

ƾĭŶƴĩřźěƆųŚƃ βϧύ ϳέϭϥϦϳή ΘϓϭήόϣϭΩϭή ϴϣέΎ ΑScaleΎ ϳϲΒδ ϧϱύ ϫϩωωϱή ΑϭΖγΎϫϩΩΩϲ ΪϨ ή ϪϧΎθϧ Mean Ϧϴ ϧύ ϴϣΖ γϩωωή ϫϲ ϧϭήϓ = xi xi f Ϫ Ζ γ f i i n fi variance = ( x i x) βϧύϳέϭ ( x i x) 0 n ϡή Ϯ Ϡϴ Ύ ϫϩωωϊ Σϭή Ζ γϲ ϳΎ ϫϩωωϊσϭύαϥϊσϭϫ ΖγέΎϴόϣϑήΤϧβϧΎϳέϭέάΟϭ ϩωωϥϯ ΪϨΘδ ϫϲαϯ ΧϱΎ ϫκχύηέύθόϣϑήτϧϭβϧύϳέϭζγϡή ϮϠϴ ϢϫέΎϴόϣϑήΤϧΪηΎΑ ΪϧήΛϮϣϥέΩΎϫ R=max-min ΪηΎΑϲϣRangeΎϳΕήϴϴϐΗϪϨϣΩϲ ΪϨ ή ή ϳΩκΧΎη ϲ έύ ήβϧύθϣϭϣθϩ ϲϣϩωύϔθγϥίϊϩηύαϩϊϩ ή ϲϡθχύϫϩωωή Ϫ Ζγ έύ ή ϳΩκΧΎη R Q 3 Q 1 Ϫ ϧύθϣϥύϥϫϡϭω έύ ϭϊϩθδϫήθθθαϥίύϫϩωω ϭήθϥ ϥίύϫϩωω ϲϩόϳϡϯγ έύ Ζγ ΪηΎΑϲϣϝΎϣήϧϪόϣΎΟϥΪϨηΎΑ ϳΩΰϧϢϫϪΑΎϫϩΩΩϦϴ ϧύθϣϭϫϧύθϣή ϝύμϣ κχύ ηβ γϊ ϴϨ ΩέϭϪΤϔλέΩέήϳίΕΎϋϼσϭϩΩή ϒϳήόΗέϲϠϣκϟΎΧΎϧΪϣέΩϭέϮθ ϡύϧϱύϫήθϐθϣ ΪϴϳΎϤϧϪΒγΎΤϣέϲ ΪϨ ή ϭϊϳίϯηˬϱέύϣϒϡθψϣϱύϫ ϲϡϣκϟύχύϧϡέω έϯθ ϡύϧ ϥήϳ Ϫϴ ήη ϥύθδϧύϐϓ ϕήϋ ϥύθδ Ύ :ϢϴϨ ϲϣϒϳήόηvariable viewέωέύϫήθϐθϣ countryϡύϧϫαύϫέϯθ ϡύϧϝϭήθϐθϣ ΩϮθϧ ϳΎΗϲγέΎϓϭΩέΪϧϲϟΎ ηϩήθηςχϲϟϭωϯηϩωύϔθγϫϡλύϓίϊϳύβϧήθϐθϣϣγ ϳΎΗέΩ ϢϴϨ ϲϣώύψθϧstringήθϐθϣωϯϧύϳtype έω name of countryˬ ϼΜϣΩΩήϴϐΘϣΩέϮϣέΩϲϓΎ ΕΎΤϴοϮΗϥϮΘϴϣlabelέΩ ϱύ ϫή ϴϐΘϣϱΪ ϨΑΪ ιϯμ ΨϣϭϢϴ ϧίϲ Ϥϧϱΰ ϴ ϩϊ ηϒϳήόηϲϥγήθϐθϣϥϯ valueζϥδϗέω ΪϨηΎΑϱΩΪϋϪ Ζγϲϫϭή έω ϼΜϣΩϮ ηϲ ϤϧϪ Θϓή ή ψϧέωεύβ γύτϣέωϫ ΩϮ ηϲ ϓήόϣϱΩΪ ϋϲ ϨόϳmissingΖϤδ ϗέω ΖϗϭήϫϪ ϢϴϨ ϴϣϲϓήόϣέ ΩΪϋΎϣΖγϩΪθϧή ΎϳΖγΎϧϮΧΎϧΎϫϪϨϳΰ ϲχήαύϫϫϣύϩθγή ΩϮθϧΏΎδΣϭΪηΎΑϥϲϨόϣϪΑΪηΝέΩ ϢϴϨ ϲϣϒϳήόη έϥϯθγϱύϩϭ ςγϭύϳ ˬΖγέϢϴϨ ϴϣΏΎΨΘϧέAlign ϢϴϨ ϲϣώύψθϧϲϥγϭnominalέήθϐθϣαύθϙϣύϳmeasure

1 Name country GNP Type String Numeric 3 GNPΎϳϲϠϣκϟΎΧΎϧΪϣέΩϥΰϴϣϡϭΩήϴϐΘϣ ΩέΩϱΩΪϋήϳΩΎϘϣϪΑιΎμΘΧϪ ϢϴϨ ϲϣώύψθϧnumeric ήθϐθϣωϯϧύϳtype έω Gross National Productˬ ϼΜϣΩΩήϴϐΘϣΩέϮϣέΩϲϓΎ ΕΎΤϴοϮΗϥϮΘϴϣlabelέΩ ϢϴϧίϲϤϧϱΰϴ valueζϥδϗέω ϢϴϨ ϲϣϒϳήόη έϥϯθγϱύϩϭ ςγϭύϳ ˬΖγέϢϴϨ ϴϣΏΎΨΘϧέAlign ϢϴϨ ϲϣώύψθϧϲβδϧϭscale έήθϐθϣαύθϙϣύϳmeasure ΩέϮΧϲϣΰϴϤϣϢϗέ ΎΗϢϴϧΰΑ ή έdecimal Width 8 8 Decimal - Label country GNP Value - - Missing - - column 10 10 Align left left Measure Nominal Scale ΎϫκΧΎηϪΒγΎΤϣϝϭεϭέ Analyze Report Case Summaries ϩή ΠϨ ίζ Ϡϓς γϯηέϱέύ ϣϱύ ϫκχύ ηϣθ ψϩηζ ϬΟή ψϧωέϯϣήθϐθϣϫ ΩϮηϲϣίΎΑϱϩήΠϨ κχύ ηstatistic Ϫ Ϥ ΩϥΩίΎ Αβ γϭϣθ ϫωϲ ϣϝύ ϘΘϧVariable Ζ γέζϥγϩήπϩ ϪΑΩϮΟϮϣ ϭkutosisϲ Ϊϴθ ˬskewnessϲ ϟϯ ˬβϧΎϳέϭˬϦϴ ϧύθϣˬϫϧύθϣϊϩϧύϣέήψϧωέϯϣϱέύϣϱύϫ ϢϴϨ ϲϣώύψθϧ Ϫ ήϫϭϊ ϳΪϨΒϧέϩή ΠϨ ϦϳΖγήΘϬΑΩϮηϲϣήϫΎχoutput1ϡΎϧϪΑϪϧΎ ΪΟϩήΠϨ έωspssϲοϭήχ ΪηΪϫϮΧϪϓΎοϩήΠϨ ϦϴϤϫέΩΪϴϨ ϪϓΎχ ΎϫκΧΎηϪΒγΎΤϣϡϭΩεϭέ Analyze Descriptive statistics - frequencies ϢϴϨ ϢγέΰϴϧέϲϧϭήϓϝϭΪΟϢϴϧϮΗϲϣ ΪϨΘδϫΎϬ ΪλPercentileϭΎϫ ϫωcutpoints ˬΎϬ έύ QuartilesϞϣΎηPercentile Values ϲ ϣϊ ϳίϮΗέΎ ϴόϣ Distributionˬϲ Ϊ Ϩ ή έύθόϣdispersionˬΰ ήϥηϱύϫκχύηcentral Tendency ΪϨηΎΑ ϢϴϨ ΏΎδΣέϲ Ϊϴθ ϭϲ ϟϯ ϢϴϧϮΗϲϣΪϨηΎΑΕϭΎϔΘϣϭϩΪϨ ή ΎϫϩΩΩή ϴΗέDisplay frequency table Ϫ Ϩϳΰ Ϊ ϳΎΑϢϴϫϮ ΨΑέϲ ϧϭήϓϝϭϊ ΟϭΪϨ ηύαϱέή ΗΎϫϩΩΩή ϢϴϧΰΑ ƾĭŶǀƄĩƩŚƯźƳŹŵřƺưƳ ΪηΎΑϲϣkutosis>0ϩΪϴθ ΖϟΎΣέΩϭkutosis<0ϲΨ ΖϟΎΣέΩ ΖγΪϣ!ϪϧΎϴϣ!Ϧϴ ϧύθϣϭskewness<0 ϪΑϲ ϟϯ ΖϟΎΣέΩ Ϧϴ ϧύ ϴϣ!Ϫ ϧύθϣ!ϊ ϣϭ skewness>0ζ γέϫ Αϲ ϟϯ Ζ ϟύσέω Ζγ

ϝύμϣ ϱύ ϫκχύ ηβ γϊθϩ ΩέϭϪΤϔλέΩέήϳίΕΎϋϼσϭϩΩή ϒϳήόΗέϥΎ ϣϭϥύϣίˬϧθηύϣωϯϧϱύϫήθϐθϣ έϲϩθ ηύϣωϯ ϧϫ ςγϯηϭϥύϣίϡϊ έωωωήηϥΰθϣϫπθθϧϊθϳύϥϧϫβγύτϣέϲ ΪϨ ή ϭϊϳίϯηˬϱέύϣϒϡθψϣ ΪϴϳΎϤϧκΨθϣ ΖΧϮγϥΰϴϣ ϥύϣί ϥύ ϣ ϦϴηΎϣωϮϧ ΡήσϞΧΩ ϲμψη ΡήσΝέΎΧ ϲμψη ΡήσϞΧΩ ϲμψη ΡήσΝέΎΧ ϲδ ΎΗ ΡήσϞΧΩ ϲδ ΎΗ ΡήσΝέΎΧ αϯαϯη ΡήσϞΧΩ Ζϧϭ ΡήσΝέΎΧ αϯαϯη ΡήσϞΧΩ ϲδ ΎΗ ΡήσΝέΎΧ ϲμψη ΡήσϞΧΩ Ζϧϭ ΎϫήϴϐΘϣΏΎΨΘϧ Name Type Width Decimal Label Value Missing column Align Measure 1 3 4 car place time fuel Numeric Numeric Numeric Numeric 8 8 8 8 0 0 car place time fuel 1=ca =taxi 3=bus 4=vanet 0=in center 1=out center - - - - 10 10 10 10 left left left left Nominal Nominal ΩέΩΖϴϤϫϲϧϭήϓϝϭΪΟϭΖδϴϧϢϬϣϱέΎϣϱΎϫκΧΎηΪϧϩΪηϱΪϨΑΪ Ϫ ϲϳύϫήθϐθϣϱήα valid percentϭpercentϥϯθ γϭωϧθ Αϲ ϧϭήϓϝϭϊ ΟέΩϢϳΩή ϲ ϣϒ ϳήόΗmissingή ΎΠϨϳέΩΎϣ ΪηϲϣΩΎΠϳΕϭΎϔΗ Ύ ϫϩωωή Ϫ Ζ γϲ όϥπηϊ λέωϩϊ ϨϫΩϥΎθ ϧϲ ϧϭήϓϝϭϊ ΟέΩCumulative percentϥϯθ γ ΩέΪϧϲϨόϣϥϮΘγϦϳΪηΎΑNominal ϢϴϫϮΧϲϣΎϣΎϣΖγϩΪηέή ΗέΎΑ ϳΩέϮϣήϫ ΎΒϳήϘΗϭΪϫΪϴϤϧϥΎθϧϱΰϴ ΖΧϮγϲϧϭήϓϝϭΪΟ ϢϴϨ ϲϣϲϓήόϣέgfuelϡύϧϫαϱϊϳϊοήθϐθϣάϟϣθπϩδαϒϡθψϣϱύϫϫϡλύϓέω Transform-Record-Into Different Variable ϲϣέold and new valueϫϥ Ωβ γˬϩωίέchangeϭϣθϩ ϲϣνέωέϊϳϊοήθϐθϣΐδ ήαϭϡύϧ ΖϤδϗέΩϢϴϧί ϳϪ Α ϳήχΎ ϨΗϭϢϴ ϫωζβδ ϧϊ ϳΪΟή ϴϐΘϣϢϳΪ ϗήθϐθϣέϊϙϣήϫϫαϣθϫϯχϲϣή Old value Ϊ ϳΪΟΞ ϧέ ϳϢϳΪ ϗή ϴϐΘϣέΪ Ϙϣή ϫϫ ΑϢϴϫϮ Χϲϣή ϭϣθϧίϲϣ ϴΗέvalueϪϨϳΰ ϢϴϨ έήϗήα ΎΠϨϳέΩϪ ϢϴϨ ϲϣϩωύϔθγrangeϫϩϳΰ ίϣθϩ ϒϳήόΗ Range<10 1 10<Range<0 0<Range<30 3 Range>30 4 Scale Scale 4

ŵśťſřspss1 ŶŝįŹįŚƣō 5 Ϣϴ Ϩ ϲϣϒϳήόηvalueέωϣθηϥϯθγέωgfuelήθϐθϣϒϳήόηζϥδϗέωβ γϭϣθϧίϲϣέaddϭ ˮΖδϴ ϭ ˬ ˬ ΩΪϋίέϮψϨϣ Measure Align column Missing Value Label Decimal Width Type Name Nominal left 10-1=low =middle 3=high 4=very high Gfuel 0 8 Numeric Gfuel 1 Ύ ΗϢϴ Ϩ ϲ ϣϫβ γύτϣfuelήθϐθϣϱήαέϱέύϣϱύϫκχύηϭgfuel ήθϐθϣϱήαέϲϧϭήϓϝϭϊοϝύσ ΪηΎΑϪΘηΩϲϨόϣ ƾūƹźųƹŪƿŚŤƳŚƿŢſřƮƸƯšŚƗǈƏřŵƹŹƹƾƬƇřƶŰƠƇƵŶƳźǀĭƁŹřżĭŵźƟƽřźŝæƩřƹŶſ ƮǀƴĩũŹŵƾƬƇřƶŰƠƇŹŵřŹŚƷƵŵřŵƮǀƳřƺţƾƯŹƺƐģƮǀƃŚŝƶŤƃřŵřŹƾƳřƹřźƟƩƹŶūźĭřçƩřƺŘſ ŚƸƷƹźĭŵřŶƘţźƔƳŻřƹƶƬƇŚƟƩƺƏźƔƳŻřŢƀǀģŚƷƵŵřŵƽřźŝŮǀŰƇƽŶƴŝƵŵŹƶƤƿźƏèƩřƺŘſ

XŚƷźǀƜŤƯ ƮƬĜƿŵ æ ƮƬĜƿŵơƺƟ ç žƴśƀǀƫ è žƴśƀǀƫơƺɵ é řźťĩŵ ê f i ƾƳřƹřźƟ. íëææçê±¹á{ä m fi f i ƾŞƀƳƾƳřƹřźƟŚƿƩŚưŤůř.. 6 ƾƘưŬţƾƳřƹřźƟ êå ƾƳřƹřźƟƩƹŶū ƾƘưŬţƾŞƀƳƾƳřƹřźƟ ΖγϝΎϤΘΣϥΎϤϫϪ ΖγΕϻΎΣϞ ϪΑΏϮϠτϣΕϻΎΣΩΪόΗϩΪϨϫΩϥΎθϧϲΒδϧϲϧϭήϓ ϖ Βσή ά ϟϥϊ λέω όϥπη Βδϧ ϧϭήϓϭϊηύα ϣωϯχίϟβϗεϻύσωϯϥπϣ όϥπη ϧϭήϓ ΪϨΘδϫβϧΎδϴϟήҨίΕϼϴμΤΗ έω ΎҨήϔϧ Ϫ ΩϮη ϣκψθϣβϧύδθϟ ήαϻύαϝϭϊο ΖγϩΪηΩέϭmissing Ϫ Ζγ ҨΎϫϩΩΩϪΑρϮΑήϣInvalid Percent ϥϯθγ ϝύμϣ έ Ϡ λϩωωϝϭϊ ΟϝΎΣΪηΎΑ ϣ ϧϭήϓϝϭϊοϧҩϫ ΖγήҨίΡήηϪΑϩέΩ ҨϥΎϨ έύ ΕϼϴμΤΗΖδϴϟ ΪϴϨ ΩΎΠҨ έϊϣϊ ΩΪόΗ ϠϴμΤΗ ƮƬĜƿŵ æ ƮƬĜƿŵơƺƟ ç žƴśƀǀƫ è žƴśƀǀƫơƺɵ é řźťĩŵ ê Ϣϴ Ϩ ϒҨήόΗέ ϧϭήϓϡύϧϫα ήθϐθϣϣҩέϯβπϣύϣζγ ϠϴμΤΗ έϊϣϥϭϣҩέωήθϐθϣ ҨΎϣΎΠϨҨέΩ ϢϴϨ ΩέϭϝϭΪΟέΩέΕΎϋϼσΎΗ ΎϫήϴϐΘϣΏΎΨΘϧ Name Type Width Decimal Label Value Missing column Align Measure 1 madrak Numeric 8 - - - - 10 left Scale frequency Numeric 8 - - - - 10 left Scale έή ΗέΎΒ ҨϡΪ ήϫϥϯ Ϊϧί ϣ ҨέϪϤϫϢϴϫΩϞϴ θη ϧϭήϓϝϭϊοmadrakήθϐθϣ ήαϥϻή ήθϐθϣϫαέϥίϭϣθϩ ϒҨήόΗέΩϥίϭήϴϐΘϣΪҨΎΑβ ϢҨέΩϢϠ ҨΩήϔϧ ΎϣϪ ΗέϮλέΩΖγϩΪη ΖγέΩ ϪΠϴΘϧ ϭ ϢҨήϴ ϣ madrak ήθϐθϣ αύγ ήα έ ϧϭήϓ ϝϭϊο ϟϭϣθϫω ϣ ΖΒδϧ ϧϭήϓ ΩϮη ϣϩωωϥύθϧ Data Weight Case Ϫ ϢϴϤϬϓ ϣϫτϔλϧθҩύ ΖγέΖϤγέΩWeight OnϪϧΎθϧΎΑϢϴϨ ίύαέϫτϔλϧҩϫ ϥύϣίήϫ ΪϧέΩϥίϭΎϫϩΩΩ åç

Max Min 50 0 6 ϢϴϨ ϣϩωύϔθγϝϯϣήϓϧҩίϧγήθϐθϣ ΪϨΑϩΩέ ήα 5 5 ϪϨҨΰ ί ΪϨΑϩΩέΩΎΠҨ ήαϊҩ ϣζγωϫα ϩίύαϝϯσϭϣθϩ ϣώύψθϧ έϩωέωϊόη Transform recode- into different variable ϢϴϨ ϣϒҩήόηage groupϡύϧϫαέϊҩϊοήθϐθϣ ϣϩωύϔθγζγϩϊη ΪϨΑϩΩέϪ Age groupϊҩϊοήθϐθϣί ϧϭήϓϝϭϊοϫβγύτϣ ήαϫθ ϧ ϩωύϔθ γage ϨόҨ ϟϭή ϴϐΘϣίϦϴ ϧύ ϴϣϞ Μϣ έύ ϣ Ύ ϫκχύ ηϫθϙαϫβγύτϣ ήα ϟϭϣθϩ ϢϴϨ ϦϴϴόΗϩΪϨҨΎϤϧϩΩέήϫ ήαή ϣωέϊϧ ϨόϣϩΪη ΪϨΑϩΩέήϴϐΘϣϦϴ ϧύθϣήҩίωή ϢϴϫϮΧ įŷƴŝƶŵź X f ƵŵŹƵŶƴƿŚưƳ İƳřƹřźƟ 0 6 3 7 33 30 fixi ΖγϡiϩΩέϩΪϨҨΎϤϧ x i ˬϦϴ ϧύθϣϝϯϣήϓϖβσϭ i ϢϴϨ ϣκψθϣvalue ΖϤδϗέΩέϥVariable viewζϥδϗέωage groupϊҩϊοήθϐθϣϒҩήόηύα 1 3 4 Name Gender Age Weight Agegroup Type Numeric Numeric Numeric Numeric Width 8 8 8 8 Decimal - - - - Label 1=male =female - - 1=0-6 =7-33 3=34-40 4=41-47 5=48-54 - grouping variable for age 7 Value ϝύμϣ ΏΎδ ΣέΪ ϣζθδ ϨΟή ϴϐΘϣ ή ΑϭϦϴ ϧύ ϴϣϥίϭϭϦ γήθϐθϣ ήαϣҩέωέϥίϭϭϧγˬζθδϩοήθϐθϣϫγ ΪϴϨ ΎϫήϴϐΘϣΏΎΨΘϧ Name Type Width Decimal Label Value Missing column Align Measure 1 Gender Numeric 8 - - 1=male - 10 left Nominal =female Age Numeric 8 - - - - 10 left Scale 3 Weight Numeric 8 - - - - 10 left Scale ϝϭϊ Ο ϟϭζγϩϊηέή ΗέΎΒ ҨϩΩΩήϫϪ ΩϮη ϣκψθϣ ϧϭήϓϝϭϊοϥϊηκψθϣίβ έύ ϫϩωωϊҩύαϟ θϣϧҩϊϓέ ήαϊηύα ίήθϥ ϥ ΎϫϩΩΩ ήμ ΪΣϪ ΖγΏϮΧ ϧϭήϓ ΩϮηήΘθϴΑ ϧϭήϓύηϣθϩ ΪϨΑϩΩέ įŷƴŝƶŵźʃƹřɓƹź - Missing - - - - column 10 10 10 10 Align left left left left Measure Nominal Scale Scale Scale

Ϫ Α ϧϭήϓϝϭϊ ΟΪ ηϊ ϫϯχήθϭα ϠΒϗϝϭΪΟίϪ ϢϴϨ ϣϣγέ ϧϭήϓϝϭϊοϊҩϊοήθϐθϣ ήα Ζ γϊα ήθϭα ϧϭήϓϝϭϊούηωή ήθ έΰαέϩωέϝϯσϭήθϥ έύϫϩωέϥϯθθϣωέω ΘδΑΎϣ ΎϫϩΩέ Ωέϭ grouping variable for age Valid Cumulative Frequency Percent Valid Percent Percent 0-6 1 10.0 10.0 10.0 7-33 5 50.0 50.0 60.0 34-40 0.0 0.0 80.0 41-47 1 10.0 10.0 90.0 48-54 1 10.0 10.0 100.0 Total 10 100.0 100.0 Transform - visual BanderŚƿvisual Binning įŷƴŝƶŵźƭƹŵɓƹź ΩϮη ϤϧϡΎΠϧNominalήϴϐΘϣ ήα ΪϨΑϩΩέϪ ΪϴηΎΑϪΘηΩΖϗΩ visual BinningΪҨΪΟϩήΠϨ έωϣθϧΰθϣcontinueϭϣθϩ ϣώύψθϧέweight ήθϐθϣϩϊηίύαϩήπϩ έω ϞΤϣϭρΎϘϧMake cutpointsϫϥ ΩϥΩίΎΑϝΎΣgweightϢϴϨ ϣωέϭέϊҩϊοήθϐθϣΐδ ήαϭϣγ ϢҨίΎγ ϣέζδ η ΩέΩΩϮΟϭΖδ ηρύϙϧϧθθόη ήαϩέϫγ Equal Width Intervals Ϫ ϢϴϫΪ ϴϣΖ γ Ύ ΠϨҨέΩϭΪηΎΑΎϣminέϝϭϪτϘϧ Ϊ όαϣθ Ϩ Ωέϭέ ΩΪ ϋ Θ γϯθ ή ΑΖ γή ΘϬΑ ϩωέϝϯ σ ϣϯ γϭϣθ Ϩ ϣωέϭ Ϫ ΖγΎϫϩΩέΩΪόΗ ΩΪ όηϊ ϴϫΪΑέϩΩέϝϮ σή Ϊδ ҨϮϧ ϣεωϯχϫ Ζγ ΖϤδϗέΩέϩΩέΩΪϋϦҨήΧϭΪϫΩ ϣεωϯχέϩωέ.ϊϫϊθϣϥύθϧεωϯχέ Last cutpoint location Equal Percentiles Based on Scanned Cases έωήθ ϴϣmax-minϪϠλΎϓίϩΩέήϫϪ ΪλέΩϭϩΩέΩΪόΗ ϢϴϫΪϴϣ Cutpoints at Mean and Selected Standard Deviation Based on Scanned Cases ΪϨ ϴϣϦϴϴόΗέΎϫϩίΎΑέΎϴόϣϑήΤϧϪγΎҨϭΩˬ ҨαΎγήΑ έϩωέn+1ωϊ όηζδ ηϫ τϙϧnωϊ όηωϯηϩωύϔθγϫ ϪϨҨΰ ϪγϦҨϡΪ ήϫίϝύσήϫϫαϫθ ϧ ΖηΩΪϫϮΧ ΪϧϮη ϣήϫύχϣϫύϫϩωέϩήπϩ ϥύϥϫέωmake LabelϪϤ ΩϥΩίΎΑ 8

(έύ ϴόϣϑή Τϧϭ) Ϧϴ ϧύ ϴϣήҨΩΎ ϘϣΎ Α ϨΤϨϣϦҨωΎϔΗέ ΩέΩρΎΒΗέ έύ ϴόϣϑή Τϧ ϭέϊ Ϙϣ ҨϝϮ ΣΎ ϫϩωωϣ ήηϥΰθϣ ΖγϦϴ ϧύθϣίύϫϩωω ΪϨ ή ϥΰθϣ ϥίϯ ϣζ ϧωεή Ϥϧ γέωϥύ ΤΘϣ ҨέΩϝΎ Μϣ ϥϯϩϋϫα ΖϤ γϫ ΑϪ ή ϫϭϊ ηύα ϣήθθ ϴΑ Ϧϴ ϧύ ϴϣϑή σΐϡϗ έεή ϤϧϦ ҨϪ Ωή ϓΩΪ όηϣҩϭή Αζϴ ϦϴҨΎ ΎҨϻΎΑΕήϤϧ Ҩ Ύ ΑϥϮΗ ϣζϟϯϭδαέέύθϓέϧҩωϯη ϣήθϥ ΪϧϪΘϓή.Ωή ϝϊϣϝύϣήϧϊҩίϯη ŶƿŶūźǀƜŤƯįƹŹźŝƍƹźƄƯšŚŞſŚŰƯƭŚŬƳřƹŶƿŶūźǀƜŤƯŵŚŬƿřƁƹŹ ϢҨΩή ϣϒҩήόηέ ΪҨΪΟήϴϐΘϣTransform recode- into different variable εϭέίϩωύϔθγύα ϢҨέϭΎϴΑΖγΪΑέ ΪҨΪΟήϴϐΘϣϪΒγΎΤϣεϭέΎΑϢϴϫϮΧ ϣϝύσ ϟϭ Transform Compute Numeric ΖϤδ ϗέωέϝϯ ϣήϓϭϣθ Ϩ ϣνέωέϊ ҨΪΟή ϴϐΘϣϡΎ ϧtarget VariabeleΖϤδ ϗέω ΏΎδΣϡή ΐδΣήΑ ή ҨΩϥϮΘγέΩέWeightήϴϐΘϣϢϴϫϮΧ ϣή ϼΜϣϢϴδҨϮϧ ϣexpression ϢϴϨ ΏΎΨΘϧέιΎΧ όαύηύҩweight 1000 ϢϴδΑϮϨΑέϝϮϣήϓϦҨ ϧϯη ϣϣθϩ ΩϮ η ϤϧήΟϝϮϣήϓϩΩΩήϴϴϐΗΎΑSPSS έωϫ ΖγϦҨExcelΎΑSPSSέΩ δҩϯϧϝϯϣήϓϕήϓϫθ ϧ ή ϴϴϐΗέϩΩΩϩΎ ή ϫexcelέω ϟϭωϯ ηή ΟϝϮ ϣήϓ ΩΪ ΠϣΖδҨΎΑ ϣϭωωή ϤϧίϭέϪΑϪΠϴΘϧϭ ΪϨ ϣήθθϐηΰθϧϫπθθϧϣθϫω Ϊ Σϭή ϴϴϐΗ ϻύμϣωϯηϡύπϧ λύχ ΎϫϩΩΩ ήαςϙϓϭ λύχςҩήηζτηϝϯϣήϓϧҩϣθϫϯψαή ϼΜ ϣϣθ Ϩ ϩωύϔθ γcomputeϩή ΠϨ ϦϴҨΎ ifϫ Ϥ ΩίΪҨΎΑΩϮηϡΎΠϧΎϬϤϧΎΧ ήαςϙϓϡή ϪΑϥίϭ Gender =1& Age>30 ΎҨGender =1 Ϫ ϢҨέΰ Αρήη ή ΑϭϢҨέά Α λύχ Ύ ϫϩωω ήαϣθϩ ϒҨήόΗselectϡΎϧϪΑήϴϐΘϣ ҨϢϴϧϮΗ ϣϫ ϨҨή ҨΩϩέ Ύϫήϔλ ήαύҩϣθϩ ΏΎδΣΎϬ Ҩ ήαέϝϯϣήϓϊόα ή ҨΩ ΎϫϩΩΩ 9

SPSS ŹŵƵŶƃřźūřšřŹƺŤſŵƕřƺƳřŢŞŧƁƹŹ ϣνέωύϣϥύϣήϓϭϩϊηίύαsyntax ϩήπϩ ˬΕέϮΘγΩί ϩήπϩ ήϫέω pasteϫϥ ΩίϩΩΎϔΘγΎΑ ϊϗϯϣζγήθϭαωή ήο ΩΪΠϣέϦϴϣήϓRunέϮΘγΩΎΑϥϮΗ ϣϩήπϩ ϦҨΩΪΠϣϥΩή ίύαύα ΘΣΩϮη ΩϮηΝέΩϥέΩΕέϮΘγΩΎΗΪηΎΑίΎΑϢϫsyntaxϪΤϔλspssϪΤϔλϥΩή ίύα řżŭưįśʒiūƹźųʊŵźƹōţſŷŝƹśʒƶŵřŵĩǀīơţ Data viewϫτϔ λέωϊ ҨΪΟή ϴϐΘϣComputeϩή ΠϨ ίϩωύϔθ γύαϫ ΖδϨҨ ϠΒϗϩέΎΑϩέϦҨϕήϓ ϥωή ή ΘϠϴϓωϮ ϧ ҨϦ ҨϭΩϮ η ϣϟλύσεήθθϐηoutputϭ ΟϭήΧέΩϩέϦҨΎΑ ϟϭωϯθθϣωύπҩ Ζγ ϝϭεϭέ Data Select Cases Ϫ ΑΪ ηύαϝύ όϓall Casesή Ϫ ΩϮ η ϣίύαϭήαϭέϩήπϩ if condition is ϥωή ϝύ όϓύ ΑϭΖ γή ΘϠϴϓϡΪ ϋ Ύ Ϩόϣ ϣρή η ϼΜ ϣϣθ Ϩ ϣνέωέήψϧωέϯϣρήηsatisfied ϡύ ΠϧΎ ΑϝΎ Σή Ϫ Ϊ Ϩ ϪΒ γύτϣέύϫωήϣςϙϓϣҩέά ϭϊ Ϩ ϴϣϪΒ γύτϣύϫωήϣ ήαςϙϓϣҩήθ ΑϦϴ ϧύθϣή ήθϡθϓ ϭϊθ ϣς ΧέΎ Ϭϧί Ύ ϫωέϯ έϣ ϫdata viewέω ή ΑϥΪ ηώύ ΨΘϧϥέΩϪ Ϊ Ϩ ϣωύ ΠҨ ϧϯθ γ ΪϫΩ ϣϥύθϧέεύβγύτϣ ΪϨ ϣώύψθϧ ϓΩΎμΗϪϧϮϤϧ ҨRandom of casesώύψθϧύα ϪΤϔ λϧθҩύ έωϭωϯ η ϣϡύπϧήθϡθϓokϥωίύαϭuse filter availableέωήθϡθϓωέϯϣήθϐθϣώύψθϧύα ΪηΪϫϮΧϩΩΩζҨΎϤϧfilter onΰθϧ ϡϭωεϭέ ΪόΑϢҨέΩ ϣήαall CasesϪϨҨΰ ϥωίϭdata Select CasesεϭέΎΑέ ϠΒϗ ΎϫήΘϠϴϓϝϭ Data Split Files ΖγήΘϠϴϓϡΪϋϝϭϪϨҨΰ ΟϭήΧέΩ Ϫδ ҨΎϘϣΖ ϟύσέωέϝϭϊ ΟϡϭΩϪ ϨҨΰ ΪϫΩ ϣϥύθϧ ΪηΪϫϮΧϩΩΩϥΎθϧϪϧΎ ΪΟ ϼϣΎ ϡϯγϫϩҩΰ ϥύθ ϧύ ϬϧίϭΎ ϫωήϣζϥδ ϗϭωϫ ΑέgenderήϴϐΘϣϪ ΩΩΪϫϮΧ 10

ΪϴϨ ίύαήҩίήθδϣίέdemoϟҩύϓ5ϝύμϣ My computer win(programming) - program files - spsseval Tutorial sample files- Demo ϭύ ϫϣϧύ ΧΪ ϣέωϥΰθϣϧθ ϧύθϣβ γϊҩέϭζγωϫαύϫήθϐθϣωϊόηˬύϫcaseωϊόηωέϯϣέωέϡίϻεύϋϼσ ή Α ϧϭήϓϝϭϊ ΟΪ ҨέϭΎϴΑΖγΪΑϦϴηΎϣωϮϧαΎγήΑέΪϣέΩ ϧϭήϓϝϭϊοϭϊθϩ ΏΎδΣϪϧΎ ΪΟέϥΎҨΎϗ ΪҨέϭΎϴΑΖγΪΑϩΪη ΪϨΑϩΩέΪϣέΩαΎγ ƪƿŚƟĨƿŻřšŚƗǈƏřŸųř File display data files informationϝϭεϭέ Analyze-Report Case SummariesϡϭΩεϭέ ϪϧΎ ΪΟϞ ηϫαύϭϧίϭύϫωήϣϊϣέωϥΰθϣϧθ ϧύθϣ ήαϒϟ Data Split Files gender- organize output by groups- ok Ϧϴ ϧύθϣ ϪΒγΎΤϣ ήα ϟ ϭ ϢϴϨ ϣ ΏΎδΣ inccat ΎҨ ΪϣέΩ ήθϐθϣ ήα ϧϭήϓ ϝϭϊο β γ okβ γϭϣθϧί ϣ ϴΗέMeanϪϨҨΰ statisticsζϥδϗέωincomeήθϐθϣΐδσήαέ ϧϭήϓ ΩϮη ϤϧϪΒγΎΤϣϩΪη ΪϨΑϩΩέήϴϐΘϣ ήαϧθ ϧύθϣ Gender = Female Statistics(a) Household income in thousands N Valid 3179 Missing 0 Mean 68.7798 Gender = Male Statistics(a) Household income in thousands N Valid 31 Missing 0 Mean 70.1608 a Gender = Female a Gender = Male Income category in thousands(a) Valid Cumulative Frequency Percent Valid Percent Percent Under $5 563 17.7 17.7 17.7 a Gender = Female $5 - $49 108 38.0 38.0 55.7 $50 - $74 57 18.0 18.0 73.7 $75+ 836 6.3 6.3 100.0 Total 3179 100.0 100.0 Income category in thousands(a) Valid a Gender = Male Cumulative Frequency Percent Valid Percent Percent Under $5 611 19.0 19.0 19.0 $5 - $49 1180 36.6 36.6 55.6 $50 - $74 548 17.0 17.0 7.6 $75+ 88 7.4 7.4 100.0 Total 31 100.0 100.0 11

ϦϴηΎϣωϮϧΐδΣήΑ ϧϭήϓϝϭϊοώ ϦҨ ςγϯη ΪϫΪϴϣ ϥύθϧ έ ϦϴηΎϣ ωϯϧ Ϫ carcat ήθϐθϣ β ϢϴϨ ήθϡθϓ έ ϦϴηΎϣ ωϯϧ ΪҨΎΑ ϻϭ Data Split Files carcat - organize output by groups- okέϯθγω ϢϴϨ ϣώύδσέinccat ΎҨϩΪη ΪϨΑϩΩέΪϣέΩήϴϐΘϣ ϧϭήϓέϯθγωϊόαϣθϩ ϣήθϡθϓ Income category in thousands(a) a Primary vehicle price category = Economy Valid Cumulative Frequency Percent Valid Percent Percent Under $5 1174 63.8 63.8 63.8 $5 - $49 667 36. 36. 100.0 Total 1841 100.0 100.0 Income category in thousands(a) a Primary vehicle price category = Standard Valid Cumulative Frequency Percent Valid Percent Percent $5 - $49 171 75.6 75.6 75.6 $50 - $74 554 4.4 4.4 100.0 Total 75 100.0 100.0 Income category in thousands(a) - a Primary vehicle price category = Luxury Valid Cumulative Frequency Percent Valid Percent Percent $50 - $74 566 4.8 4.8 4.8 $75+ 1718 75. 75. 100.0 Total 84 100.0 100.0 ƵŶƃźŤƬǀƟŚƿsplitŢƫŚůŹŵcarcatźǀƜŤƯŚƿƲǀƃŚƯƕƺƳŽŚſřźŝinccat źǀɯťưśƿƶŷƃįŷƴŝƶŵźŷưōźŵiƴřƹřźɵƶşſśűư Income category in thousands Primary vehicle Cumulative price category Frequency Percent Valid Percent Percent Economy Valid Under $5 1174 63.8 63.8 63.8 Standard Luxury Valid Valid $5 - $49 667 36. 36. 100.0 Total 1841 100.0 100.0 $5 - $49 171 75.6 75.6 75.6 $50 - $74 554 4.4 4.4 100.0 Total 75 100.0 100.0 $50 - $74 566 4.8 4.8 4.8 $75+ 1718 75. 75. 100.0 Total 84 100.0 100.0 ŪƿŚŤƳ ŶƳŹřŵŹǇŵçêåååźƿŻŶƯōŹŵŶƳŹřŵEconomyƩŶƯƲǀƃŚƯƕƺƳƶĩëèíæ ŶƳŹřŵŹǇŵÔÒÍÍÍįǇŚŝŶƯōŹŵŶƴƴĩİƯƵŵŚƠŤſřLuxury ƲǀƃŚƯƕƺƳŻřƶĩÔÒÏÏ 1

ƵŶƃźŤƬǀƟŚƿsplitŢƫŚůŹŵmartial źǀɯťưśƿƪʒśţƕƺƴžśſřźŝinccat źǀɯťưśƿƶŷƃįŷƴŝƶŵźŷưōźŵʋǀįƴśǀưƶşſśűư Income category in thousands Cumulative Marital status Frequency Percent Valid Percent Percent Unmarried Valid Under $5 578 17.9 17.9 17.9 Married Valid $5 - $49 18 38.1 38.1 56.0 $50 - $74 55 17.1 17.1 73.1 $75+ 866 6.9 6.9 100.0 Total 34 100.0 100.0 Under $5 596 18.8 18.8 18.8 $5 - $49 1160 36.5 36.5 55.3 $50 - $74 568 17.9 17.9 73. $75+ 85 6.8 6.8 100.0 Total 3176 100.0 100.0 13 ŪƿŚŤƳ ŶƳŹřŵŹǇŵçêåååéîåååƲǀŝŶƯōŹŵŶƴŤƀƷŵźŬƯƶĩèíææ ŶƳŹřŵŹǇŵìêåååįǇŚŝƲǀŝŶƯōŹŵŶƴŤƀƷƪƷŚŤƯƶĩçëíÏ

Transform-Visual binning cutpoint 17.5 agegroupϊҩϊοήθϐθϣωύπҩϭϧγήθϐθϣ ΪϨΑϩΩέ ƵŶƃźŤƬǀƟŚƿsplitŢƫŚůŹŵagegroupźǀƜŤƯŚƿİƴſƵŵŹŽŚſřźŝinccat źǀɯťưśƿƶŷƃįŷƴŝƶŵźŷưōźŵʋǀįƴśǀưƶşſśűư Income category in thousands Age in years (Binned) Frequency Percent Valid Percent Cumulative Percent 19-8 Valid Under $5 410 5.3 5.3 5.3 $5 - $49 319 40.7 40.7 93.0 9-38 Valid 39-48 Valid 49-58 Valid 59-68 Valid 69+ Valid $50 - $74 41 5. 5. 98. $75+ 14 1.8 1.8 100.0 Total 784 100.0 100.0 Under $5 331 19.1 19.1 19.1 $5 - $49 99 53.5 53.5 7.6 $50 - $74 93 16.9 16.9 89.5 $75+ 18 10.5 10.5 100.0 Total 1735 100.0 100.0 Under $5 14 7.0 7.0 7.0 $5 - $49 641 36.0 36.0 43.0 $50 - $74 453 5.4 5.4 68.4 $75+ 563 31.6 31.6 100.0 Total 1781 100.0 100.0 Under $5 73 5.6 5.6 5.6 $5 - $49 341 6.0 6.0 31.6 $50 - $74 47 18.8 18.8 50.4 $75+ 650 49.6 49.6 100.0 Total 1311 100.0 100.0 Under $5 169 6.0 6.0 6.0 $5 - $49 135 0.8 0.8 46.8 $50 - $74 75 11.5 11.5 58.3 $75+ 71 41.7 41.7 100.0 Total 650 100.0 100.0 Under $5 67 48. 48. 48. $5 - $49 3 16.5 16.5 64.7 $50 - $74 11 7.9 7.9 7.7 $75+ 38 7.3 7.3 100.0 Total 139 100.0 100.0 ŪƿŚŤƳ ŹǇŵìêåååįǇŚŝŶƯōŹŵêåĨƿŵżƳéíêíİƴſƵŵŹŹŵƶĪǀƫŚůŹŵŶƳŹřŵŶƯōŹŵŹǇŵçêåååźƿŻêçèæîçíİƴſƵŵŹŹŵæ.ŶƳŹřŵ 14

íëæçç±¹âä m 15 ƶƴƺưƴƭśƀƣř Ϫ ΖγϪόϣΎΟ ҨίήμϨϋnϞϣΎηϩΩΎγ ϓΩΎμΗ ήθ ϪϧϮϤϧ ΩέΩήμϨϋήϫΏΎΨΘϧ ήα ϭύδϣβϧύηύϭθ Ҩϭ ϓΩΎμΗζҨΎϣίΩέΩ ҨΎΗnϪϧϮϤϧήϫΏΎΨΘϧ ήα ϭύδϣβϧύη ϝϭϊοίϩωύϔθγύαϣ ϪϨҨΰϫΎΑ έά ϩέύϥηϟαύϓύҩϩϊη έά ϩέύϥηϫόϣύοϩωύϔθγωέϯϣ ϓΩΎμΗΩΪϋ Ϣ ϪϨҨΰϫϭΖϋήγϪΑ γήθγωζθϡαύϗ έω ΩϮη ϣϟϣύηέϫόϣύοήμϩϋϧθϣiήϫϫ Ζγ ϪϧϮϤϧ ϴΗΎϤΘδϴγ ϓΩΎμΗ ήθ ϪϧϮϤϧ ΐγΎϨϣ ϠϴΧήҨά ΐϴΗήΗ ϊϣϯο ήα Ζγ ήϔλ ήθϗ ϭ ϡϯϡόϣ Ϫ ϪόϣΎΟ ί ήμϩϋ ήϫ ϞϣΎη ϨόҨ ί ΩΪόΗ Ϫ ϢϴϧΪΑ ϢϴϫϮΨϴϣ ϼΜϣΩέΩ ΕϭΎϔΗ ϼϣΎ ή ҨΩ ΎΑ ϪϧϮϤϧ ήϫ ήθ ϪϧϮϤϧ ϦҨ έωϭ Ζγ Ύϫ Ϧ ϭ ϝϭ ήθ ϪϧϮϤϧ έω ΪϧϮη ϣ ΏήΧ ΘϓΎδϣ ί ΪόΑ ΎΟέ ήα ήϓύδϣ έύτϗ Ύϫ Ϧ ϭ ϼΜϣΩΪϋΕϭΎϔΗΎΑ ή ҨΩ ΎϫϦ ϭϡϭω ήθ ϪϣϮϤϧέΩϭϢϴϨ ϣώύψθϧέϭ ϭ ϭ N ϩϊη κψθϣ ήθ ϪϧϮϤϧ ϪϠλΎϓ k ΪόΑ ϩωή έά ϩέύϥη N ΎΗ ί έ ϪόϣΎΟ εϭέ ϦҨ έω n ϦϴϟϭϪΑkϥΩϭΰϓΎΑϭΖγΎϣϪϧϮϤϧΪΣϭϦϴϟϭϪ ϩϊηώύψθϧkύη ίϑωύμηϫαωϊϋ Ҩβ γ ΪҨ ϣζγϊαϣϫύϫϊσϭήҩύγˬϊσϭ ҨΏΎΨΘϧϭΰҨΎϤΘϣ ΎϫϪϘΒσϪΑϱέΎϣϪόϣΎΟ ҨϢϴδϘΗϩΪη ΪϨΑϪϘΒσ ϓΩΎμΗ ήθ ϪϧϮϤϧ ϪϘΒσήϫί ϓΩΎμΗϪϧϮϤϧ ΎҨΰϣ ϪϘΒσήϫέΩ ϓΎ ήλύϩϋωϯοϭίϥύϩθϥσ ϪόϣΎΟ ΎϫήΘϣέΎ ίήθϭα ΎϫϦϴϤΨΗϥΩέϭΖγΪΑ ϪΑέΎϬϧΎδϧή ϟϭϊҩ ϣζγϊα ϪΠϴΘϧ ҨϢҨήϴ ΑέϪόϣΎΟ Ҩ ΎϬϧΎδϧΪϗςγϮΘϣή ϼΜϣ ϪΠϴΘϧϢϴϨ ϪΒγΎΤϣϩΪη ΪϨΑϪϘΒσ ήθ ϪϧϮϤϧέΩέΪϗςγϮΘϣϭϢϴϨ ϢϴδϘΗΩήϣϭϥίϪϘΒσϭΩ ΪҨ ϣζγϊα ήθϭα ϦҨΪηΎΑϪϘΒσήϫϞΧΩέΩήλΎϨϋϑϼΘΧίήΘθϴΑΕΎϘΒσϦϴΑήλΎϨϋϑϼΘΧϪ ΗέϮλέΩ Ϡ Ϟλ ΪϫΩ ϣέ ήθϙθϗωξҩύθϧϩωύγ ϓΩΎμΗ ήθ ϪϧϮϤϧεϭέϪΑΖΒδϧεϭέ ϞϴϣϝΎϣήϧΖϤγϪΑϥϊҨίϮΗΩϮη έΰαϫϧϯϥϧϩίϊϧή ϓΩΎμΗ ήθ ϪϧϮϤϧήϫέΩ ΰ ήϣϊσϫθπϗ ΰ ήϣϊσϫθπϗ Ωή ΪϫϮΧ ϥωϯαϧ ϤϫρήηϪ ΖγϪόϣΎΟήλΎϨϋί ҨΎϫϪηϮΧΏΎΨΘϧ ϪηϮΧ ϓΩΎμΗ ήθ ϪϧϮϤϧ ΩϮηΖҨΎϋέΪҨΎΑϥέΩ ΐϠϏ έω Ύϫ ϪηϮΧ ϞϴϜθΗ ϱύϩβϣ ϭ ΩϮη ϲϣ ϩϊθϣύϧ ϪηϮΧ ΎΣϼτλ ϪϛϲϳΎϫ ΖϤδϗ ϪΑ έ ϪόϣΎΟ ˬΖγ ϱωύα Ύϳ ϙϯϡα ϭ ήϭη ˬϥΎΘδϫΩ ˬϥΎΘγήϬη ˬϥΎΘγ ϞϴΒϗί ϲϳύθϓήϐο ϱύϫ ϱϊϩα ϢϴδϘΗ ˬΕΎϗϭ ϥύθα ϪΑ. ΪϨηΎΑ ϲϧύηϯ Ϣϫ ΪϗΎϓ ϭ ΪϨϫΩ ζηϯ έϫόϣύο Ϟϛ ΪϳΎΑ ΎϫϪηϮΧ. ΪϨϨϛ ϲϣ ϱϊϩα ϢϴδϘΗ ΪηΎΑ ϪΘηΩ ϖϡόη ΎϫϪηϮΧϦϳ ί ϲϝϳ ςϙϓ ϭ ϲϝϳ ϪΑ ΪϳΎΑ ϪόϣΎΟ ήλύϩϋ ί ϡϊϛ ήϫ ή ϳΩ ˬϱ ϪϠΣήϣ ΪϨ Ύϳ Ϛϳ ϱ ϪηϮΧ ϱήθ ϪϧϮϤϧˬ ϩϊθ ϴ ϱήθ ϪϧϮϤϧ ΡήσSPSS ϱέΰϓ ϡήϧ ϱ ϪΘδΑ.ΪϨϛ ϲϣ Ϣϫήϓ Complex Samples ΎΑέˬϱΪϨΑ ϪϘΒσ ϱήθ ϪϧϮϤϧ ŵźřŵiįťƀŝiƭưřƺɨƶģƶŝƶīƴƿřƹŷǀʒŵůǀƌƺţřźƶƴƺưƴƶżřŷƴřʋťɵśƿįśʒɓƹźʃřƺřſ

Crosstabs İƤƟřƺţƩƹřŶū έωέή Ҩί ϘϓϮ ΗϝϭΪ ΟΩϭέ ϣέύ Α Ϥ γ Ύ ϫή ϴϐΘϣϦϴΑρΎΒΗέϩΩΩϥΎθϧ ήα ϘϓϮΗϝϭΪΟ ΪϴϨ ΩέϭέΰϓϡήϧϪΤϔλ ŹŚĪǀŝ ƱŻ ŵźư ϢϴϨ ϣώύψθϧέϟϐηϭζθδϩοήθϐθϣϭω ϢϴϨ ϣϒҩήόηϥωωϥίϭ ήαΰθϧέωϊόηήθϐθϣϫ ΏΎΨΘϧέΎΒ ҨςϘϓέΖϟΎΣήϫΎϫϩΩΩΩϭέϭϪΤϔλέΩΪόΑ Male jobless ϢϴϨ ϣ ƪƛŚƃ Male employee Female jobless Female employee 3 Name Gender job frequency Type Numeric Numeric Numeric Width 8 8 Decimal 0 0 0 Label - - Value 1=male =female 1=jobless =emploee Missing - - - column 10 10 10 Align left left left Measure Nominal Nominal ΖϬΟ ϢϴϫΩ ϣ ϥίϭ Ύϫ ϩωω ϪΑData Weightcases frequency ϮϨϣ ί ϩωύϔθγ ΎΑ β γ ϮϨϣί ϘϓϮΗϝϭΪΟϞϴ θη Analyze Descriptive Statistics- Crosstabs έω ήҩί ϝϭϊο Ϫ ϢҨέά Α Ϥγ ΎҨ Nominal ήθϐθϣ ϭω ΪҨΎΑ ϩϊη ίύα ϩήπϩ ϥϯθγ ϭ ήτγ ΖϤδϗ έω ΩϮη ϣήϫύχoutput job * gender Crosstabulation Count job gender Total male female male jobless 5 15 0 emploee 0 10 30 Total 5 5 50 ήθϐθϣϭωϝϼϙθγϫθοήϓϫ ϭω ΧϡΎϧϪΑ ϧϯϣίίϥϯθθϣcrosstabsϩήπϩ έωstatisticsϫϥ ΩϥΩίΎΑ ΩϮϤϧϩΩΎϔΘγˬΪϨ ϴϣϥϮϣίέ Ϥγ Chi-Square Tests Scale Value df Asymp. Sig. (-sided) Pearson Chi-Square 8.333(b) 1.004 Continuity Correction(a) 6.750 1.009 Likelihood Ratio 8.630 1.003 Exact Sig. (-sided) Exact Sig. (1-sided) Fisher's Exact Test.009.004 Linear-by-Linear Association 8.167 1.004 N of Valid Cases 50 a Computed only for a x table b 0 cells (.0%) have expected count less than 5. The minimum expected count is 10.00. 16

ϥύθϧήθϐθϣϭωϝϼϙθγ ήαέ έω ϨόϣτγϪ ϢϴΘδϫAsymp. Sig. (-sided) ϥϯθγϝύβϧωύϣ ΪηΎΑ ϣήθϐθϣϭω ΘδΑϭ ϨόϣϪΑϪ ΖγϢ έύθδαύπϩҩέωϊϫω ϣ ΩϮη ϣώύδσϝϯϣήϓϧҩςγϯηϊηύα ϣ ϤγήϴϐΘϣϭΩϝϼϘΘγϥϮϣίϪ ϭω ΧϥϮϣί ϩϊηϩϊϫύθϣ O i E i έύψθϧωέϯϣ ( Oi Ei ) E ( Oi Ei ) E i i ΪϧϪΘδΑϭ ΪϧϞϘΘδϣ ƶƴśƿŵźřŵŵƺūƹiəśşţźřįżśŝśśşſřƕƺƴƹţǀƀƴūʋǀŝŷǀƴĩƣǀƥűţʃřƺřſ ΟϭήΧέΩϥϮΘγϭήτγήϴϴϐΗ ϩωή ϴϠ ϥ ϭέέύαϭωζγ ϓΎ ϢϴϨ νϯϋέϥϯθγϭήτγ ΎΟϢϴϫϮΨΑή ΟϭήΧϝϭΪΟέΩ Ωή νϯϋέϥϯθγϭήτγ ΎΟpivoting trayϩήπϩ ϭformatting toolbarέΰαέϯϧίϩωύϔθγύαϭ Ϊϴθ ΰϴϧsplit filesίϩωύϔθγϭ ϠΒϗ ΎϫεϭέΎΑϥϮΘϴϣέϝϭΪΟϦϴϤϫ LayerήϴϐΘϣϪҨϻ ҨΩΎΠҨ ΩϮϤϧ ϩωύϔθγ ϘϓϮΗ ϝϭϊο έω ϣϯγ ήθϐθϣ ί ϥϯθθϣ ϪϫέϪγϝϭΪΟ ҨΎΗϢϴϨ ϪϓΎοέήϴϐΘϣϪҨϻ Ҩ ϨόҨ έ ϡϯγ ήθϐθϣ ΕήϴΛΎΗ Θϗϭ Ϫ ΪϫΩ ϣ ϥύθϧ ϭ ϢҨίΎδΑ ϥϯθγϭήτγ ΎϫήϴϐΘϣϦϴΑϪτΑέϪϧϮ ϢϴϨ ϣϝήθϩ ΪΑΎҨ ϣήθθϐη ϭempcat ϠϐηϖΑϮγϥΰϴϣϭΪϴϨ ίύαέdemoϟҩύϓ ϪΑέ genderζθδϩοήθϐθϣϭϊҩέϭζγϊαέinccat ΪϣέΩ ΪϴϨ ΩέϭϡϮγήϴϐΘϣϥϮϨϋ έήτγϊλέωϭϩωίέcellϫϥ Ωcrosstabs ϩήπϩ έωή ΪϫΩ ϣϥύθϧΰθϧϊλέωύαέωϊϋϣθϩ ϝύόϓ Years with current employer * Income category in thousands * Gender Crosstabulation Count Income category in thousands Gender Under $5 $5 - $49 $50 - $74 $75+ Under $5 Female Years with current Less than 5 369 557 130 61 1117 employer 5 to 15 148 553 77 11 1189 More than 15 46 98 165 564 873 Male Total 563 108 57 836 3179 Years with current employer 17 Total Less than 5 409 516 107 67 1099 5 to 15 131 56 74 08 1175 More than 15 71 10 167 607 947 Total 611 1180 548 88 31 ΖγέϻΩ ϻύαϊϣέωϭϝύγ ίήθθθα ϠϐηϪϘΑΎγϪΑρϮΑήϣΩΪόΗϦҨήΗϻΎΑΎϫΩήϣϭΎϬϧίϩϭή έωϫπθθϧ ΖγέϻΩ ϻύαϊϣέωϭϝύγ ίήθϥ ϠϐηϪϘΑΎγϪΑρϮΑήϣΩΪόΗϦҨήΗϦϴҨΎ ΎϫΩήϣϭΎϬϧίϩϭή έω

ϨόҨ Ζγ.000 ˬ ϥϯθγ ΩΪϋ Ϫ ϢҨϮη ϣ ϪΟϮΘϣ ϭω Χ ϥϯϣί ΎΑ Asymp. Sig. (-sided) ΪϧϪΘδΑϭϭΪϨҨϭΩέΪϧΩϮΟϭΪϣέΩϭ ϠϐηϖΑϮγήϴϐΘϣϭΩϦϴΑ ϟϼϙθγ Chi-Square Tests Pearson Chi-Square Likelihood Ratio 57.590 Linear-by-Linear Association N of Valid Cases Value df Asymp. Sig. (-sided) 506.010(a) 6.000 6.000 01.691 1.000 6400 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 318.50. ϟϭϊϫωϥύθϧέεϼθμτηϥΰθϣϭζθδϩοϧθαρύβηέϫ ΪϴϨ Ϣγέ ϟϭϊοdemoϟҩύϓέωϧҩήϥη έωύϭϧϊϣέωςγϯθϣϥΰθϣωϊόη ΎΟϪΑ ΪϨ ΝέΩϝϭΪΟ ϘϓϮΗ ϝϭϊο ί ϥϯη Ϥϧ ΎΠϨҨ έω ηέύϔγ ϝϭϊο ΪҨΎΑ Ϫ ϠΑ ΩϮϤϧ ϩωύϔθγ Ωή ΩΎΠҨ Analyze Tables Custom Table ήτγ έω ϭ ΖϴδϨΟ gender ϥϯθγ έω ΪϣέΩ ήθϐθϣ ϭ edu ΎҨ ΕϼϴμΤΗ τγ έω ΪηΎΑ scale ΪҨΎΑ ΎϤΘΣ Ϫ income ϢҨέά ϣcountζϥδϗ ϥϯθγ ϭ ήτγ ήθϐθϣ ϢϬϣ έύθδα ϪΘ ϧ ή ΪηΎΑϩΪη ΪϨΑϩΩέήϴϐΘϣΪҨΎΑ ΎϤΘΣ nominal ϪΑ ήϧ Ζγέ ϴϠ ΎΑ ΩϮΒϧ ϪΒγΎΤϣ ΖϬΟ ήθϐθϣ ϭ ϢϴϨ ϣ ϞҨΪΒΗ ΪηΎΑscale ΪҨΎΑ ΎϤΘΣ Gender Female Household income in thousands Male Household income in thousands Mean Mean Level of education Did not complete high school 59.0 60.49 High school degree 65.1 67.7 Some college 71.63 68.60 College degree 76.7 81.0 Post-undergraduate degree 84.81 89.6 έωsummary statisticsϫϥ ΩίϩΩΎϔΘγΎΑέ ή ҨΩ έύϣ ΎϫκΧΎηϦϴ ϧύθϣ ΎΟϪΑϥϮΗ ϣ ΩϮϤϧϪΒγΎΤϣϩήΠϨ ϥύϥϫ 18

carcat ΎҨ ϦϴηΎϣ ωϯϧ ϭ gender ΖϴδϨΟ ϦϴΑ ρύβηέ Ϫ ΪϴϨ Ϣγέ ϟϭϊο Demo ϞҨΎϓ έω ϦҨήϤΗ ϝϭϊοέωύϭϧϊϣέωςγϯθϣϥΰθϣωϊόη ΎΟϪΑ ϟϭϊϫωϥύθϧέprimary vehicle price category ΪϨ ΝέΩ Analyze Tables Custom Table scaleϊҩύα ΎϤΘΣϪ incomeϊϣέωήθϐθϣϭcarcatύҩϧθηύϣωϯϧήτγέωϭζθδϩοgenderϥϯθγέω ˬϦϴ ϧύθϣ Ύϫ κχύη summary statistics ϪϤ Ω ϥωί ΎΑ ϭ ϢҨέά ϣ count ΖϤδϗ έω ΪηΎΑ ϢϴϨ ϣώύψθϧϣϫέϊϣϭϣϥθϩθϣˬϣϥҩΰ Ύϣ Primary vehicle price category Female Household income in thousands Gender Male Household income in thousands Mean Maximum Minimum Mode Mean Maximum Minimum Mode Economy.17 31.00 9.00 5.00 1.6 31.00 9.00 3.00 Standard 4.45 61.00 9.00 34.00 4.67 61.00 9.00 31.00 Luxury 13.60 1070.00 59.00 65.00 136.65 1116.00 58.00 6.00 ŶƯōŹŵƎſƺŤƯ ϦҨήΗϻΎΑΎΑϥΎҨΎϗ ϟϭϊϧήχ ϣέβ ϮϟϦϴηΎϣϦҨήΘϬΑέϻΩέΰϫ ΪϣέΩϦҨήΗϻΎΑΎΑΎϬϤϧΎΧϪΠϴΘϧ ΪϧήΧ ϣβ ϮϟϦϴηΎϣέϻΩέΰϫ ΪϣέΩ ΖγϥΎҨΎϗίήΘϤ ΪϧήΧ ϣβ ϮϟϦϴηΎϣϪ ҨΎϫϢϧΎΧΪϣέΩϦϴ ϧύθϣ ŹřŵƺưƳƕřƺƳř Mean Household income in thousands ƱŻ 80.00 60.00 40.00 0.00 0.00 ŵźư 68.78 Female ŹǇŵ Gender ƎſƺŤƯ ŶƯōŹŵ 70.16 Male ƎſƺŤƯ ƶƴƿżʒ NominalϒϠΘΨϣ ΎϬϫϭή ϦϴΑέΩscaleήϴϐΘϣ ҨϪδҨΎϘϣ ϢϫΎΑήΘθϴΑΎҨscaleήϴϐΘϣϭΩϪҨΎϘϣ ΎϫcaseϡΎϤΗϦϴΑέΩήϴϐΘϣ ҨϪδҨΎϘϣ ΪϴϨ ΖγέΩέΎϫέΩϮϤϧϦҨϦҨήϤΗ ϝϭωϯϧέωϯϥϧζχύγ Graph Chart builder Ύϫx έϯτϣέωgenderήθϐθϣ Ύϫy έϯτϣέωincomeήθϐθϣ Clustering variable on ϪϨҨΰ Groups/point idϫϥ ΩέΩ ΪϴϨ ΝέΎΧϥΩϮΑϝΎόϓίέX ΎҨ Graph Legacy dialogs bar variable.. mean Household income in thousands / category Axis. gender 19

ϡϭωωϯϧϝϭϊοζχύγ Data split files - gender Analyze Descriptive statistics Descriptive - Household income in thousands, Price of primary vehicle Household income in thousands Price of primary vehicle Mean Mean Gender Female 68.78 9.95 Male 70.16 30.31 ϡϭωέωϯϥϧζχύγ Graph Legacy dialogs bar simple summaries of seprate variables - variable.. mean Household income in thousands / Price of primary vehicle- ok 69.47 60.00 Mean 40.00 30.13 0.00 0.00 Household income in thousands Price of primary vehicle 0

íëæçæë±¹zæqä m ϞΒϗϪδϠΟΕϻϮΌγϪΑΦγΎ ҨέΩέΪϨΘδϫ ϪϨҨΰ ΪϨ ϭϊϧέωϥύδ ҨΦγΎ Ϫ ҨΎϫήϴϐΘϣDemoϞҨΎϓέΩϥϮΘϴϣϪϧϮ ˮΩή έϭϊϥοϝϭϊο Analyze Tables Custom Tables Ζγέ ϴϠ ΎΑΪΘΑέΪϧέΩήϴΧΎҨϪϠΑΏϮΟςϘϓϪ ϥύδ ҨΦγΎ ΎΑ ΎϫήϴϐΘϣϪϴϠ β γϝϭεϭέ ΖϤγ ί β γ stacking ΖϟΎΣ ϪΑ ϩωωέήϗ ϝϭϊο ήτγ ΖϤδϗ έωϭ ϩωή ϞҨΪΒΗ category ωϯϧ ϪΑ ϢϴϨ ϣώύψθϧέrow labels in columns ϪϨҨΰ category positionζϥδϗϩήπϩ ϦϴҨΎ Ζγέ No Yes Count Count Wireless service 3853 547 Multiple lines 3709 691 Voice mail 3645 755 Paging service 4819 1581 Internet 4509 1636 Analyze Tables Tables of frequency z x x d x z x x d n x n nd Nz n ( N 1) d z nd nd z. z z 1 ϡϭωεϭέ ΪϴϧΰΑέokϭΪҨήΒΑϩήΠϨ έωέύϫήθϐθϣϫϥϫ ˮϢϴϨ ϪΒγΎΤϣϪϧϮ έϫϧϯϥϧϩίϊϧ Ϥ ήθϐθϣ ήα±ϒϟ ΩϭΪΤϣΎϧϪόϣΎΟέΩ n d ΖγκΨθϣϪόϣΎΟήλΎϨϋΩΪόΗNϪ ΩϭΪΤϣϪόϣΎΟέΩ z 1.64 ˬ 0.05 ήα ϔϴ ήθϐθϣώ ΎΟϪΑ ΖγΖϔλ ϥϧθηϊϧϝύϥθσq=1-pϭήψϧωέϯϣζϔλϧθηωϝύϥθσpϫ ϢϴϫΩ ϣέήϗέpqˬ Nz pq N d z pq n ( 1)

ƉźƟƱƺƯŻō κχύη ϥϯϩϋ ϪΑ ϪϧϮϤϧ κχύη έϊϙϣ ΏΎΨΘϧ ϭ ϪϧϮϤϧ ΎΑ ΖϴόϤΟ κχύη ϪδϳΎϘϣ ϱήα νήϓ ϥϯϣί ΩϭέϲϣέΎϛϪΑΖϴόϤΟ ϢϳϮηϞϤΤΘϣϢϴϧϮΗϲϣϪϛΖγϱΎτΧϥΰϴϣϭϪϧϮϤϧϭΖϴόϤΟϩίΪϧϪΑϪΟϮΗΎΑΏΎΨΘϧϦϳΖϗΩ ϊϳίϯηϫαζθόϥοϫ ήϫωϯαϊϫϯχήθθθαϥϯϣίζϗωϊηύαήθθθαζθόϥοϫαζβδϧϫϧϯϥϧϩίϊϧϫ ήϫ ΩϮΑΪϨϫϮΧήΗϥϮΗή Ύϫ ϥϯϣίϊηύαήθϝϳωΰϧϝύϣήϧ ŚƷƲǀĮƳŚǀƯƶƀƿŚƤƯįřźŝƱƺƯŻōśŚŴŤƳř Á Ä Â¼ Ët ½Â»M one sample T test ÃÁ³ Ë {Y e ÃÁ³ Á{YÌ] Ã{Z ZËYÁÌ Z M oneway Anova Ì YÁµZ Á ¾» Ë µy À Á½Y Â f»ézåä Â¼ t½â»m Independent-Sample t test ÉYÄ Â¼ Á{ÁÉf»YZaZ ÊÀfËÁ¾» f» f» Äf]YÁ Äf]YÁ Ã{Z ZËYÁÌ Z M oneway Anova Ì YÁµZ Á ¾» Ë µy À Á½Y Â ƲǀĮƳŚǀƯƶŝƍƺŝźƯįŚƷƱƺƯŻō ϪόϣΎΟ ϪΑ έ ϪϧϮϤϧ ΖϴλϮμΧϢϴϫϮΧ ϣ ϭ ϢҨ ϩωή ΏΎΨΘϧ ϪϧϮϤϧ Ҩ ϭ ϢҨέΪϧ έ ϪόϣΎΟ Ϫ ϧύϣί ϦϴΑ ϑϼθχ ϥϯθθϣ ΕΎϴοήϓ ϥϯϣί ΖϴϠΑΎϗ ί ϩωύϔθγ ΎΑ ϭ ϢϴϫΩ ϣ ϡύπϧ ϥϯϣί άϟ ϢϴϫΪΑ ΖΒδϧ ΐ ΗήϣϢϴϨ ΩέέϥΎϣϭΪηΎΑΖγέΩ έύϣνήϓή ˬνήϓϥϮϣίϡΎΠϧέΩΪϴΠϨγέΎϫϦϴ ϧύθϣ ϢҨϮη ϣϡϭωωϯϧ ΎτΧΐ ΗήϣΪηΎΑΖγέΩΎϧϢϴΘϓήҨά Ϫ έ έύϣνήϓή ϭϝϭωϯϧ ΎτΧ ΎτΧΖγήΘϤ ϡϭωωϯϧ ΎτΧϭΩϮΑΪϨϫϮΧήΗϥϮΗή ΎϫϥϮϣίΩϭήΑζϴ ϝύϣήϧϑήσϫαϊҩίϯηϫ ήϫ ΪηΎΑΖγέΩϪ ϨҨρήηϪΑH1ΩέϝΎϤΘΣ ϨόҨϡϭΩωϮϧ ϡϯϡόϣβϧύҩέϭϭ ϝϯϭπϣϧθ ϧύθϣύαϝύϣήϧϊҩίϯηί ҨΎΗnϪϧϮϤϧ Ҩ X1, X,..., X n ϢϴϨ νήϓ ΖγϪόϣΎΟϦϴ ϧύθϣωέϯϣέωήҩίνήϓϥϯϣίϑϊϫϊϩηύα ΩέΩέήϗΖγϪϓήσ Ҩΐ ήϣνήϓ ҨϪ H 1 νήϓϟαύϙϣέωϩωύγνήϓ Ҩ νήϓ H 0 : 0 H1: 0 H 0

έϯσϫαϭϊϩ ϤϧΕϭΎϔΗή ҨΩϪϧϮϤϧϪΑϪϧϮϤϧ ҨίϥϮϣίϪΠϴΘϧϭΖγήΘθϴΑϥϮΗϝΎϣήϧϊҨίϮΗέΩ ΐϴΗήΗ ϪΑ Ϫ ΩϮη ϣ ϩωύϔθγ ήθϣέύ Ύϫ ϥϯϣί ί ϥωϯα ϝύϣήϧ νήϓ ΎΑ έΰα ϊϣϯο έω Ϡ ΪϧέΩϪϴ ΗΕΎϋϼσϦΘϓή έήϗ ΎϬϧϮϣί ΪϨϨ ϣ ϩωύϔθγ ήθϣέύ Ύϧ Ύϫ ϥϯϣί ί Ϯ ϭ ϝύϣήϧ ήθϗ ϊҩίϯη ΎΑ Ύϫ ΖϴόϤΟ έω ΐΟϮϣϥΰϴϣϥΎϤϫϪΑϪ ή ҨΩΩή ҨϭέΪϨϨ Ϥϧ λύχνήϓϥβϧύҩέϭϭϫόϣύο ΎϬόҨίϮΗϩέΎΑέΩ ήθϣέύ Ύϧ ΖγΪҨΪΟ ΎϫϩΩΩϪϋϮϤΠϣΎΑtϥϮϣίϥΩήΑέΎ ϪΑϭΕή ήҩωύϙϣϥωή ΝέΎΧΩϮη ϤϧϥϮΗΖϓ ΪηΎΒϧϴΤλϪ σήηϫαζγh0ϥωή ΩέϝΎϤΘΣ έύϣϥϯϣί ҨpowerΎҨϥϮΗ p value ή Ζγ ϝύϥθσ έϊϙϣ ΎҨ p value ί ϩωύϔθγ έύϣ νήϓ ϥϯϣί ΎϬηϭέ ί Ҩ ΩϮη ϣωέ H 0 νήϓέϊϙϣ MeansƵŶƃįŶƴŝƵŵŹźǀƜŤƯĨƿŹŵİĭĦƿƹŶƴģŚƿƹŵįřźŝįŵŶƗźǀƜŤƯĨƿƲǀĮƳŚǀƯƶƀƿŚƤƯįźŤƯřŹŚěƱƺƯŻō ΖϴόϤΟίϩϭή ϭωϧθαϲβγύϩϣϫδϳύϙϣϥϯηϲϣζθόϥοΰϛήϥηκχύηϥϯϩϋϫαϧθ ϧύθϣϫδϳύϙϣύα ΩέϭΖγΪΑϪϧϮϤϧ ΖϴόϤΟ ϩϊϩϳύϥϧ ϭ κχύη ϥϯϩϋ ϪΑ Ϊϣ ϭ ϪϧΎϴϣ ϞΜϣ ΰϛήϤΗ ϱύϫέύθόϣ ή ϳΩ Ύϳ Ϧϴ ϧύθϣ Ϫϛ ϲϳύπϧ ί ϪδϳΎϘϣέΎϬϧΰϛήϤΗϱΎϫέΎϴόϣΖγήΘϬΑΖϴόϤΟϭΩΎϳϲ ϳϭϭΩϪδϳΎϘϣϱήΑˬΪϨΘδϫϩΪηϪΘΧΎϨη Ωήϛ Analyze Compare Means Means Dependent listζϥδϗέω Ωέϭ έ ϩϊη ϱήθ ϩίϊϧ ϲ ϳϭ Ύϳ ήθϐθϣ ϱωϊϋ ήϳωύϙϣ ϱέω ΪϳΎΑ ήθϐθϣ Ϧϳ ΪϴϨϛ ΪηΎΑ Independent list ΖϤδϗέΩϭ ϲϓήόϣέϱϊϩαϩϭή ΎϳϱΪϨΑϪΘγΩήϴϐΘϣ αύγήα ϲ ϳϭ Ϧϴ ϧύθϣ έύϛ Ϧϳ ΎΑ ΪϴϨϛ ΖγΪΑ ϩϭή ήϫ ϱήα ϱϊϩαϫθγω ήθϐθϣ ΪϧϮηϲϣϪδϳΎϘϣϭϩΪϣ ϞϫΎΗΖϴόοϭαΎγήΑΪϣέΩϪδҨΎϘϣ ϼΜϣ x x ΪηΎΑ ϣt Ϧϴ ϧύθϣϩέύϣ s n ΎϬϴ ϳϭÁStatisticsϩέΎϣέϩήϴϏϭέΎϴόϣϑήΤϧˬϦϴ ϧύθϣϫϧϯϥϧ ϳ ΎϬϴ ϳϭϪΑρϮΑήϣήϳΩΎϘϣ Report Household income in thousands Marital status Mean N Std. Deviation Unmarried 69.73 34 78.395 Married 69.6804 3176 79.1361 Total 69.4748 6400 78.71856 ΪϨҨϮ ϣparametersήθϣέύ έ ϠλϪόϣΎΟέΩΎϬϧϝΩΎόϣ Ϣϴθ ϣϟϫύηζθόοϭαύγήαΰθϧέincomeήθϐθϣϧθ ϧύθϣ έωϯϥϧ 69.7 69.68 40.00 0.00 Mean Household income in thousands60.00 3 0.00 Unmarried Marital status Married

ΎϫήϴϐΘϣ ΪϨΑϪҨϻ Ωή ΪϨΑϪҨϻϥϮΘϴϣΖϴδϨΟϩήϤϫϪΑέΖϴόϗϮϣMeanϞϤόϟέϮΘγΩίϩΩΎϔΘγΎΑ Analyze Compare Means Means- dependent list income Independent list.martial Next Independent list.genderl Report Household income in thousands Marital status Gender Mean N Std. Deviation Unmarried Female 7.0633 1533 83.001 Male 66.74 1691 73.7530 Total 69.73 34 78.395 Married Female 65.7179 1645 68.15853 Male 73.939 1530 89.7551 Total 69.6797 3175 79.13606 Total Female 68.7788 3178 75.74700 Male 70.1608 31 81.5616 Total 69.4744 6399 78.7471 4

Household income in thousands 100.00 1000.00 800.00 600.00 400.00 00.00 0.00 50 47 4,99 150,535,001,844 5,363 5,840 6,017 5,845 4,386 1,557 4,965 1,953 1,45,43 4,95,308 41 4,958 1,143 5,96 83 17 6,8 3,636 5,40 4,76,18 1,647 4,806 3,834 77,980 1,81 1,63 3,54 5,530 4,77 4,057 1,706 4,709 6,046 97 1,147 506 5,606,415 3,008 78,049 3,044,005 3,307 3,758 4,904 1,7495,649 1,86 1,31 4,449,839,636 4,604,687 5,043 ϣbox ҨήϴϐΘϣήϫ ήαέωϯϥϧωϯϧϧҩέωζγboxplot έωϯϥϧϧҩήθϭαύϫϧθ ϧύθϣϫδҩύϙϣ ήα ΖγήΘθϴΑΎϫϩΩΩ ΪϨ ή ΪηΎΑήΘθϴΑboxωΎϔΗέϪ ήϫϫ Ϊθ 1 Max Q 3 Mean=median= Q Q 1 Min ήηϻύαή ϭζγϥέύϙθϣϊηύαboxςγϭϧθ ϧύθϣή Ζγ ϟϯ ϪϧΎθϧΪηΎΑςγϭίήΗϦϴҨΎ ΎҨ ϥ ϩέύϥηϫ ΖγΕή ϩωω ҨϩΪϨϫΩϥΎθϧ Ζγ ϩέύϥηcaseϫαρϯαήϣϫ ΪϫΪϴϣϥΎθϧ ΪϨҨϮ ϣoutliarεή ϩωωϫα ΪηΎΑϪΘηΩέρήηϦҨϪ ϩωω Ϡ έϯσϫα ΩϮη ϣώύδσεή Q Q Q 3 1 1 Q Q Outliar Q 1 Q 3 1 ΩϮΟϭΎΑΪϧέΩϪϠλΎϓQήΑήΑϪγίήΘθϴΑϪ ΪϨҨϮ ϣextreme variableΰθϧεή ϠϴΧ ΎϫϩΩΩϪΑ ϢҨέϭΎϴϧΖγΪΑΖγέΩέϥϮϣίϪΠϴΘϧϭΩϮηϞҨΎϤΘϣΎϬϧΖϤγϪΑϦϴ ϧύθϣζγϧ ϤϣΕή ΎϫϩΩΩ ϢϴϨ ϑάσέεή ΎϫϩΩΩΖγήΘϬΑβ ϟϭ ΪϨ ή ϨόҨΪηΎΑϪΘηΩΩϮΟϭ ϧύηϯ Ϥϫή ήθϐθϣϭωϧθ ϧύθϣϧθα ΎϫboxplotϪδҨΎϘϣέΩ ϡύπϧέϥϯϣίωϯη ϣϭϊηύα ϤϧΎϫϦϴ ϧύθϣϧθα ΩΎҨίϑϼΘΧάϟΖγ ϣϭω ΪϨ ή ϞϣΎη ΩΩ Graph legacy Dialogs - Boxplot simple variable.income Category Axis..Martial ρύϙϧ Ϫ ΪϫΪϴϣ ϥύθϧ έωϯϥϧ ϦҨ ΩέΩΩϮΟϭ έύθδαεή ϮϨϣίΕή ΎϫϩΩΩϑάΣ ήα Data-select cases-if income<00 ϢϴϨ ϣώύψθϧέ Εή ϠϴΧ Ϫ έ ί ήθθθα ΎΗ έωϯϥϧ ϭ ΕΎΒγΎΤϣ έω ΪϨΘδϫ ΩέϭΎϴϧ Unmarried Marital status Married 5

One_ sample T Test ƵŶƃƶŤųŚƴƃŹřŶƤƯĨƿƪŝŚƤƯŹŵƶƳƺưƳƲǀĮƳŚǀƯƱƺƯŻō t ϥϯϣί ϭ ϪδϳΎϘϣ ϥύϝϣ έϯθγω Ϧϳ ϱήο ΎΑ ΩϭέϲϣέΎϛϪΑϥϮϣίζΠϨγϱήΑ ΖγΎϫϩΩΩϥΩϮΑϝΎϣήϧήΑνήϓϥϮϣίϦϳέΩ ϪΘηΩ ϱωϊϋ ήϳωύϙϣ ΪϳΎΑ ϩϊη ϲϓήόϣ ήθϐθϣ ΪηΎΑ ΪϨηΎΑ ϪΘηΩ Ϣϫ ϲ ϟϯ ϱέϊϙϣ ΎϫϩΩΩ ή ΩήϛΪϫϮΧΖϣϭΎϘϣϩΎΒΘηήΑήΑέΩϥϮϣίϦϳ Ζγ ϩϊη ϪΘϓή ήψϧ έω ήϳί ϞϜη ϪΑ ϥϯϣί ϲγϊσέϊϙϣ80ϭζγϫϧϯϥϧϧθ ϧύθϣέϊϙϣ ϢϴϧίϲϣϦϴ ϧύθϣϱήαζθόϥοίϫϛζγ Analyze compare Means One_ sample T Test / Test variable Income Test value.. 80 ϼΜϣΎΠϨϳέΩtest value έϊϙϣύαϊϳύαϥύθϩθ ϧύθϣϫϛϲϳύϫήθϐθϣύϳήθϐθϣtest variablesζϥδϗέω Ωήϴ ϲϣέήϗωϯηϫδϳύϙϣ80 ΩϮΟϭ95%ϥΎϨϴϤσϪϠλΎϓϚϳϼΜϣϥΎϨϴϤσϪϠλΎϓΪλέΩϒϳήόΗϥΎϜϣΰϴϧoptionsϪϤϛΩΏΎΨΘϧΎΑ ΩέΩ ϞϣΎηϪϛΖγϒϠΘΨϣϱΎϫϪϧϮϤϧίϲϠλϮϓΪλέΩϩΪϨϫΩϥΎθϧϥΎϨϴϤσϪϠλΎϓϢϴηΎΑϪΘηΩΖϗΩ ˬΖϴόϤΟίή ϳΩϪϧϮϤϧ έωˬϊϫωϲϣϥύθϧ ϥύϩθϥσϫϡλύϓϛϳϝύμϣϱήαϊϩηύαϲϣϧθ ϧύθϣ ΩϮΑΪϨϫϮΧϪϠλΎϓϦϳέΩΎϫϪϧϮϤϧϦϴ ϧύθϣˬϫϧϯϥϧ ΖδϴϧϩΪηϪΘϓή ϪϧϮϤϧί ηύϧ ϨόΑΖγέΩ Ϩόϣ ϭύδηύҩϑϼθχϧθҩϯ ϣ Θϗϭ ϪϧϮϤϧ ϨόΑ Ζγ Ϩόϣ ΎҨ ϣ ΘϗϭϪΘ ϧϫθ ϧ ΎҨϑϼΘΧ ϨόҨΪηΎΒϧέΩ Ϩόϣή ΩϮΑΪϫϮΧϦϴϤϫϪΠϴΘϧΩϮηϥϮϣίϢϫ ή ҨΩϪϧϮϤϧΎΑή Ϫ ϠΑή Ϫ ϠΑ ΖγϥϮϣίˬκϴΨθΗεϭέΖγΖϴόϤΟί ηύϧ ϭύδη 6

ˮϪϧΎҨΖγέΩ ϨόϣΪϣέΩήϴϐΘϣΩέϮϣέΩ έϊϙϣύαϩϊηϩωίαϊσϧθ ϧύθϣϣθϩθβαϣθϫϯχ ϣ H0: 80 H1: 80 SPSS H0: 80 0 H1: 80 0 T-TEST /TESTVAL = 80 /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95). T Household income in thousands Household income in thousands x x s = n One-Sample Statistics N Mean Std. Deviation Std. Error Mean 6400 69.4748 78.71856.98398 69.47.48 80 0.98398 One-Sample Test Test Value = 80 t df Sig. (-tailed) 95% Confidence Interval of the Mean Difference Difference Lower Upper -10.696 6399.000-10.5516-1.4541-8.596 = 10.5516 0.98398 = -10.696 < 0 ϪΠϴΘϧ Ζγ6400-1=6399 ϨόҨn-1 ΩίϪΟέΩ Sig.(-tailed) = p value 0.000 <0.05 = H 0 νήϓωέωέϊϧωϯοϭ H 0 ΪϴҨΎΗ ήα ϠϴϟΩβ ΖγϩΪηήΘ Ϯ ί p value έϊϙϣϥϯ ΩϮΧϦҨϪ ΪηΎΑ ϤϧήϔλϞϣΎη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ϭϩϊη ϔϨϣ 80 ϨόҨΖγ ϔϨϣϩίΎΑϦҨϥϮ ΖγέΎ ηεϭύϔηϩϊϩϫωϥύθϧϭζγ νήϓϩϊϩϩ ΩέϞϣΎϋ H 0 ΖγήΘϤ ίϧθ ϧύθϣ ϨόҨ ΖγέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ. 7

H0: 70 H1: 70 T-TEST /TESTVAL = 70 /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95). Household income in thousands SPSS H0: 70 0 H1: 70 0 One-Sample Statistics Std. Error N Mean Std. Deviation Mean Household income in thousands 6400 69.4748 78.71856.98398 One-Sample Test t df Sig. (-tailed) Test Value = 70 8 ϢϴϨ ϣέή Ηΰϴϧ ΩΪϋ ήαέϥϯϣίϧҩ 95% Confidence Interval of the Difference Mean Difference Lower Upper -.534 6399.594 -.5516 -.4541 1.4038 ϪΠϴΘϧ Sig.(-tailed) = p value 0.594!0.05 = ΩέΪϧΩϮΟϭ H 0 Ωέ ήα ϠϴϟΩβ ΖγϩΪηήΘ έΰα ί p value έϊϙϣϥϯ ΩϮΧϦҨϪ ΪηΎΑ ϣήϔλϟϣύη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγϪϧϮϤϧςγϮΗ H 0 νήϓϩϊϩϩ ΪϴҨΎΗϞϣΎϋ ΖδϴϧέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ. ήθθϐηέ ΎҨϝϭωϮϧ ΎτΧέΪϘϣϥϮΘϴϣOptionΖϤδϗέΩOne_ sample T TestϩήΠϨ έωϫθ ϧϫθ ϧ ΪϧϮη ϣωέύϫϫθοήϓήθθθαϣҩά Α έ ϨόҨϢҨήΒΑϻΎΑ ΎΗέϥΎϨϴϤσΐҨήοή ΩΩ

Statistics ϝύμϣ ΪҨέϭΖγΪΑέweightήϴϐΘϣΎҨϥίϭϦϴ ϧύθϣϭϊθϩ ίύαέspss sample data.savϟҩύϓ Analyze- report frequency Ζγ ϥίϭϧθ ϧύθϣϫ ϨҨνήϓΎΑΪϴϫΩϡΎΠϧ ϧϯϣί H0: 75 H1: 75 SPSS H0: 75 0 H1: 75 0 T-TEST /TESTVAL = 75 /MISSING = ANALYSIS /VARIABLES = weight wieght_after /CRITERIA = CI(.95). weight N Mean Valid Missing 11 0 68.318 weight One-Sample Test Test Value = 75 95% Confidence Interval Mean of the Difference t df Sig. (-tailed) Difference Lower Upper -1.0 10.50-6.6818-18.8805 5.5169 ϪΠϴΘϧ Sig.(-tailed) = p value 0.50!0.05 = ΩέΪϧΩϮΟϭ H 0 Ωέ ήα ϠϴϟΩβ ΖγϩΪηήΘ έΰα ί p value έϊϙϣϥϯ ΩϮΧϦҨϪ ΪηΎΑ ϣήϔλϟϣύη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγϪϧϮϤϧςγϮΗ H 0 νήϓϩϊϩϩ ΪϴҨΎΗϞϣΎϋ ΖδϴϧέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ. 9

ΪҨέϭΖγΪΑέweight -afterήθϐθϣύҩϣҩ έίϊόαϥίϭϧθ ϧύθϣ Analyze compare Means One_ sample T Test / Test variable weight, weight - after Test value.. 75 T-TEST /TESTVAL = 75 /MISSING = ANALYSIS /VARIABLES = wieght_after weight /CRITERIA = CI(.95). Ζγ ϢҨ έίϊόαϥίϭϧθ ϧύθϣϫ ϨҨνήϓΎΑΪϴϫΩϡΎΠϧ ϧϯϣί One-Sample Statistics weight wieght_after Std. Error N Mean Std. Deviation Mean 11 68.318 18.1580 5.47485 11 63.5455 14.940 4.3093 One-Sample Test weight wieght_after Test Value = 75 95% Confidence Interval of the Mean Difference t df Sig. (-tailed) Difference Lower Upper -1.167 9.73-7.05000-0.7111 6.6111 -.658 10.04-11.45455-1.0563-1.858 ϪΠϴΘϧ Sig.(-tailed) = p value 0.04 0.05 = H 0 νήϓωέωέϊϧωϯοϭ H 0 ΪϴҨΎΗ ήα ϠϴϟΩβ ΖγϩΪηήΘ Ϯ ίweight-afterήθϐθϣ) p value έϊϙϣϥϯ ΩϮΧϦҨϪ ΪηΎΑ ϤϧήϔλϞϣΎη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγέΎ ηεϭύϔηϩϊϩϫωϥύθϧϭζγ H 0 νήϓωέϟϣύϋ ΪηΎΑϡή ϮϠϴ ΪϧϮΘϴϤϧϢҨ έίϊόαϥίϭϧθ ϧύθϣβ ΖγέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ. exclude cases analysis by ϪϨҨΰ optionέωή ϪΘ ϧ έω ϢϴηΎΑ ϪΘηΩ missing ϭ ϢϴϧΰΑ ϴΗ έ analysis ϭϊϩ ϣώύδσϫϧύ ΪΟέϡΪ ήϫήθϐθϣϭωϫδҩύϙϣ ϪΘϓή ήψϧέωϥϭϊαέϫθϓέέύϛϫαϱύϫήθϐθϣϱήαϥϯϣί ΩϭέϲϣέΎϛϪΑή ϳΩϱΎϫήϴϐΘϣϱήΑϩΪθϤ ήϳωύϙϣ ϴΗέexclude cases listwise ϨόҨϡϭΩϪϨҨΰ ή ϟϭ ϣϑάσέωέω missingϫ caseύҩωέϯϣϟ ϢϴϧΰΑΎҨΩέϮϣϞ ϢϴϧΰΑ έύϛϫαϱύϫήθϐθϣϫϥϫϱήαϩϊθϥ έϊϙϣύαωέϯϣϊϩ ΪϨ έω Ϫ ΪηΎΑ ϩωω ϥωή ϧ ҨΎΗήΛ έω Ϫ missing ϦҨ.Ϊη ΪϫϮΨϧ ϪΘϓή ήψϧ έω ϥϯϣί έω ϪΘϓέ ΪϨ Ϥϧ ϗήϓϊηύαϩϊηϒҩήόηmissing value 30

( Independent-Samples T TestƵŵŹŚƿƵƹźĭƹŵƲǀŝźǀƜŤƯĨƿƲǀĮƳŚǀƯƱƺƯŻō ϢϴϨϛϲϣϩΩΎϔΘγεϭέϦϳίϩϭή ϭωϧθαέωζθόϥοϲ ϳϭϚϳϦϴ ϧύθϣϥωϯαήαήαϥϯϣίϱήα ϲϥϛ έϊϙϣ Ϛϳ ήθϐθϣ ϭ Ζγ ϩϊη ϊϳίϯη ϝύϣήϧ ΕέϮλ ϪΑ ΖϴόϤΟ ϲ ϳϭ Ϫϛ Ζγ ϦϳήΑ νήϓ ΖγϱΩΪϋ ΖγϩΪηΏΎΨΘϧϲϓΩΎμΗϭϞϘΘδϣΕέϮλϪΑϪϧϮϤϧϦϴϨ Ϥϫ ΖγέΩέϮΧήΑΖϴϤϫίϥϮϣίϦϳέΩΕή ρύϙϧωϯοϭϡϊϋϭϥέύϙη ΖδϴϧαΎδΣϲ ϟϯ ϪΑΖΒδϧϥϮϣίϦϳ ΪϫΩϲϣήϴϴϐΗέϥϮϣίϲΟϭήΧˬϩϭή ϭωέωύϫ ϥϥωϯβϧήαήαύαύϫβϧύϳέϭϥωϯαήαήα ΪϴϨϛϲσέήϳίήϴδϣέϮΘγΩϦϳϪΑϲγήΘγΩϱήΑ Analyze - Compare Means - Independent-Samples T Test/Test variables.weight Grouping variables gender test variableζϥδϗέω έ ΪϧϮη ϥϯϣί ΪϳΎΑ Ϫϛ ϲϳύϫήθϐθϣ Ύϳ ήθϐθϣ ΪϴϨϛΩέϭ Grouping VariableΖϤδϗέΩ ΪϴϨϛκΨθϣέϱΪϨΑϩϭή ήθϐθϣ ϭω ϦϴΑ ϪδϳΎϘϣ ΪϧϮΗϲϣ ςϙϓ ϥϯϣί Ϧϳ ΩέϭΖγΪΑέϩϭή ήθϐθϣ ϱήα define group ϪϤϛΩ ΏΎΨΘϧ ΎΑ ϭω ϒϳήόΗ ϱήα ήψϧ ΩέϮϣ έϊϙϣ ϱϊϩαϩϭή ΪϨΑϩϭή ήθϐθϣή ΘΣΪϴϨϛΩέϭέϩϭή ϥϯϣί ϦҨ ΪηΎΑ ϪΘηΩ ϩϭή ϭω ί ήθθθα ΪηΪϫϮΧϪΒγΎΤϣϩϭή ϭωέωςϙϓ έϊϙϣϱϭύδϣϭήθθθαϭήθϥϛεέϯλϫαύϫϩϭή ϚϴϜϔΗϥΎϜϣΰϴϧcut pointέϊϙϣϣθψϩηύαϧθϩ Ϥϫ H0: m m 1 H1: m m 1 SPSS H0: m m 0 1 H1: m m 0 1 Ζδϫΰϴϧcut point κψθϣέϝϭϫθγωˬϱϊϩαϩϭή ήθϐθϣϱήαgroup1έϊϙϣ ΪϨϛϲϣ κψθϣέϡϭωϫθγωˬϱϊϩαϩϭή ήθϐθϣϱήαgroupέϊϙϣ ΪϨϛϲϣ Cut έϊϙϣϧθθόηύαϥϯθθϣωϯα ΩΪϋˬ ΪϨΑϩΩέήϴϐΘϣή Ωή ϒϳήόΗήϳίΕέϮλϪΑέϩϭή ϭωpoint cut pointίϱϭύδϣϭήθθθαήϳωύϙϣ ϩϭή cut pointίήθϥϛήϳωύϙϣ ϩϭή 31

H0: m m 1 H1: m m 1 SPSS H0: m m 0 1 H1: m m 0 1 ΪϴϨ γέήαϥωήϣϭϥύϧίϩϭή ϭωέωέϥίϭήθϐθϣϧθ ϧύθϣ ϥωήϣϩϭή έωϥίϭήθϐθϣϧθ ϧύθϣ m 1 ϥύϧίϩϭή έωϥίϭήθϐθϣϧθ ϧύθϣ m T-TEST GROUPS = gender(1 ) /MISSING = ANALYSIS /VARIABLES = weight /CRITERIA = CI(.95). Group Statistics weight gender N Mean Std. Deviation Std. Error Mean male 4 75.7500 19.103 9.5601 female 6 6.7500 18.86730 7.7054 weight Equal variances assumed Equal variances not assumed Levene's Test for Equality of Variances F Sig. Independent Samples Test t df Sig. (-tailed) t-test for Equality of Means Mean Difference ϪΠϴΘϧ 95% Confidence Interval of the Difference Std. Error Difference Lower Upper.009.98 1.06 8.319 13.00000 1.407-15.611 41.611 1.059 6.51.37 13.00000 1.7701-16.47694 4.47694 Sig = p value 0.98 > 0.05 = ϤϧΩέΎϫβϧΎҨέϭ ήαήαβ ΖγϩΪηήΘ έΰα ίζγβϧύҩέϭϫαρϯαήϣϫ weightήθϐθϣ) p value έϊϙϣϥϯ Ωή ϢϴϫϮΧ γέήαέϡϭωήτγεέϯμϩҩήθϗέωϣθϩ ϣϩύ ϧϝϭήτγέωϧθ ϧύθϣϫαρϯαήϣ p value ϪΑάϟΩϮη Sig(-tailed) = p value 0.319 > 0.05 = ΩϮΧϦҨϪ ΪηΎΑ ϣήϔλϟϣύη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγϪϧϮϤϧςγϮΗ H 0 νήϓϊθҩύηϟϣύϋ ΖγήΑήΑϥΎϧίϭϥΩήϣϩϭή ϭωέωϥίϭϧθ ϧύθϣβ ΖδϴϧέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ. 3

ΪϴϨ γέήαϝύγ ϦϴҨΎ ϭϝύγ ϻύα Ϩγϩϭή ϭωέωέϥίϭήθϐθϣϧθ ϧύθϣ computeύҩrecodeϩέίϥϯθθϣϝύγ ϦϴҨΎ ϭϝύγ ϻύαϩϭή ϭωϫαϥίϭήθϐθϣ ΪϨΑϩϭή ήα ϪϤ Ωϭ Independent-Samples T TestϩήΠϨ έωϫ ΖγϦҨϩέϦҨήΗϩΩΎγ ϟϭωϯϥϧϩωύϔθγ Ωή Ωέϭέ ΩΪϋϭΩϮϤϧϩΩΎϔΘγcutpointϪϨҨΰ ίoption H0: m m 1 H1: m m 1 SPSS H0: m m 0 1 H1: m m 0 1 ϝύγ ίήηϧθҩύ Ϩγϩϭή έωϥίϭήθϐθϣϧθ ϧύθϣ m 1 ϝύγ ίήηϻύα Ϩγϩϭή έωϥίϭήθϐθϣϧθ ϧύθϣ m T-TEST GROUPS = age(30) /MISSING = ANALYSIS /VARIABLES = weight /CRITERIA = CI(.95). Group Statistics weight age N Mean Std. Deviation Std. Error Mean >= 30.00 7 74.5714 18.36534 6.94145 < 30.00 3 5.5000 10.8517 6.6498 Sig = p value 0.81Levene's > 0.05 Test = for weight Equal variances assumed Equal variances not assumed Equality of Variances F Sig. Independent Samples Test t df Sig. (-tailed) t-test for Equality of Means Mean Difference ϪΠϴΘϧ 95% Confidence Interval of the Std. Error Difference Difference Lower Upper 1.334.81 1.903 8.093.07143 11.5964-4.66997 48.8183.360 6.606.05.07143 9.35060 -.30937 44.453 33 ϪΠϴΘϧ ϤϧΩέΎϫβϧΎҨέϭ ήαήαβ ΖγϩΪηήΘ έΰα ίζγβϧύҩέϭϫαρϯαήϣϫ weightήθϐθϣ) p value έϊϙϣϥϯ Ωή ϢϴϫϮΧ γέήαέϡϭωήτγεέϯμϩҩήθϗέωϣθϩ ϣϩύ ϧϝϭήτγέωϧθ ϧύθϣϫαρϯαήϣ p value ϪΑάϟΩϮη Sig(-tailed) = p value 0.093> 0.05 = ΩϮΧϦҨϪ ΪηΎΑ ϣήϔλϟϣύη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγϪϧϮϤϧςγϮΗ H 0 νήϓϊθҩύηϟϣύϋ ΖγήΑήΑϝΎγ ίήηϧθҩύ ϭϝύγ ίήηϻύα Ϩγϩϭή ϭωέωϥίϭϧθ ϧύθϣβ ΖδϴϧέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ.

Ϫ ϧύδ ϭ ΪϧέΩ ϪϴϟΎϋ ΕϼϴμΤΗ Ϫ ϧύδ ϩϭή ϭωέω Demo ϞҨΎϓ έω έ ΪϣέΩ ήθϐθϣ Ϧϴ ϧύθϣ ΪϴϨ γέήαϊϧϩωή ϧϟθϥ ΗέϥΎθΗϼϴμΤΗ ϩϊθϧϟθϥ ΗΕϼϴμΤΗϩϭή έωincomeϊϣέωήθϐθϣϧθ ϧύθϣ m 1 H0: m1 m H0: m 1 m 0 ϪϴϟΎϋΕϼϴμΤΗϩϭή έωincomeϊϣέωήθϐθϣϧθ ϧύθϣ m SPSS H1: m m H1: m1 m 0 1. T-TEST GROUPS = ed(1 5) /MISSING = ANALYSIS /VARIABLES = income /CRITERIA = CI(.95). Group Statistics Household income in thousands Std. Error Level of education N Mean Std. Deviation Mean Did not complete high school 1390 59.866 61.30036 1.6440 Post-undergraduate degree 359 87.1699 94.79998 5.00335 Household income in thousands Equal variances assumed Equal variances not assumed Levene's Test for Equality of Variances F Sig. Independent Samples Test t df Sig. (-tailed) t-test for Equality of Means Mean Difference 95% Confidence Interval of the Std. Error Difference Difference Lower Upper 35.18.000-6.636 1747.000-7.30373 4.11419-35.3798-19.3448-5.184 438.180.000-7.30373 5.6659-37.65464-16.958 Sig = p value 0.000 0.05 = ΩέΎϫβϧΎҨέϭ ήαήαβ ΖγϩΪηήΘ Ϯ ίζγβϧύҩέϭϫαρϯαήϣϫ incomeήθϐθϣ) ΩϮη ϣωέ H 0 νήϓβ Ζγ ίήθ Ϯ ϢϴϨ ϣϩύ ϧϡϭωήτγέωϧθ ϧύθϣϫαρϯαήϣ Sig(-tailed) = p value 0.000 0.05 = 34 ϪΠϴΘϧ p value έϊϙϣ ϥϯ p value ϪΑάϟΩϮη ϣ ΩϮΧϦҨϪ ΪηΎΑ ϤϧήϔλϞϣΎη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγέΎ ηεϭύϔηϩϊϩϫωϥύθϧϭζγ H 0 νήϓωέϟϣύϋ ΖδϴϧήΑήΑϪϴϟΎϋϭϩΪθϧϞϴϤ ΗΕϼϴμΤΗϩϭή ϭωέωϊϣέωϧθ ϧύθϣβ ΖγέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ.

( Paired-Samples T Test ŢƠūźǀƜŤƯƹŵƲǀĮƳŚǀƯƶƀƿŚƤƯƱƺƯŻō ϢϴϨϛϲϣϩΩΎϔΘγϥϮϣίϦϳίΪϧϮηϪδϳΎϘϣΪϳΎΑϪϧϮϤϧέΩήϴϐΘϣϭΩϦϴ ϧύθϣή ϪϛΪϧϮηϲϣϪΘϓή ήψϧέωx,yΐηήϣνϭίεέϯλϫαϩϊηϱήθ ϩίϊϧϱύϫϫμψθϣϥϯϣίϧϳέω ΖγϪϧϮϤϧέΩϡϭΩϲ ϳϭyϭϝϭϲ ϳϭxϥέΩ ΪΘΑέΩϪϧϮϤϧΩήϓϪΑϢϴϨϛκΨθϣϥϮΧέΎθϓζϫΎϛέΩέϭέΩϚϳήΛϢϴϫϮΧϲϣ ϝύμϣ ϱήα ϥϯχ έύθϓ ΰϴϧ ϲόϗϭ ϭέω ϥϊϧωέϯχ ί β γ ϢϴϨϛϲϣ ϱήθ ϩίϊϧ έ ϥϯχ έύθϓ ϩωω ΎϤϧϭέΩ ϪδϳΎϘϣϲόϗϭϱϭέΩίΪόΑϭϞΒϗέϥϮΧέΎθϓϦϴ ϧύθϣϊϫϯχϲϣϥϯϣίϊηϊϫϯχϱήθ ϩίϊϧ ΪϨϛ ΪϨΘδϫϱΩΪϋήϳΩΎϘϣΖγϦϳήΑνήϓ ΖγϝΎϣήϧϊϳίϮΗϱέΩΎϬϧΕϭΎϔΗΎϳΪϧέΩϝΎϣήϧϊϳίϮΗήϴϐΘϣϭΩήϫΎϫ ϩωω ΪϧϩΪηΏΎΨΘϧϲϓΩΎμΗϭϞϘΘδϣΕέϮλϪΑΎϫϪϧϮϤϧ έϯσϫαήθϐθϣϛϳέωϩϊθϥ έϊϙϣωϯοϭεέϯλέωϊηύαήαήαϊϳύαήθϐθϣϭωήϫϱήαύϫϫϧϯϥϧωϊόη ΪηΪϫϮΧϪΘϓή ϩϊϳωύϧωέϯϣϥϟϣύϛ ΩέΪϧϲϟΎϜηήϴϐΘϣϭΩβϧΎϳέϭέΪϘϣέΩΕϭΎϔΗ ΪϴϨϛϲσέήϳίϞΣήϣϥϮϣίϦϳϪΑϲγήΘγΩϱήΑ Analyze - Compare Means - Paired-Samples T Test... H0: m m 1 H1: m m 1 SPSS H0: m m 0 1 H1: m m 0 1 ϝϭήθϐθϣϧθ ϧύθϣ m 1 ϡϭωήθϐθϣϧθ ϧύθϣ m ϪΑϭϩΩήϛΏΎΨΘϧ ΖϤγΖϤδϗέΩέήϴϐΘϣϭΩήϫ.ΪϴϫΩϝΎϘΘϧpair VariableΖϤδϗ 35

Analyze - ΪϴϨ ϪδҨΎϘϣϢϫΎΑέweight-after ϭweightήθϐθϣϭω Compare Means - Paired-Samples T Test... H0: m m 1 H1: m m 1 SPSS H0: m m 0 1 H1: m m 0 1 weightϥίϭήθϐθϣϧθ ϧύθϣ m 1 weight-afterϣҩ έίϊόαϥίϭήθϐθϣϧθ ϧύθϣ m T-TEST PAIRS = weight WITH wieght_after (PAIRED) /CRITERIA = CI(.95) /MISSING = ANALYSIS. Paired Samples Correlations N Correlation Sig. Pair 1 weight & wieght_after 10.941.000 Paired Samples Test Pair 1 weight - wieght_after Paired Differences 95% Confidence Interval of the Std. Error Difference Mean Std. Deviation Mean Lower Upper t df Sig. (-tailed) 5.65000 7.37884.33339.37150 10.9850.41 9.039 ϪΠϴΘϧ ήθϐθϣϭω ϨόҨΪηΎΑήΘ ҨΩΰϧ1ΩΪϋϪΑΩΪϋϦҨϪ ήϫϫ ΪϫΪϴϣϥΎθϧέ0.941ΩΪϋcorrelationΎҨ ΘδΒϤϫΐҨήο ΪϧΩϮΑ ϨϫΎϤϫϢϫΎΑ ϼϣΎ ϭζγϫθηωωϯοϭϣҩ έίβ ϥίϭζϫύ ΩέϮϣϡΎϤΗέΩ ϨόҨΪϧέΩϢϫ ϭέ ήθθθαήλ ΩϮη ϣωέ H 0 νήϓˬ ί p value ϥωϯαήθ Ϯ ϪΑϪΟϮΗΎΑ Sig(-tailed) = p value 0.039 0.05 = ΩϮΧϦҨϪ ΪηΎΑ ϤϧήϔλϞϣΎη95% Confidence Interval of the DifferenceϥϮΘγέΩϩΪηϩΩΩϥΎθϧΩΪϋϭΩ ΖγέΎ ηεϭύϔηϩϊϩϫωϥύθϧϭζγ H 0 νήϓωέϟϣύϋ ΖδϴϧήΑήΑϢҨ έίϊόαϥίϭϭϥίϭήθϐθϣζϔοϧθ ϧύθϣβ ΖγέΩ ϨόϣϑϼΘΧ ΖγέΩ ϨόϣˬΪηΎΑ ίήθ έΰαύҩήαήαtϖϡτϣέϊϗ. 36

One-Way AnovaƵƹźĭƲƿŶƴģŹŵźǀƜŤƯĨƿƲǀĮƳŚǀƯƶƀƿŚƤƯƱƺƯŻō ΩϮΟϭϩϭή ϦϳΪϨ ϦϴΑέΩκΧΎηϚϳέΪϘϣϪδϳΎϘϣϥΎϜϣϪϓήσϚϳβϧΎϳέϭΰϴϟΎϧϝϭΪΟίϩΩΎϔΘγΎΑ ΩέΩ ϲϫϭή ϥϭέω ϱύτχ ΕΎόΑήϣ Ϧϴ ϧύθϣ έϊϙϣ ΖΒδϧ ή βϧύϳέϭ ΰϴϟΎϧ ϝϭϊο ϱύϫϩέύϣ ϪΑ ϪΟϮΗ ΎΑ ϲϫϭή ϦϴΑϱΎτΧΕΎόΑήϣϦϴ ϧύθϣέϊϙϣύαϩϭή Ϧϴ ϧύθϣίϩϭή ήϫήϳωύϙϣϑϼθχεύόαήϣωϯϥπϣ ζϙϧϩϊϩϫωϥύθϧϊηύαϱωύϳίϑϼθχϱέωϟϛϧθ ϧύθϣίϩϭή ήϫϧθ ϧύθϣϑϼθχεύόαήϣωϯϥπϣ ΪϫΩϲϣϥΎθϧέϩϭή Ϧϴ ϧύθϣήθθϐηέωϟϣύϋ ϪϴϟϭϱΎϫνήϓ ΪηΎΑϪΘηΩϴΤλήϳΩΎϘϣΪϳΎΑέϮΘϛΎϓήϴϐΘϣ ΪηΎΑϪΘγϮϴ ήϳωύϙϣϱέωϊϳύαϫθδαϭήθϐθϣ ΪϨηΎΑϲϓΩΎμΗϝΎϣήϧϊϳίϮΗϱέΩΪϳΎΑϩϭή ήϫέωϫϧϯϥϧ ΪηΎΑϥέΎϘΗϱέΩϊϳίϮΗΪϳΎΑϲϟϭΖγϡϭΎϘϣϥΩϮΑϝΎϣήϧρήηϪΑΖΒδϧβϧΎϳέϭΰϴϟΎϧϥϮϣί ϩέύϣ ί ϩωύϔθγ ΎΑ έ ρήη Ϧϳ ΪϨηΎΑ ϩϊη ΏΎΨΘϧ ήαήα βϧύϳέϭ ΎΑ ϲθθόϥο ί ΪϳΎΑ Ύϫϩϭή Ωήϛϱήϴ ϩίϊϧϥϯηϲϣlevens ΩέΩΩϮΟϭΎϫϩϭή ϦϴΑέΩϦϴ ϧύθϣέϊϙϣέωήθθϐηϱήθ ϩίϊϧϥύϝϣϝϭϊοϧϳίϩωύϔθγύα ΪϴϨϛϲσέήϳίϞΣήϣέϮΘγΩϦϳϪΑϲγήΘγΩϱήΑ Analyze - Compare Means - One-Way ANOVA... Ωέϭ Dependent List ϞΤϣ έω έ ϪΘδΑϭ ήθϐθϣ ΪϴϨϛ έ ϪΘδΑϭ ήθϐθϣ έϊϙϣ Ϫϛ ϱήθϐθϣ ϞϣΎϋ ήθϐθϣ ϥϯϣί ΪϴϫϮΧϲϣ ϥ ϒϠΘΨϣ ϱύϫϩϭή ϪΑ ΖΒδϧ ΪϴϨϛΩέϭFactorΖϤδϗέΩέΪϴϨϛ Ωέϭ Ζδϴϟ έω έ ϪΘδΑϭ ήθϐθϣ ϦϳΪϨ ϥϯηϲϣ ήθϐθϣ Ϛϳ ί ςϙϓ έϯθϛύϓ ήθϐθϣ ϱήα ϲϟϭ ΪϴϨϛ ΩήϛϩΩΎϔΘγϥϮΗϲϣ έϯθϛύϓήθϐθϣρϯτγέωήθϐθϣήϫϧθ ϧύθϣέύϛϧϳύα ΪϧϮηϲϣϪδϳΎϘϣ ϡύπϧέύϫϲϳύηϭωϫαρϯαήϣϥϯϣίϊθϫϯχ ϲϣή ΪϴϨϛΏΎΨΘϧέPost HocϪϨϳΰ ΪϴϫΩ ϥωϯα ήαήα ΕέϮλ έω ΪϴϧϮΗϲϣ έ ϥϯϣί ωϯϧ ΪϨϧΎϣ ϲϟϭ έωύϛ ϱύϫϫϩϳΰ ί ϲϝϳ ΎϫβϧΎϳέϭ ΪϴϨϛΏΎΨΘϧtukey-Bunfferoni- LSD ΪϴϧΰΑέContinueϪϤϛΩβ γ ϱύϫϧθ ϧύθϣ ί ϲτχ ϲβθϛήη ΪϴϫϮΧϲϣ ή Contrast ϪϤϛΩ ί ΪϴϨϛ ϪδϳΎϘϣ Ϣϫ ΎΑ έ Ύϫϩϭή ΪϴϨϛϩΩΎϔΘγ ΐϳήοωϮϤΠϣΖγήΘϬΑϪϛΪϴηΎΑϪΘηΩϪΟϮΗ ΪηΎΑήϔλήΑήΑϲτΧΐϴϛήΗ 37

CoefficientΖϤδϗέΩέϦϴ ϧύθϣήϫΐϳήοΐθηήηϫα ΪϴϧΰΑέAddϪϤϛΩϭϩΩήϛΩέϭ Coefficient ΖϤδϗέΩΐϳήοωϮϤΠϣϪϛΪϴϨϛΖϗΩ ΪηΎΑήϔλήΑήΑΪϳΎΑϭΖγϩΪηϪΒγΎΤϣTotal ϪϨϳΰ ΪϳέΩΝΎϴΘΣΎϫϦϴ ϧύθϣϧθαϲτχϫταέϛϳή ΪϴϨϛΏΎΨΘϧέPolynomial ΪϴϧΰΑέContinueϪϤϛΩ źǀɯťưŷƴģʋǀįƴśǀưƶƀƿśƥưʊƺưżō ΩϮΑΪϫϮΧήϳίΕέϮλϪΑϥϮϣίνήϓ H0: m1=m=m3 H1: m1#m#m3 ϡϊϋζϡϋϫϛωήϛκψθϣϲϳύηϭωϱύϫϥϯϣίίϩωύϔθγύαϥϯη ϲϣωϯηωέήϔλνήϓϫϛϲηέϯλέω ΖγϩΩϮΑΝϭίϡΪϛϱήΑϱϭΎδΗ ΪϨγΎϨηϲϣΰϴϧPost HocϥϮϨϋϪΑέϥϮϣίϦϳ ΩέΩΩϮΟϭContrastΎϫϦϴ ϧύθϣίϲβθϛήηϥϯϣίϥύϝϣϧθϩ Ϥϫ 38

Analyze - ˮΩέΩ ήλϫ ϠϴμΤΗϒϠΘΨϣΡϮτγήΑΪϣέΩϦϴ ϧύθϣϫ ΪϴϨ ϪδҨΎϘϣDemoϞҨΎϓέΩ Compare Means - One-Way Anova H 0 : m m m m m 1 3 4 5 H1: m m m m m 1 3 4 5 did not completed high schoolϝϭϩϭή έωϊϣέω ήθϐθϣϧθ ϧύθϣ m 1 high sckool degreeϡϭωϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m some collegeϡϯγϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m 3 college degreeϡέύϭ ϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m 4 post-undergraduate degreeϣπϩ ϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m 5 GET FILE='C:\Program Files\SPSS\Tutorial\sample_files\demo.sav'. DATASET NAME DataSet1 WINDOW=FRONT. ONEWAY income BY ed /STATISTICS HOMOGENEITY /MISSING ANALYSIS. Test of Homogeneity of Variances Household income in thousands Levene Statistic df1 df Sig. 14.766 4 6395.000 ANOVA Household income in thousands Sum of Squares df Mean Square F Sig. Between Groups 376079.699 4 94019.95 15.309.000 Within Groups 397604.51 6395 6141.680 Total 396511.950 6399 Sig = p value 0.000 0.05 = ϪΠϴΘϧ ΩϮη ϣωέ H 0 νήϓˬ ί p value ϥωϯαήθ Ϯ ϪΑϪΟϮΗΎΑ ήαήαεϼθμτη ΎϬϫϭή ϦϴΑέΩΪϣέΩήϴϐΘϣϦϴ ϧύθϣβ ΖγΕϼϴμΤΗ ΪϨΑϩϭή ί ηύϧϭζγέω ϨόϣϑϼΘΧ Ζδϴϧ between Groups -ϞϛϦϴ ϧύθϣίϩϭή ήϫϧθ ϧύθϣϑϼθχεύόαήϣωϯϥπϣ ϫϭή ϦϴΑ ΎτΧΕΎόΑήϣΩΎҨίΕϭΎϔΗ ϩϊϩϫωϥύθϧwithin Groups±ϩϭή Ϧϴ ϧύθϣίϩϭή ήϫήϳωύϙϣϑϼθχεύόαήϣωϯϥπϣϲϫϭή ϥϭέωϱύτχεύόαήϣύα Ζγϩϭή Ϧϴ ϧύθϣήθθϐηέωfactorϟϣύϋζϙϧ 39

βϧύҩέϭϫ ΪϫΪϴϣϥΎθϧTest of Homogeneity of VariancesϝϭΪΟέΩΎϬδϧΎҨέϭϪδҨΎϘϣϥϮϣίίϩΪϣΖγΪΑΞҨΎΘϧ = Sig< ϭϊϧέω έω ϨόϣϑϼΘΧΕϼϴμΤΗήϴϐΘϣϩϭή έωϊϣέωήθϐθϣ Ύϫ p value 0.000 ήθϐθϣϩϭή έωϊϣέωήθϐθϣ ΎϫϦϴ ϧύθϣϧθαϫ ΪϫΪϴϣϥΎθϧANOVAϝϭΪΟέΩβϧΎҨέϭΰϴϟΎϧίϩΪϣΖγΪΑΞҨΎΘϧ ϢϫΎΑϭΩϪΑϭΩPost HOCϪϨҨΰ ίϩωύϔθγύαϭϣθηύαύϭϓϼθχϝύβϧωϫαϊҩύαβ ΩέΩΩϮΟϭ έω ϨόϣϑϼΘΧΕϼϴμΤΗ ϢϴϨ ϪδҨΎϘϣ ΩέΩΩϮΟϭΖϟΎΣϭΩPost HOCϩήΠϨ έω ΪϨηΎΑϪΘηΪϧ ΗϭΎϔΗϊϣϮΟβϧΎϳέϭϪ ΘϟΎΣέΩϩΩΎϔΘγΩέϮϣ ΎϬϧϮϣίϪΑρϮΑήϣϻΎΑΖϤδϗ Equal Variances Assumed ΪϨηΎΑΕϭΎϔΘϣϊϣϮΟβϧΎϳέϭϪ ΘϟΎΣέΩϩΩΎϔΘγΩέϮϣ ΎϬϧϮϣίϪΑρϮΑήϣϦϴϳΎ ΖϤδϗ Equal Variances Not Assumed έϯσϫαϊϧέϊϧ έω ϨόϣϑϼΘΧϢϫΎΑΎϬϫϭή βϧύϳέϭϫ ΘϟΎΣέΩΎϬδϧΎҨέϭ ήαήαϻύαζϥδϗέω ΩϮη ϣώύψθϧ έύϣϟθϡτηϭϫϳΰπη ήαέdunnettˬduncanˬtukeyϝϭϊθϣϥϯϣίϫγϫϧϯϥϧ ϩϭή ΎϬϫϭή ί ϳ ϥύθϣ ί Ϫ ΩΩ έήϗ ϪΟϮΗ ΩέϮϣ ΪϳΎΑ έ ϪΘ ϧ Ϧϳ Dunnett ϥϯϣί ΩέϮϣ έω ϦϳΪϨΠϨδΑϥΎΑέΎϬϫϭή ήϳύγύηϣϳήθ ϣήψϧέωϊϫύηϝήθϩ ϩϭή ϥϯϩϋϫαέ ϳ ϠϴμΤΗ ϩϭή ϥϯϩϋϫαύϭϫϭή ϦϳίϡΪ ήϫώύψθϧϊηύαlastήχϩϭή ΎϳFirstϝϭϩϭή ϥϯη ϣϩϭή Control ϪϨϳΰ DunnettϥϮϣίϥΩή ϝύόϓύαέύ Ϧϳ ήαϊϩ ϤϧΩΎΠϳ ήθθϐηξϳύθϧέωήχύϳϝϭ ήα ϴϠ ΎΑΪόΑϪϠΣήϣέΩϢϴϨ ϣώύψθϧέϝήθϩ ϩϭή ϥ ϭήαϭέϊαήϣέωϭϩϊηϝύόϓcategory έ ήψϧ ΩέϮϣ ΎϬϴΟϭήΧ ϢϴϧϮΗ ϣ Ok ϭέ ήα ϴϠ ΎΑ ϭ Ϣϳϭέ ϣ ϠΒϗ ϩήπϩ ϪΑ Continue ϭέ ϢϴϨ ϩϊϫύθϣ ˬ Games HowellˬDunnet s T3 ˬTamhane s T ΎϬϧϮϣίΎϬδϧΎҨέϭ ήαήαύϧϧθҩύ ΖϤδϗέΩ ΪϧϮη ϣϩωύϔθγdunnet s C έύϭδϧύϳέϭ ήαήαϫ Hoνήϓή ϳΩϥΎϴΑϪΑˬΪθϧϪΘϓήϳά ΎϬδϧΎϳέϭ Ϩ ϤϫνήϓϥϮ ϝύμϣϧϳέω ϢϴϨ ϣϩωύϔθγ ϨϴҨΎ ΎϬϧϮϣίίΖγϩΪηΩέΪϨ ϣρήτϣ έωϊϩθδϫ ϭύδϣ ΎΒҨήϘΗΎҨ ϭύδϣϫϧϯϥϧ ΎϫϩίΪϧϪ όϗϯϣύηfϩέύϣˬpost hocξҩύθϧέωϫθ ϧ ΞҨΎΘϧϭΖγϥϮΗΪϗΎϓ ϭύδϣύϧϫϧϯϥϧϩίϊϧύα ϟϭϊϩ ϣζϣϭύϙϣΰθϧ ϭύδϣύϧ ΎϫβϧΎҨέϭήΑήΑ ΪϫΩ ϣθτλύϧ ΞҨΎΘϧϦҨϪΑϪ ϢϴΘδϫϝΩϭΩΎϣΪηΎΑ ҨΩΰϧsigˬFϩέΎϣΩέϮϣέΩϭΪϧΩϮΒϧήΑήΑΎϫβϧΎҨέϭή ϮϫΩή ΏΎΨΘϧ ΰϴϧ έ Welch,Brown-Forsythe Ύϫ ϪϨҨΰ option ϪϤ Ω έω άϟ ˮϪϧ ΎҨ ϢϴϨ Α ΩΎϤΘϋ ϢϴϨ ήοέanovaϩέύαϭωϭϩωή ϑάσέεή ήҩωύϙϣϊҩύαϊθҩύηεέϯλέωϣθϩ ϣ γέήα 40

Post Hoc Tests (I) Level of education Multiple Comparisons Dependent Variable: Household income in thousands (J) Level of education Mean Difference (I- J) Std. Error Sig. 95% Confidence Interval Upper Bound Tamhon's Did not complete high school High school degree-m -6.34094.33139.064-1.874.1905 m 1 Lower Bound Some college-m3-10.6837(*).74450.00-17.9587 -.5781 College degree-m4-18.78400(*) 3.01158.000-7.33-10.3447 Post-undergraduate degree-m5-7.30373(*) 5.6659.000-4.19-1.4846 High school degree Did not complete high school-m1 6.34094.33139.064 -.1905 1.874 Some college-m3-3.9743.74970.811-11.6318 3.7769 College degree-m4-1.44306(*) 3.0163.000-0.895-3.9909 m Post-undergraduate degree-m5-0.9679(*) 5.6930.001-35.7894-6.136 Some college Did not complete high school-m1 10.6837(*).74450.00.5781 17.9587 High school degree-m 3.9743.74970.811-3.7769 11.6318 m 3 College degree-m4-8.51563 3.34591.105-17.8907.8594 Post-undergraduate degree-m5-17.03536(*) 5.46465.019-3.4016-1.6691 College degree Did not complete high school-m1 18.78400(*) 3.01158.000 10.3447 7.33 High school degree-m 1.44306(*) 3.0163.000 3.9909 0.895 m 4 Some college-m3 8.51563 3.34591.105 -.8594 17.8907 Post-undergraduate degree-m5-8.51973 5.60355.749-4.704 7.309 m m m m m m m 1 1 1 5 m m 3 4 5 m m m 4 5 3 Post-undergraduate degree Did not complete high school-m1 7.30373(*) 5.6659.000 1.4846 4.19 High school degree-m 0.9679(*) 5.6930.001 6.136 35.7894 Some college-m3 17.03536(*) 5.46465.019 1.6691 3.4016 m m m m m 5 College degree-m4 41 8.51973 5.60355.749-7.309 4.704 1 m ϥϯϣίέύϭ ήϫϫπθθϧ mϭm1ϟμϣζγήαήαϣϫήγζθ ϱύϫϩϭή έωϊϣέωϧθ ϧύθϣ m3 m3ϭm1ϟμϣζδθϧήαήαϫϡλύϓύαϱύϭϫϭή έωϲϟϭ 4 m3 4 m5

(I) Level of education (J) Level of education 4 Mean Difference (I- J) Std. Error Sig. 95% Confidence Interval Upper Bound Dunnett T3 Did not complete high school High school degree-m -6.34094.33139.064-1.871.190 m 1 Lower Bound Some college-m3-10.6837(*).74450.00-17.9585 -.578 College degree-m4-18.78400(*) 3.01158.000-7.30-10.3450 Post-undergraduate degree-m5-7.30373(*) 5.6659.000-4.1194-1.4881 High school degree Did not complete high school-m1 6.34094.33139.064 -.190 1.871 Some college-m3-3.9743.74970.810-11.6317 3.7768 College degree-m4-1.44306(*) 3.0163.000-0.8950-3.9911 m Post-undergraduate degree-m5-0.9679(*) 5.6930.001-35.7859-6.1397 Some college Did not complete high school-m1 10.6837(*).74450.00.578 17.9585 High school degree-m 3.9743.74970.810-3.7768 11.6317 m 3 College degree-m4-8.51563 3.34591.104-17.8905.8593 Post-undergraduate degree-m5-17.03536(*) 5.46465.019-3.3985-1.673 College degree Did not complete high school-m1 18.78400(*) 3.01158.000 10.3450 7.30 High school degree-m 1.44306(*) 3.0163.000 3.9911 0.8950 m 4 Some college-m3 8.51563 3.34591.104 -.8593 17.8905 Post-undergraduate degree-m5-8.51973 5.60355.747-4.675 7.80 Post-undergraduate degree Did not complete high school-m1 7.30373(*) 5.6659.000 1.4881 4.1194 High school degree-m 0.9679(*) 5.6930.001 6.1397 35.7859 Some college-m3 17.03536(*) 5.46465.019 1.673 3.3985 m 5 College degree-m4 8.51973 5.60355.747-7.80 4.675

(I) Level of education (J) Level of education Mean Difference (I- J) Std. Error Sig. 95% Confidence Interval Upper Bound Upper Bound Dunnett C Did not complete high school High school degree-m -6.34094.33139-1.7076.057 m 1 High school degree Some college-m3-10.6837(*).74450-17.7647 -.770 College degree-m4-18.78400(*) 3.01158-7.0099-10.5581 Post-undergraduate degree-m5-7.30373(*) 5.6659-41.7380-1.8695 Did not complete high school-m1 6.34094.33139 -.057 1.7076 m Some college-m3-3.9743.74970-11.4370 3.581 College degree-m4-1.44306(*) 3.0163-0.6810-4.051 Post-undergraduate degree-m5-0.9679(*) 5.6930-35.4039-6.517 Some college Did not complete high school-m1 10.6837(*).74450.770 17.7647 High school degree-m 3.9743.74970-3.581 11.4370 College degree-m4-8.51563 3.34591-17.6548.636 m 3 Post-undergraduate degree-m5-17.03536(*) 5.46465-3.0089 -.0618 College degree Did not complete high school-m1 18.78400(*) 3.01158 10.5581 7.0099 m 4 High school degree-m 1.44306(*) 3.0163 4.051 0.6810 Some college-m3 8.51563 3.34591 -.636 17.6548 Post-undergraduate degree-m5-8.51973 5.60355-3.8715 6.830 Post-undergraduate degree Did not complete high school-m1 7.30373(*) 5.6659 1.8695 41.7380 m 5 * The mean difference is significant at the.05 level. High school degree-m 0.9679(*) 5.6930 6.517 35.4039 Some college-m3 17.03536(*) 5.46465.0618 3.0089 College degree-m4 8.51973 5.60355-6.830 3.8715 43

Games- Howell (I) Level of education Did not complete high school m 1 (J) Level of education High school degree-m Mean Difference (I- J) Std. Error Sig. 95% Confidence Interval Upper Bound Upper Bound -6.34094.33139.051-1.7040.01 Some college-m3-10.6837(*).74450.00-17.760 -.7766 College degree-m4-18.78400(*) 3.01158.000-7.0053-10.567 Post-undergraduate degree-m5-7.30373(*) 5.6659.000-41.798-1.8776 High school degree Did not complete high school-m1 6.34094.33139.051 -.01 1.7040 Some college-m3-3.9743.74970.609-11.4330 3.578 m College degree-m4-1.44306(*) 3.0163.000-0.6770-4.091 Post-undergraduate degree-m5-0.9679(*) 5.6930.001-35.3961-6.595 Some college Did not complete high school-m1 10.6837(*).74450.00.7766 17.760 High school degree-m 3.9743.74970.609-3.578 11.4330 m College degree-m4-8.51563 3.34591.081-17.6488.6175 3 Post-undergraduate degree-m5-17.03536(*) 5.46465.016-31.9958 -.0749 College degree Did not complete high school-m1 18.78400(*) 3.01158.000 10.567 7.0053 High school degree-m 1.44306(*) 3.0163.000 4.091 0.6770 m 4 Some college-m3 8.51563 3.34591.081 -.6175 17.6488 Post-undergraduate degree-m5-8.51973 5.60355.550-3.8554 6.8160 Post-undergraduate degree Did not complete high school-m1 7.30373(*) 5.6659.000 1.8776 41.798 High school degree-m 0.9679(*) 5.6930.001 6.595 35.3961 m 5 Some college-m3 17.03536(*) 5.46465.016.0749 31.9958 College degree-m4 8.51973 5.60355.550-6.8160 3.8554 44

íìæçç±ºnàaä m Ωή ϩωύϔθγωέϊϧύθγήθϐθϣίϥϯθθϣϊϩηύβϧϊσϭϣϫήθϐθϣϭωˬϫδҩύϙϣζϭοή ϪΘ ϧ ϥνήψϣϭήδ ΕέϮλΪΣϭsήΑϢϴδϘΗΎΑϭΩϮη ϣήϔλ ϨΤϨϣΪΒϣίνήϋ x x ϪΒγΎΤϣΎΑ x x z ΩϮΑΪϫϮΧΪΣϭ ΑzΖҨΎϬϧέΩϭϩΪη Ҩ s ϢϴϨ ϣϩωύϔθγcomputeέϯθγωίspssέΰϓϡήϧέωzϫβγύτϣ ήα įźťưřźśěśƴįśʒʊƺưżō ήθϣέύ ΎϧϥϮϣίίϩΩΎϔΘγϞϻΩ ΪηΎΒϧϝΎϣήϧΎϫϩΩΩϊҨίϮΗ ΪηΎΑΩΎҨί ΪϨ ή ϑϼθχ ΪηΎΑϢ ϪϧϮϤϧϩίΪϧ ˮϢϴϨ ϤϧϩΩΎϔΘγ ήθϣέύ ΎϧϥϮϣίίϡίϻςҨήηϦΘδϧΩϥϭΪΑΪΘΑίή ΖγϢ ΪηΎΑϝΎϣήϧΎϫϩΩΩϊҨίϮΗϪ τҩήηέωϥϯϣίϧҩϥϯηήҩί ήϔλνήϓωέ ήαϭζγ ҨΎϋΩνήϓˬϞΑΎϘϣνήϓΎϣΪϨ ϣκψθϣέζθόϥοϊҩίϯηήϔλνήϓ ϢϴϨ ΩέέήϔλνήϓϪϧϮϤϧ ҨϪέΎΑϪ ΖγϦҨϥϮϣίϑΪϫϭΖγϩΪηΎϋΩ ΩέήϔλϪϴοήϓϪ ϢҨϩΩΩϥΎθϧϪϧϮϤϧ ҨΎΑςϘϓϥϮ ΖγϴΤλϞΑΎϘϣνήϓϢϴҨϮ ϤϧΖϗϭ ϴϫ ΖδϴϧϞΑΎϘϣϪϴοήϓΕΎΒΛϞϴϟΩϦҨ ϟϭϊη ϥϯϣί ήαύϣ ϠλϑΪϫΪҨϴϣΖγΪΑϪϧϮϤϧίϭΖγήϔλνήϓΩέ ήαέϊϙϣϧҩήθϥ p value ήαζγϩϊη ϡύπϧ ϩωϯϭθαέύ ϥϯϣίϧҩύαϫϧή ϭζγ H 0 ϪϴοήϓΩέϭ 45 p value ϥϊη Ϯ έ κχύηϭ ϢҨήϴ Α ϟϯϥόϣ ϪϧϮϤϧ ϢΒϨ ΏΎΨΘϧ ΖγέΩ έ Ϫϴοήϓ ϝϭί Ζγ ήθϭαέύ ϨҨ ί ήθ ϮϠΟ ΪϨ ϞϤΤΗΪϧϮΘϴϣϖϘΤϣϪ Ζγ ҨΎτΧϢϫ ϢϴϨ ΏΎΨΘϧέϪϴϘΑϭϪϧϮϤϧϢΠΣΪόΑϢϴϨ ΝήΨΘγ ϥϯϣίϥϯη ϢϴϤμΗ ϢҨ ϩωϯα ΎτΧ ϥϭϊα ΩϮη ςϡϗ Ϣϫ ϪϧϮϤϧ ϥϯϣί ςγϯη ϭ ΪηΎΑ ςϡϗ ήϔλ νήϓ Ύόϗϭ ή ϢҨϩΪηΎτΧέΎ ΩΕέϮμϨҨήϴϏέΩϪϧή ϭζγέω ΖγϩΩϮΑϴΤλΖϴόϤΟέΩήϔλνήϓϪ σήηϫαϫϧϯϥϧςγϯηήϔλνήϓωέϝϭωϯϧ ΎτΧ ΖγϩΩϮΑςϠϏΖϴόϤΟέΩήϔλνήϓϪ σήηϫαϫϧϯϥϧςγϯηήϔλνήϓϝϯβϗϡϭωωϯϧ ΎτΧ 1 ϥύϩθϥστγ ϴΤλ H 0 ςϡϗ H 0 H 1 ϥϯϣίϥϯη 0 ΩέϡΪϋ H 0 Ωέ ϝϭωϯϧ ΎτΧϭ έω Ϩόϣτγ 1 ϡϭωωϯϧ ΎτΧ 1 ϥ ΎΗ Ϧϴ ϧύθϣϣҩήθ ΑϪϧϮϤϧ ή ϨόҨ ϥύϩθϥστγ ΪΘϓ ϣϥύϩθϥσϫϡλύϓέω x έω ϭ ηΰ έωϊϩηύα ϪΘηΩ ήθϥ ϝϭ ωϯϧ ΎτΧ Ϫ ϢϴϨ ϩωύϔθγ ΪҨΎΑ ҨΎϬϧϮϣί ί ΘόϨλ ϥϥωϯαςϡϗρήηϫαήϔλνήϓωέ ϨόҨΪηΎΑϦҨήΘθϴΑϥ1 Ϫ Ζγ ϧϯϣίϥϯϣίϧҩήηϥϯηή ŶǀƴĩƢǀƤŰţ 1ƶƐŝřŹƵŹŚŝŹŵƩřƺŘſ

47 ϢϴϨ ϣϩωύϔθγήҩίέϯθγωίspssέω ήθϣέύ Ύϧ ΎϫϥϮϣίϡΎΠϧ ήα Analyze Nonparametric Tests 1-sample k-s Ϫ ΪϫΪϴϣϥΎθϧKolmogorov- Smirnov ZϑϮϧήϴϤγ±ϑϭή ϮϤϟϮ εϭέίϩωύϔθγύαϥϯϣίϧҩ ҨΎϤϧΎҨϥϮγϮ ˬΖΧϮϨ ҨˬϝΎϣήϧˮΖγ ϋϯϧϫ ϩϊηώύψθϧήθϐθϣϊҩίϯη ϪΒΗέϑϼΘΧϭΩέϭ ϣζγϊαέϫϧύθϣˬϊϩ ϣϩωύϔθγϫϧύθϣϭύϫϩωω ΪϨΑϪΒΗέί ήθϣέύ ΎϧϥϮϣί Most έω ϥϯϣί ϪΠϴΘϧ έω ΪηΎΑ ϪΘηΩ ϑϼθχ ϠϴΧ ή ϭϭ ΪϨ ϣ ϪΒγΎΤϣ Ύϫ ϩωω ΎΑ έ ϪϧΎϴϣ ϑϼθχέϊϙϣϭωϧҩήθ έΰαϖϡτϣέϊϙϣabsoluteϥϯϩϋύαϑϼθχέϊϙϣϧθϟϭϊδҩϯϧ ϣextreme ΩέΩϡίϻΪηΎΑ ϣϥήҩίέωϩϊη Ύ ήҩί έϯθγω ίϊηύβϧ ϝύϣήϧ ϊҩίϯηύҩ Ϣ ϪϧϮϤϧ ΩΪόΗ Θϗϭ ϪϧϮϤϧ ϭω ϦϴΑ Ϧϴ ϧύθϣ ϪδҨΎϘϣ ϥϯϣί ήα ϢϴϨ ϣϩωύϔθγ Analyze Nonparametric Tests Independent samples ϥϯϣίωϯϧ Kolmogorov- Smirnov ZϑϮϧήϴϤγ±ϑϭή ϮϤϟϮ έωϫδҩύϙϣ ήαΰθϧέϊҩίϯηϟ η ΰ ήϣκχύηήαϩϭϼϋ ΪηΎΒϧϪϴΒηϪϧϮϤϧϭΩήϫϊҨίϮΗϪ ΗέϮλέΩΩήϴ ϣήψϧ ΖγήΘΒγΎϨϣϩέΎϣϦҨ Man-Whitney U ϨΘҨϭϦϣ ΎΑ ϭ Ζγ ΐγΎϨϣ ΰ ήϣ Ύϫ κχύη ϪδҨΎϘϣ ήα ΪϨ ϣώύδσέϩέύϣύϫϫβηέϊϥοίϩωύϔθγ Moses extreme reactionsαίϯϣ ϩϭή ϦϴΑϪδҨΎϘϣϭΩέΩΪϴ ΎΗΎϬϧϪΒΗέϭ ҨΎϬΘϧέΪϘϣήΑ ΪϫΩ ϣϡύπϧ ҨΎϬΘϧέΪϘϣ ϭέέζҩύϣίϭϊϫύη Wald wolfowitz runsϊϟϭ ΪϨ ϣϫδҩύϙϣέϊҩίϯηϟ ηϑϭή ϮϤϟϮ ΪϨϧΎϣϭΪϨ ϣϩωύϔθγρϯϡψϣ ΎϫϪΒΗέί ϢϴϨ ϣϩωύϔθγήҩίέϯθγωίϩϭή ΪϨ ϦϴΑϦϴ ϧύθϣϫδҩύϙϣϥϯϣί ήα Analyze Nonparametric Tests K Independent samples ΎϫϪϧϮϤϧή ϟϭϊϧωϯαϟϙθδϣύϫϫϧϯϥϧύπϧέω ϟϭωϯαϣϫϝύϣήϧζϟύσέωύϫϥϯϣίϧҩϫαύθϣ ϞϘΘδϣ ΎϫϪϧϮϤϧήϔϧϭΩ ήαϭέωήθλύηζҩύϣί ϼΜϣϢϴϨ ϣϩωύϔθγήҩίεέϯθγωίϊϩηύαςβηήϣ ΪϧήҨά ϣήθλύηϣϫίϭζγςβηήϣϭέωίϊόαϭϟβϗήϔϧ Ҩ ήαύϣζγ

48 ΎϫΖϔΟϦϴ ϧύθϣϫδҩύϙϣϥϯϣί Analyze Nonparametric Tests Related samples ϥϯϣίωϯϧ Ύϫ ϩωω Ϫ Ζγ Θϗϭ Wilcoxon ϥϯϣί ϦҨ έω ϭ ϥύϣί ϞΜϣ ΪϨηΎΑ ϪΘγϮϴ Ύϫ ϪΒΗέ ϑϼθχ ΖϬΟ ϭ Ύϫ ϪΒΗέ ϑϼθχ ϦҨ ί ϩωύϔθγ ΩϮη ϣ ϪΘϓή ήψϧέω ΖγΟέϥϮϣί Ζ ϬΟς Ϙϓ ϟϭζγ ҨϻΎΑϞΜϣSign ΩϮη ϣϫθϓή ήψϧέωύϫϫβηέϑϼθχ Ζ γέωεέϯ λϫαύϫ ϩωωή McNemar Ϧ ҨίΖ γή ΘϬΑ ϭ ΪϨΘδ ϫς ϠϏϭ ϥϯϣίϧϳίϻϯϥόϣϊθϩϛϩωύϔθγϥϯϣί ΩϮηϲϣϪΘϓή έύϛϫαέύθϓέϛϳίϊόαϭϟβϗϫδϳύϙϣϱήα ϩϭή ˬΎ ϫϩωωϊ ϴϨϛϩΩΎϔΘ γεϭέϧ ϳίMcNemarεϭέίϲ ϤϴϤόΗΪϨηΎΑϲϳΎΗΪϨ ϱύϫϩωωή ΪϨΘδϫϱΪϨΑ ϲϳύηϊϩ ϱύϫϫϧϯϥϧϧθ ϧύθϣϫδϳύϙϣϥϯϣί Analyze Nonparametric Tests K Related samples ϥϯϣίωϯϧ ϱήθϣέύ ϱύϭηϭέ ΪϨϧΎϣ ϦϣΪϳήϓ ϥϯϣί έ ήθϐθϣ ΪϨ ϊϳίϯη Ϧϴ ϧύθϣ ϪδϳΎϘϣ ϱέά ϪΒΗέ ί ϩωύϔθγ ΎΑ ΪϨϛϲϣ ϥϯϣί ϥϯϣί ΎϫϪΒΗέ ϪδϳΎϘϣ ϭ ϩϭή ήϫ ϱήα ΩϮηϲϣϡΎΠϧ ϖϓϯη ί ϩωύϔθγ ΎΑ ϝϊϩϛ ϥϯϣί ϡύπϧ έ ϥϯϣί Ϧϳ ϱϫβηέ ϲ ΘδΒϤϫ ΪϫΩϲϣ ϦϣΪϳήϓ ϥϯϣί ΪϨϧΎϣ ΰϴϧ ϥήϛύϛ ϥϯϣί ϭ ϲϳύηϭω ϱύϫϩωω ΎΑ ϲϟϭ ϩωήϛ ϞϤϋ ϪδϳΎϘϣ ϱήα ΪϫΩϲϣ ϡύπϧ έ ΕΎΒγΎΤϣ ΖγΐγΎϨϣςϠϏϭΖγέΩ

ΖΒΛϪϴϧΎΛΐδΣήΑΪϧέά ΑΩϮΧ ΎΟέΩέΐγΎϨϣϪότϗΎΗΪϨϨ ϴϣϑήλϪ Α Ϫ έ ϧύϣίϝύμϣ ŦƬŨƯ ƵźƿřŵƖŝźƯ ççå èååçëå çêåçîåèåå çëåçíåçîå çèå èéåæîå æîå èååçêå ççåçìåçéå çêå èçåçìå çíå çîåçëå çìå èéåçêå çéå èååçêå ϢҨϩΩή ΪϴҨΎϤϧϪΒγΎΤϣέ ΎϴηκϴΨθΗϥΎϣί ήα ϔϴλϮΗ ΎϫέΎϣ ΪϨ ή ϭΰ ήϥη ΎϫέΎϴόϣ ΪϴϨ ϢγέϥΎϣίϪγϦҨ ήα ϪδҨΎϘϣέΩϮϤϧ Ҩ ˮΖγϝΎϣήϧϊҨίϮΗ έωϟ ηϫγήϫ ήακθψθηϥύϣίύҩ ϥύϣί Ϧϴ ϧύθϣ ϦϴΑ ΪϴϫΩ ϥύθϧ Boxplot έωϯϥϧ ί ϩωύϔθγ ΎΑ ΩέΩΩϮΟϭ έω ϨόϣϑϼΘΧϩϭή ϪγϦҨέΩκϴΨθΗ ϭ ήθϣέύ ΪϴϨ ϥϯϣίέϻύανήϓ έύϣ ΎϫϥϮϣίίϩΩΎϔΘγΎΑ ήθϣέύ Ύϧ ΏϮΟ ΩέΩΩϮΟϭϩέϭΩΎϫήϴϐΘϣΖϴϴόΗϭΎϫϩΩΩΖΒΛ ήα έωϫ ϥύϣί ϣϭωϭκϡμϣˬϩήҩωˬϊαήϣϟϣύηϟ ηήθϐθϣ ϟϭϣҩήθ ΑήψϧέΩήϴϐΘϣϭΩϝϭΖϟΎΣ ϢҨέΩΩέϮϣ ΕέϮμϨҨ ϢҨέΩΩέϮϣ ΖϟΎΣϦҨέΩϪ ΚϠΜϣˬϩήҨΩˬϊΑήϣϢҨήϴ ΑήψϧέΩήϴϐΘϣϪγϡϭΩΖϟΎΣ Ωή ϩωύϔθγϥϯη ϣϩέϭωί ϔϴλϮΗ ΎϫέΎϣ ήα Analyze Descriptive statistic- descriptive Analyze Descriptive statistic- Explore objects ΖϤδϗ Ϫγ έω έ ϥύϣί ήθϐθϣ ϥϯθθϣ Explore ί ϩωύϔθγ ΎΑ Ϫ Ζγ ϦҨ ϩέϭω ϦҨ ϕήϓ ΩϮϤϧϩΪϫΎθϣϪϧΎ ΪΟ Descriptive Statistics N Range Minimum Maximum Mean Std. Deviation Variance square 10 110.00 190.00 300.00 56.0000 9.88868 893.333 triangle 10 90.00 190.00 80.00 41.0000 6.85351 71.111 circle 10 70.00 70.00 340.00 303.0000 3.59378 556.667 Valid N (listwise) 10 49

: ϪδҨΎϘϣέΩϮϤϧ Graph Legacy Dialogs- Bar simple - Define other statistic- variable time 400.00 Category Axis objects 300.00 Mean time 00.00 100.00 0.00 square circle triangle ϝύϣήϧϊҩίϯηωϯοϭ Graph Legacy Dialogs - Histogram-display normal curve- variable...time Panel...objects 3 4 Frequency 3 1 0 150.00 00.00 50.00 300.00 350.00 Frequency 1 0 3 1 0 3 Mean =67.4 Std. Dev. =37.881 N =9 1 square circle triangle 0 150.00 00.00 50.00 300.00 350.00 50

Analyze Nonparametric Tests 1-sample k-s ΪϨΑϩϭή ϥϭϊαϝϭζϟύσϒϟ One-Sample Kolmogorov-Smirnov Test time N 9 Mean 67.414 Normal Parameters(a,b) Std. Deviation 37.88107 Most Extreme Absolute.090 Differences Positive.090 Negative -.083 Kolmogorov-Smirnov Z.485 Asymp. Sig. (-tailed).973 a Test distribution is Normal.b Calculated from data. ϪΠϴΘϧ ΩϮη ϤϧΩέϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ H 0 νήϓˬ ί p value ϥωϯαήθ έΰαϫαϫοϯηύα Sig = p value 0.973! 0.05 = Data- split files variable.objects Analyze Nonparametric Tests 1-sample k-s One-Sample Kolmogorov-Smirnov Test objects square circle Triangle time N 10 Mean 56.6667 Normal Parameters(a,b) Std. Deviation 31.678 Most Extreme Absolute.194 Differences Positive.15 Negative -.194 Kolmogorov-Smirnov Z.655 Asymp. Sig. (-tailed).784 N 10 Mean 303.0000 Normal Parameters(a,b) Std. Deviation 3.59378 Most Extreme Absolute.51 Differences Positive.51 Negative -.14 Kolmogorov-Smirnov Z.79 Asymp. Sig. (-tailed).556 N 10 Normal Parameters(a,b) Mean 41.0000 Std. Deviation 6.85351 Most Extreme Absolute Differences.131 ΪϨΑϩϭή ΎΑϡϭΩΖϟΎΣΏ Positive.083 Negative -.131 Kolmogorov-Smirnov Z.415 Asymp. Sig. (-tailed).995 a Test distribution is Normal. b Calculated from data. 51

ϪΠϴΘϧ ϊαήϣϩϭή έωωϯη ϤϧΩέϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ H 0 νήϓˬ ί p value ϥωϯαήθ έΰαϫαϫοϯηύα Sig = p value 0.784! 0.05 = ϩήҩωϩϭή έωωϯη ϤϧΩέϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ H 0 νήϓˬ ί Sig = p value 0.556! 0.05 = ΚϠΜϣϩϭή έωωϯη ϤϧΩέϥΎϣίήϴϐΘϣϊҨίϮΗϥΩϮΑϝΎϣήϧ H 0 νήϓˬ ί Sig = p value 0.995! 0.05 = time square circle 5 triangle p value ϥωϯαήθ έΰαϫαϫοϯηύα p value ϥωϯαήθ έΰαϫαϫοϯηύα BoxplotέΩϮϤϧΎΑϩϭή ϪγέΩϥΎϣίϦϴ ϧύθϣϑϼθχ ΪηΎΒϧsplit filesζϟύσέωϫθ ϧ Graph Legacy Dialogs Boxplots - variable...time category.objects 350.00 300.00 50.00 00.00 5 150.00 ϓΎ έωϯϥϧ ΎΑ ΕϭΎπϗ ϟϭ ΪϫΪϴϣ ϥύθϧ ΕϭΎϔΘϣ ϝύ η ϩϭή Ϫγ έω ϥύϣί ήθϐθϣ Ϧϴ ϧύθϣ ϪΠϴΘϧ Ωή ϥϯϣίϊҩύαζδθϧ

H 0 : m m m 1 3 H 1 : m m m 1 3 ϥϯϣί ήθϣέύ ΎϧϝϭΖϟΎΣ SquareϊΑήϣϩϭή έωϥύϣί ήθϐθϣϧθ ϧύθϣ m 1 circleϩήҩωϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m 53 TriangleΚϠΜϣϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m 3 Analyze Nonparametric Tests K Independent samples- test variable... time Grouping variable. object(1,3) Kruskal-Wallis Test Ranks time objects N Mean Rank square 10 13.0 circle 10 4.40 triangle 10 8.90 Total 30 Test Statistics(a,b) a Kruskal Wallis Test b Grouping Variable: objects ϪΠϴΘϧ ΩϮη ϣωέϥύϣίήθϐθϣϊҩίϯηϥωϯαϝύϣήϧ H 0 νήϓˬ ί p value ϥωϯαήθ Ϯ ϪΑϪΟϮΗΎΑ value 0.05 = Sig = p 0.000 Analyze - time Compare Means - One-Way Anova ANOVA Sum of Squares df Mean Square F Sig. Between Groups 070.000 10360.000 14.337.000 Within Groups 19510.000 7 7.593 Total 4030.000 9 time Test of Homogeneity of Variances Levene Statistic df1 df Sig..067 7.936 time Chi-Square 16.683 df Asymp. Sig..000 ήθϣέύ ϡϭωζϟύσ ϪΠϴΘϧ ΩϮη ϣωέϥύϣίήθϐθϣϊҩίϯηϥωϯαϝύϣήϧ H 0 νήϓˬ ί p value ϥωϯαήθ Ϯ ϪΑϪΟϮΗΎΑ Sig = p value 0.000 0.05 = ΖγΎϴη ΪϨΑϩϭή ί ηύϧϭζγέω ϨόϣϑϼΘΧ Ϫ ΪϫΪϴϣ ϥύθϧ Test of Homogeneity of Variances ϝϭϊο έω ΎϬδϧΎҨέϭ ϪδҨΎϘϣ ϥϯϟ ϥϯϣί ί ϩϊϣ ΖγΪΑ ΞҨΎΘϧ p value 0.936 = Sig! ϭϊϧέϊϧ έω ϨόϣϑϼΘΧΎϴηήϴϐΘϣϩϭή έωϥύϣίήθϐθϣ ΎϫβϧΎҨέϭ ήαήανήϓύαύϭϩθ ϧύθϣύϭϓϼθχϝύβϧωϫαϊҩύαϫ ΪϫΪϴϣϥΎθϧANOVAϝϭΪΟέΩβϧΎҨέϭΰϴϟΎϧίϩΪϣΖγΪΑΞҨΎΘϧ ϢϴϨ ϪδҨΎϘϣϢϫΎΑϭΩϪΑϭΩPost HOCϪϨҨΰ ίϩωύϔθγύαϭϣθηύαύϭδϧύҩέϭ

Dependent Variable: time Tukey HSD ϢϴϨ ϣϩωύϔθγposthoc έϯθγωίϑϼθχζϡϋϧθϓύҩ ήα Multiple Comparisons. (I) objects square circle triangle (J) objects circle triangle square triangle square circle *. The mean difference is significant at the.05 level. Mean Difference 95% Confidence Interval (I-J) Std. Error Sig. Lower Bound Upper Bound -46.00000* 1.0159.00-75.8065-16.1935 16.00000 1.0159.391-13.8065 45.8065 46.00000* 1.0159.00 16.1935 75.8065 6.00000* 1.0159.000 3.1935 91.8065-16.00000 1.0159.391-45.8065 13.8065-6.00000* 1.0159.000-91.8065-3.1935 m m m m 1 3 m 1 m 3 ϭωϫαϭωϫδҩύϙϣϥϯϣίϫπθθϧ ΖδϴϧήΑήΑϩήҨΩϭϊΑήϣ Ύϫϩϭή έωϥύϣίϧθ ϧύθϣ ΖδϴϧήΑήΑϩήҨΩϭΚϠΜϣ Ύϫϩϭή έωϥύϣίϧθ ϧύθϣ ΖγήΑήΑΚϠΜϣϭϊΑήϣ Ύϫϩϭή έωςϙϓϥύϣίϧθ ϧύθϣ ΪηΎΑ ϣϩήҩωϩϭή ϑϼθχζϡϋ SquareϊΑήϣϩϭή έωϥύϣί ήθϐθϣϧθ ϧύθϣ m 1 circleϩήҩωϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m TriangleΚϠΜϣϩϭή έωϊϣέωήθϐθϣϧθ ϧύθϣ m 3 54

( O ) i Ei E H 0 : i íìæçî±ºä m 55 Chi-squareƱƺƯŻō ΩϭήϴϣέΎ ϪΑέϮψϨϣϭΩ ήαϥϯϣίϧҩ ˮϪϧΎҨΪϨ ϣ ϭήθ λύχϊҩίϯηίέωϯϥϧϊҩίϯηϖαύτη ϪΘδδ ΎϫήϴϐΘϣ ϓΩΎμΗήϴϐΘϣϭΩϝϼϘΘγ ϩϊηϩϊϫύθϣήҩωύϙϣ O i έύψθϧωέϯϣήҩωύϙϣ E i έύψθϧωέϯϣήҩωύϙϣϫϧϯϥϧ ϮγίϩΪηϩΪϫΎθϣϑϼΘΧςγϮΘϣ ΪϨ ϣϊθҩύηέζθόϥοήθϣέύ ϪϧϮϤϧ ΪϨ ϤϧΪϴҨΎΗέΖϴόϤΟήΘϣέΎ ϪϧϮϤϧ H 1 : ΖϴόϤΟϪϧϮϤϧ έϊϙϣϥωϯαϣ ϭωϯη ϣϣ ΕΎϓϼΘΧβ ΖηΩέΖϴόϤΟϊҨίϮΗϥΎϤϫϪϧϮϤϧή ΩϮη ϣϫδҩύϙϣ έύϣϝϭϊούαϥέϊϙϣϫθβϟϊϩ ϣϊθҩύηέ είήαϲϳϯϝθϧ±ϝϼϙθγϥϯϣί ΎϬοήϓ ϲϓωύμηϫϧϯϥϧ ± ΩέΪϧΖϴϤϫΎϫϩΩΩϊϳίϮΗΎϳϞϜη ± ϞϗΪΣϩϭή ήϫέωϲϧϭήϓϟϗϊσ ± ΪϨηΎΑϪΘηΩ ίζθαϲϧϭήϓύϫϩϭή ± ϲϣϫδϳύϙϣέύψθϧωέϯϣήϳωύϙϣύαέϩϊηϩϊϫύθϣήϳωύϙϣ ϲχϩέύϣίϩωύϔθγύαϥϯϣίϧϳ ΪϨϛ ϥϯϣίϫαϲγήθγωεϭέ - Descriptive Statistics - Crosstabs...... Analyze ΪϴϨϛΏΎΨΘϧέchi squareϫϩϳΰ ϭϊθϧΰαέstatisticϫϥϛωύϫήθϐθϣ ϦϴϴόΗίΪόΑ Analyze nonparametric chi-squareύҩ

56 ΖγήΑήΑ ή ҨΩΎΑΎϫϩΩΩί ҨήϫϩΪϫΎθϣϝΎϤΘΣϭΖγΖΧϮϨ ҨϊҨίϮΗΪϴϨ νήϓϝύμϣ ϢҨέΩ ίύαώύβγϩϭή ˮϪϧΎҨΖγϥΎδ ҨΎϬϨҨϥΪҨήΧϪΑΎϫϪ ΑϞҨΎϤΗϢϴϧΪΑϢϴϫϮΧ ϣϝϭϝϯόγ ΩϮηϡΎΠϧϝϭϩϭή ϪγϦϴΑϪδҨΎϘϣϢϴϫϮΧ ϣ±ϡϭωϝϯόγ ˮΖγϡϮγϭΩϪΑϡϮγ ҨήΗϮϴ ϣύ ίύαϭ ϨϔΗϦϴΑΪҨήΧΖΒδϧΎҨϡϮγϝϮΌγ ίύαώύβγωϯϧ ϩϊη έϊҩήχέϊϙϣ ϨϔΗ ήηϯθ ϣύ ίύα γϭήϋ ϦϴηΎϣ ϪΧή Ϫγ ϝϭώϯο ϢϴϨ ϣϒҩήόηnumϭtypeoftoyϡύϧϫαήθϐθϣωϯϧϭω ϢϴϫΩ ϣϊ ίύαώύβγωϯϧ έωέϝϭήθϐθϣ ϢϴϫΩ ϣϥίϭnumήθϐθϣϫα Analyze nonparametric chi-square Test variable Typeoftoy ΖγϩΪη ΩΪϋChi-Square(a)έΪϘϣ typeoftoy Observed N Expected N Residual gun 45 0.0 5.0 camputer 0 0.0.0 doll 15 0.0-5.0 car 15 0.0-5.0 bycicle 5 0.0-15.0 Total 100 Ζγ0.05ίήΘ Ϯ ϭasymp. Sig.=0.000 έϊϙϣ Ζδϴϧ ҨΖϴόϤΟήΘϣέΎ ΎΑϪϧϮϤϧϊҨίϮΗϭΪϨ ϣωέήϔλνήϓϫϧϯϥϧάϟ Test Statistics typeoftoy Chi-Square(a) 45.000 df 4 Asymp. Sig..000 ( O ) i Ei 5 0 5 5 15 900 45 E 0 0 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 0.0. ϡϭωώϯο ήαˬused specified rangeϫϩҩΰ ˬExpected Range ΖϤδϗέΩChi-SquareϥϮϣίϩήΠϨ έω Ωήϴ ΕέϮλ ΎΗ ϩϭή ϦϴΑϪδҨΎϘϣΎΗϩΩή ΏΎΨΘϧέupper=3 ήαϭlower=1 ϢϴϨ ϥύθβηήϣϝϭ ΘδҨΎΑΩϮΒϧΐΗήϣΎϣήτϧΩέϮϣ ΎϬϫϭή ή ΩΩϡΎΠϧέϥϮϣίΪόΑΩή ΏΎΨΘϧϝϭselect casesέϯθγωύαέύϫϩωωϥϯθθϣ ΪηΩέϪϧϮϤϧςγϮΗήϔλνήϓϥΎϨ ϤϫϥϮϣίϦҨϡΎΠϧΎΑ i

Frequencies typeoftoy Category Observed N Expected N Residual 1 gun 45 6.7 18.3 camputer 0 6.7-6.7 3 doll 15 6.7-11.7 Total 80 Test Statistics typeoftoy Chi- Square(a) 19.375 df Asymp. Sig..000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 6.7. ϡϯγώϯο ήηϯθ ϣύ ίύαϭ ϨϔΗΪҨήΧωϮϤΠϣϪ ϢϴϨ ϣώύδσέϯτϩҩέϡϯγϭωϫαϡϯγ ҨΖΒδϧ 1 65* 1.7 3 65* 43.3 3 ϢϴϨ ϣϣθδϙηϡϯγϭωϫαϡϯγ ҨΖΒδϧϪΑέΩΪϋϦҨΖγ ˬExpected Range ΖϤδϗέΩChi-SquareϥϮϣίϩήΠϨ έω ήαϭlower=1 ήαˬused specified rangeϫϩҩΰ Ωήϴ ΕέϮλ, ϩϭή ϦϴΑϪδҨΎϘϣΎΗϩΩή ΏΎΨΘϧέupper= ΩΪϋExpected Value ΖϤδϗέΩChi-SquareϥϮϣίϩήΠϨ έω ϢϴϨ ϣωέϭέ ϭ Frequencies typeoftoy Category Observed N Expected N Residual 1 gun 45 1.7 3.3 camputer 0 43.3-3.3 Total 65 Test Statistics Ζγ0.05ίήΘ έΰαϭ Asymp. Sig.=0.655 έϊϙϣ ΪϨ ϤϧΩέήϔλνήϓϪϧϮϤϧάϟ typeoftoy Chi-Square(a).00 df 1 Asymp. Sig..655 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 1.7. 57

ΖγϩΪηϩΩέϭϡΎϧΖΒΛέΎϣϩΪϜθϧΩϚϴϜϔΗϪΑϥΎΘγήϬηϭϥΎΘγΰϛήϣϱΎϬϫΎ θϧωέωϝύμϣ ϨόҨϪϧΎҨΩέΩΖΧϮϨ ҨϊҨίϮΗ ϟϯβϗζβδϧˮϫϧύҩωέωήθλύηϯπθϧωώύψθϧέωϩϊ θϧωωϯϧύҩ ϩϊ θϧω ϦϴΑ ϟϯβϗ ΖΒδϧ ΎҨ ˮΪϧϮη ϣ ϢϴδϘΗ ΎϬϫΎ θϧω ϦϴΑ ΖΒδϧ Ҩ ϪΑ ϟϯβϗ ΩΪόΗ ΏΎΨΘϧ ϥύϳϯπθϧω ςγϯη ήϩϫ ϭ ϲγϊϩϭϣ ϲϩόϳ ˮΖγ ϪΑ ήϩϫ ϭ γϊϩϭϣ ΩϮηϲϣ ϩϊ θϧωωϯϧ ϲγϊϩϭϣ ήϩϫ ΩΎμΘϗ ήϳύγ ϥύθγΰϛήϣ ϥύθγήϭη 1 Name faculty place frequency Type Numeric Numeric Numeric Width 8 8 8 Decimal - - - Label 58 Value Missing - - - column 10 10 10 ΎϫήϴϐΘϣΏΎΨΘϧ Align left left left Measure ϢϴϫΩ ϣϥίϭfrequencyήθϐθϣϫα ϢϴϨ ϣϩωύϔθγ chi-squareϥϯϣίίζχϯϩ ҨϊҨίϮΗϥϮϣί ήα Analyze nonparametric chi-square Test variable faculty ΪϨ ϣϥϯϣίέϥϊҩίϯηϭϊϧί ϣϊϥοέύϫ ϟϯβϗωϊόηεωϯοωϯη ϣϩϊϫύθϣϫ έϯτϧύϥϫ ϣϊθҩύηέζθόϥοήθϣέύ ϪϧϮϤϧΖϔ ϥϯη ϣϭϊϩ ϤϧΩέέήϔλνήϓϪϧϮϤϧϪ ΖγϦҨϪΠϴΘϧ ΪϨ facaulty Observed N Expected N Residual engineering 30 3.5 6.5 art 0 3.5-3.5 ecoomic 3 3.5 -.5 etc 1 3.5 -.5 Total 94 Test Statistics - - 1=eng =art 3=eco 4=etc 1=center =outer - - facaulty Chi- Square(a).596 df 3 Asymp. Sig..458 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 3.5. Scale Scale Scale

50*0.8 40 50*0. 10 ϪΑ ΖΒδϧϥϮϣί ήα Chi-SquareϥϮϣίϩήΠϨ έω 1,ˬExpected Range ΖϤδϗέΩ ΩΪϋExpected Value ΖϤδϗέΩ ϢϴϨ Ωέϭέ ΩϮΧ ϨόҨΪλέΩϪΑέΎϬϧΖΒδϧΎҨϢϴϨ ϣωέϭέ ϭ Frequencies 1 Category Observed N facaulty Expected N Residual engineering 30 40.0-10.0 art 0 10.0 10.0 Total 50 Test Statistics facaulty Chi-Square(a) 1.500 df 1 Asymp. Sig..000 a 0 cells (.0%) have expected frequencies less than 5. The minimum expected cell frequency is 10.0. ΪηΩέϪϧϮϤϧςγϮΗνήϓϦҨϪ ϨҨϪΠϴΘϧ įřƶƭưūƹŵśƿbinomialʊƺưżō ϪϠϤΟϭΩ ϓΩΎμΗήϴϐΘϣϒҨήόΗ ΪηΎΑϪΘηΩ ΗϭΎϔΘϣΞҨΎΘϧΩϮηέή ΗΎϫέΎΑϥΎδ ҨςҨήηέΩή Ϫ Ζγ θҩύϣί ϓΩΎμΗζҨΎϣί ˮϪϧΎҨΩϮη ϣϊθҩύηϫϧϯϥϧςγϯηζθόϥο ϭέίϩϊηϩωίαϊσp ΩΪΧέϡΪϋΎϳΩΪΧέϪϛϲϳΎΠϧίΩϮηϲϣϩΪϴΠϨγΪϣζϴ ϚϳΩΪΧέϝΎϤΘΣΖΒδϧϥϮϣίϦϳέΩ ΩϮηϲϣϩΩΎϔΘγϥϮϣίϱήΑϊϳίϮΗϦϳίBinomialϱϪϠϤΟ ϭωϊϳίϯηϊϣζθ ϥϯϣίϧϳϫαϲγήθγωεϭέ Analyze - Nonparametric Tests - Binomial... H 0 : p 1 H 1 : p 1 ˮΖγ ϪΑ ΎϬϧΎΘγήϬηϭϥΎΘγΰ ήϣέωϥύҩϯπθϧω ϟϯβϗζβδϧύҩϝύμϣ Analyze - Nonparametric Tests - Binomial... variable...place Test propertion.0.5 59

Binomial Test place Category N Observed Prop. Test Prop. Asymp. Sig. (-tailed) Group 1 center 56.60.50.079(a) Group outer 38.40 Total 94 1.00 a Based on Z Approximation. ΪϨ ϤϧΩέέήϔλνήϓϪϧϮϤϧϪΠϴΘϧ Chi-SquareϥϮϣίίϩΩΎϔΘγή ҨΩϩέ 1,ˬExpected Range ΖϤδϗέΩ ϢϴϨ ϣωέϭέ 0.5ϭ0.5 ΩΪϋExpected Value ΖϤδϗέΩ ΖγήΗΩϭΪΤϣ ϪϠϤΟϭΩ ϟϭωέω ήθθθαζθϣϯϥϋchi-squareϥϯϣίϫθ ϧ ΖΒδϧϪΑήҨΎγϭΩΎμΘϗ ΎϬϫϭή ΎΑήϨϫϭ γϊϩϭϣ ΎϬϫϭή ϥύҩϯπθϧω ϟϯβϗζβδϧύҩϝύμϣ ˮϪϧΎҨΖδϫ ϪΑ H 0 : p ΪϨ ϣώύψθϧέήϩϫϭ γϊϩϭϣϯπθϧω H 1 : q ΪϨ ϣώύψθϧέήҩύγϭωύμθϗϯπθϧω Analyze - Nonparametric Tests - Binomial... variable...place Test propertion.0.7 Cutpoint. ΪϨ ΏΎδΣή ҨΩϩϭή Ҩέ ίϊόαϭϩϭή Ҩέ ϭ ϩϭή ϨόҨΖγcutpoint= Θϗϭ Binomial Test facaulty Category N Observed Prop. Test Prop. Asymp. Sig. (1-tailed) Group 1 <= 50.5.7.000(a,b) Group > 44.5 Total 94 1.0 a Alternative hypothesis states that the proportion of cases in the first group <.7. b Based on Z Approximation. ΪϨ ϣωέέήϔλνήϓϫϧϯϥϧϫ ϨҨϪΠϴΘϧ 60 RunsƱƺƯŻō ΩϮηϲϣϥϮϣίΎϫ ϩωωϥωϯαϲϓωύμηϥύϝϣύϫϩωωϱϊϩαϫβηέϭϥϯϣίϧϳίϩωύϔθγύα ίήηϧθҩύ ҨϭϦϴ ϧύθϣ ϻύα Ҩ ϟϯθϣϩωωϭωϫ ΩϮη ϣϟλύσ ϧύϣίύϫϩωωϥωϯα ϓΩΎμΗ ΪηΎΑϥ ϥϯϣίϧϳϫαϲγήθγωεϭέ Analyze Nonparametric Tests Runs ϦϳϪϛΪϴϨϛϥϮϣίspssΕΎϧΎϜϣΎΑϭΪϴϨϛΩΎΠϳϲϓΩΎμΗΩΪϋ Rand()ϊΑΎΗϞδϛίϩΩΎϔΘγΎΑ ΪϨΘδϫϲϓΩΎμΗΩΪϋ έω data ϲηύϋϼσ ΪϨγ ϥωήϛ ίύα ϡύ Ϩϫ έω Ζγ ϲϓύϛ ΪϴϨϛ έϭήϣ έ Ϟδϛ ί ΕΎϋϼσ Ωϭέϭ ϩϯτϧ ΪϴϨϛΏΎΨΘϧέexcelϪϨϳΰ file typeζϥδϗ

ϢϴϨϴΒΑϢϴϫϮΧ ϣϊϧϫθϓή ϩίϊϧέϩϊηϫθχύγ ΎϫϪϟϮϟήτϗςγϮΘϣ έύ ΖϔϴηέΎϬ έωϝύμϣ ΪҨΎΑΎϫϪϟϮϟήτϗΩέΩϥΪηϩήΒϴϟΎ ϪΑίΎϴϧϩΎ ΘγΩΎҨΪϨΘδϫ ϓΩΎμΗϥΎθϨϴ ϧύθϣϫαζβδϧωϊϋϧҩ ΪηΎΑ ΎϫήτϗςγϮΘϣ 3.8 4. 3.3 4.5 Ζϔϴη Ζϔϴη Ζϔϴη Ζϔϴη İĮŤƀŞưƷƱƺƯŻō ϣ ϪΒγΎΤϣ έ ϥ ΐҨήο ϭ ΩίΩή ϣ ήθϐθϣ ΪϨ ΎҨ ϭω ϦϴΑ ρύβηέ γέήα ϪΑ ΘδΒϤϫ ϪτΑέ ήθϐθϣεήθθϐηύαήθϐθϣ ҨΕήϴϴϐΗή ΪηΎΑ ϔϨϣΎҨΖΒΜϣΖγϦ ϤϣΎϫήϴϐΘϣϦϴΑ ΘδΒϤϫΪϨ ϩήϥϫ ή ҨΩζϫΎ ΎΑ ҨζϫΎ β όϟύαύҩ ή ҨΩζҨΰϓΎΑ ҨζҨΰϓϭΪηΎΑϩήϤϫ ή ҨΩ +1ΎΗ0ίϪ ΪҨήΧ ήαύούϙηϭϊϣέωζҩΰϓϟμϣζγζβμϣύϭϧϧθα ΘδΒϤϫϢϴҨϮ ϣωϯθα ΩέΩϥΎγϮϧ ϦϴΑ ΘδΒϤϫϪ ΩϮη ϣϫθϔ ΩϮηϩήϤϫ ή ҨΩήϴϐΘϣζϫΎ ΎΑήϴϐΘϣ ҨζҨΰϓϭήϴϴϐΗή ΪϨ ϣήθθϐη-1ύη0ίϭζγ ϔϨϣΎϬϧ ΖγήϔλΎϬϧϦϴΑ ΘδΒϤϫΐҨήοΪηΎΑϪΘηΪϧΩϮΟϭ ϪτΑέήϴϐΘϣϭΩϦϴΑή ΩέΩΩϮΟϭήϴϐΘϣϭΩϦϴΑϲτΧϪτΑέΪηΎΑ ϳΩΰϧ-1Ύϳ1 ϪΑϲ ΘδΒϤϫΐϳήοή ΘδΒϤϫΐҨήο ΖγήϴϐΘϣϭΩϦϴΑρΎΒΗέΩϮΟϭϩΪϨϫΩϥΎθϧΐϳήοϦϳ ΖγήΘϜϳΩΰϧ Ύϳϭ ϪΑΐϳήοέΪϘϣΪηΎΑΪϳΪηήϴϐΘϣϭΩρΎΒΗέϪ ήϫ ΩϮηϲϣϚϳΩΰϧήϔλϪΑΐϳήοέΪϘϣήϴϐΘϣϭΩϦϴΑρΎΒΗέζϫΎϛΎΑ ΩϮΑΪϫϮΧήϔλήΑήΑϲ ΘδΒϤϫΐϳήοέΪϘϣΪϨηΎΑϞϘΘδϣήϴϐΘϣϭΩή έωζϓή ϪΠϴΘϧέϝϼϘΘγϥϮΗϲϣΪηΎΑήϔλήΑήΑϲ ΘδΒϤϫΐϳήοή ΪϨηΎΑϝΎϣήϧήϴϐΘϣϭΩή ήθχεέϯλϧϳήθϗ ΘδΒϤϫΐҨήοϪΒγΎΤϣ ΩϮηϲϣϩΩΎϔΘγCovarianceέΪϘϣίϥϮγήϴ ϲ ΘδΒϤϫΐϳήοϪΒγΎΤϣϱήΑ αύθϙϣϭϊσϭύαϫϛϲϳύπϧίϲϟϭζγΐγύϩϣρύβηέϥΰθϣϱήθ ϩίϊϧϱήαcovarianceέϊϙϣ ϥϭϊαΐҩήο ϳί ΘδΒϤϫϥΰϴϣϪδҨΎϘϣϱήΑΪϳΎΑΪϳϲϣΖγΪΑϩΪηϱήϴ ϩίϊϧϫμψθϣ ΩήϛϩΩΎϔΘγΪλέΩΪΣϭ ΩέΩέΖϴλΎΧϦϳ ΘδΒϤϫΐϳήοΎϳCorrelation ΩϮηϲϣϩΩΎϔΘγϱ ϪτϘϧέΩϮϤϧίήϴϐΘϣϭΩρΎΒΗέϩΪϫΎθϣϱήΑϻϮϤόϣ p( y / x) p( y) ϨόҨ ϓΩΎμΗήϴϐΘϣϭΩIndependentϝϼϘΘγ ϢϫΎΑϥΎθϧΩΪҨϭέϪ ϠΑϥίϭϭΪϗϞΜϣΪϨηΎΑϢϫϝϮϠόϣϭΖϠϋϪΘδΒϤϫ ϼϣΎ ήθϐθϣϭωζδθϧϡίϻ ΩέΩρΎΒΗέ 61

E( xy) E( x). E( y) Co var iance n 1 E( xy) E( x). E( y) n 1 E( xy) E( x). E( y) Corrolation Var( x) Var( y) Var( x) Var( y) n 1 n 1 0 ϭ ΘδΒϤϫςΧέΩϮϤϧ ϥϯγήθ ΘδΒϤϫΐҨήοϪΒγΎΤϣ E( xy) E( x) E( y) ϩύ ϧϊηύαϫθηωωϯοϭ ΘδΒϤϫή Analyze Corrolate Bivariate ΪϴϨϛϲσέήϳίήϴδϣέϮΘγΩϦϳϪΑϲγήΘγΩϱήΑ ΪϳέΩΝΎϴΘΣϱΩΪϋήϴϐΘϣϭΩϪΑϞϗΪΣέϮΘγΩϦϳϱήΟϱήΑ ϲ ΘδΒϤϫΐϳήοωϮϧ ίϊ ҨΎΑ Ϥ γϭ ϪΒΗέˬ ΩΪϋ ΎϬγΎϴϘϣί Ҩήϫ ήα Ωή ϩωύϔθγϥύηωϯχϫβγύτϣεϭέ Covariance Correlation ϲ ΘδΒϤϫ ϱήθϣέύ ϲ ΘδΒϤϫΐϳήο Pearson ±ϥϯγήθ ϲ ΘδΒϤϫΐϳήοϲτΧϲ ΘδΒϤϫ ϝύ ϣήϧϊ ϳίϮΗϱέΩϱΩΪ ϋήθϐθϣϭωή ϫϫϛζγϧϳήανήϓ ΪϨΘδϫϩήϴϐΘϣϭΩ 6

H H 0 1 : 0 : 0 ϱήθϣέύ Ύϧϲ ΘδΒϤϫΐϳήο SpearmanϦϣήϴ γϲ ΘδΒϤϫΐϳήοϱ ϪΒΗέϲ ΘδΒϤϫ Kendall s Tau_b±ϝΪϨϛϲ ΘδΒϤϫΐϳήοϲΒϴΗήΗϲ ΘδΒϤϫ ϲ ΘδΒϤϫΐϳήοϥϮϣί ϲ ΘδΒϤϫΐϳήοέΪϘϣϱήΑϪϓήσϭΩϥϮϣίϚϳtwo tailϫϩϳΰ ΏΎΨΘϧΎΑ- ΩϮηϲϣϡΎΠϧ One ϪϓήτϜϳϥϮϣίίΖγήΘϬΑΖγήψϧέΩΰϴϧϥΖϬΟϲ ΘδΒϤϫΐϳήοέΪϘϣήΑϩϭϼϋή - ΩϮηϩΩΎϔΘγTail ϪϓήτϜϳϥϮϣί ϪϓήτϜϳϥϮϣί H 0 : 0 H 0 : 0 H1 : 0 H1 : 0 ΩϮηϲϣϩΩΩϲΟϭήΧέΩϲ ΘδΒϤϫΐϳήοέΪϘϣϥΩϮΑέΩΎϨόϣϦϴϴόΗϥΎϜϣ flag ϪϨϳΰ ΏΎΨΘϧΎΑ- Ϧϴ ϧύθϣϲϔθλϯηέύϣζϳύϥϧϥύϝϣoptionώύψθϧύα- Ώήπϣ ϭ βϧύϳέϯϛ έϊϙϣ ζϳύϥϧ ϭ ΩέΪϧΎΘγ ϑήτϧ ϭ ΕέϮλ ϥύϥϫ ΕϼοΎϔΗ ΐϳήο ΩέΩ ΩϮΟϭ ΕϼοΎϔΗ ΖγβϧΎϳέϮϛϪΒγΎΤϣ ϱήαϫβγύτϣexclude Cases pairwiseϫϩϳΰ ΏΎΨΘϧΎΑ ή ϳΩ έω ϩϊθϥ ϩωω ϦΘϓή ήψϧ έω ϥϭϊα Ύϫ ΖϔΟ ΩϮηϲϣϪΒγΎΤϣΩέϮϣήϫϱΎϫήϴϐΘϣ Ϛϳ ΩϮΟϭ ΎΑ Exclude cases listwise ϪϨϳΰ ΏΎΨΘϧ ΎΑ ΪηΪϫϮΧϪΘϓή ϩϊϳωύϧωέϯϣϟϛωέωϲγέήαέωϩϊηϣ έϊϙϣϫϛήθϐθϣ ϱήαϱϊόαϭωβϳήηύϣϛϳΐθηήηϧϳϫαωέωωϯοϭϲγέήαϧϳέωΰθϧήθϐθϣϭωίζθαώύψθϧϥύϝϣ ΪηΪϫϮΧΩΎΠϳΎϫήϴϐΘϣΖϔΟΖϔΟϲ ΘδΒϤϫΐϳήοέΪϘϣϪΒγΎΤϣϭϲγέήΑ ϢϴϨ ϣϩωύϔθγήҩίέϯθγωί ΘδΒϤϫέΩϮϤϧϢγέ ήα Graph Legacy dialogs scatter plot simpleύҩ Matrix 63

íìçê±ºf ÅÄ m ϭω ϝϼϙθγ ϥϯϣί ήα Ϣϫ ϭ ϊҩίϯη είήα ήα ϢϬΑ Chi-square ϥϯϣί Ϫ Ϊη ϪΘϔ ϞΒϗ ϪδϠΟ έω ήθϐθϣϭωϧθαϫταέωϯοϭϫϧύθϧωϯα 0 ή (x,y)ΐηήϣνϭί ήα ϨόҨΩϭέ ϣέύ Α ϓΩΎμΗήϴϐΘϣ ΪηΎΑ ϣ ϭ ϥύθγ ΰ ήϣ ί ϒϠΘΨϣ Ύϫ ϩϊ θϧω έω ϥύҩϯπθϧω ϟϯβϗ ΖΒδϧ ϪδҨΎϘϣ ϞΒϗ ϪδϠΟ ϝύμϣ έω Ϥγ ήθϐθϣ ϭω ήϫ Ϫ ϒϠΘΨϣ Ύϫ ϪΘηέ ϭ ϩϊ θϧω ϞΤϣ Ϫ ϢϴϧΪΑ ϢϴϫϮΧ ϣ ΎϬϧΎΘγήϬη ˮϪϧΎҨΪϨϠϘΘδϣΪϨΘδϫ H H 0 1 : 0 : 0 Analyze Descriptive Statistics- Crosstabs- statistic chi-square facaulty * place Crosstabulation Count place Total center outer center facaulty engineering 16 14 30 art 14 6 0 ecoomic 13 10 3 etc 13 8 1 Total 56 38 94 Chi-Square Tests Value df Asymp. Sig. (-sided) Pearson Chi-Square 1.54(a) 3.677 Likelihood Ratio 1.551 3.671 Linear-by-Linear Association.153 1.696 N of Valid Cases 94 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 8.09. ή ϭϊϧϩωή ΜϨΧέή ҨΪϤϫΕΎϓϼΘΧ ϨόҨΖγ Ϯ Pearson Chi-Square=1.54έΪϘϣ Θϗϭ ΖγήϴϐΘϣϭΩϦϴΑϪτΑέϩΪϨϫΩϥΎθϧΪηΎΑ έΰα ΩέέήϔλνήϓϪϧϮϤϧϪ ΪϫΩ ϣϥύθϧϭϩωϯα0.05ίήθ έΰαϫ Asymp. Sig. (-sided)=0.677 ήθϐθϣϭωϧθαϝϼϙθγωϯοϭ ϨόҨΪϨ Ϥϧ ΖγΡήηϦҨϪΑϪΒγΎΤϣϝϮϣήϓˮΖγϩΪϣΖγΩϪΑΎΠ ί1.54ωϊϋϫ ϢϴϧΪΑϢϴϫϮΧ ϣ outerϥϯθγϭcenterϥϯθγϩϊηϩϊϫύθϣήҩωύϙϣ O i έύψθϧωέϯϣήҩωύϙϣ E ( O i i Ei ) έύψθϧωέϯϣήҩωύϙϣϫϧϯϥϧ ϮγίϩΪηϩΪϫΎθϣϑϼΘΧςγϮΘϣ Ei outerϥύθγήϭηϥϯθγ ήαcenterϥύθγΰ ήϣϥϯθγ ήα 30*38 30*56 E1 1.13 E1 17.87 94 94 0*56 0*38 E 11.91 8.09 E 94 94 3*56 3*38 E E 13.7 3 9.30 3 94 94 1*38 1*56 E E 1.51 4 8.49 4 94 94 64

ϣϩωύϔθγήҩίϝϯϣήϓίεϯθγϭήτγ Ύϫ ϧϭήϓωϯϥπϣίέύψθϧωέϯϣ ϧϭήϓϫβγύτϣ ήα ΩϮη A* B ήτγέωϩϊηϩϊϫύθϣ ϧϭήϓϊϥο A Ei N ϥϯθγέωϩϊηϩϊϫύθϣ Ύϫ ϧϭήϓϊϥο B ϩϊηϩϊϫύθϣ ϧϭήϓϊϥο N ϡiέύψθϧωέϯϣ ϧϭήϓ E i ( O ) i Ei (16 17.87) (14 11.91) (13 13.7) (13 1.51) 0.6 E 17.87 11.91 13.7 1.51 i ( O ) i Ei (14 1.13) (6 8.09) (10 9.30) (8 8.49) 0.91 E 1.13 8.09 9.30 8.49 i ϥύθγΰ ήϣ ϥύθγήϭη Pearson Chi Square=0.6+0.91=1.54 ΪϨ ΏΎδΣέΎϫ E I Analyze Descriptive Statistics- Crosstabs- place * facaulty Crosstabulation ϨόҨέΎψΘϧΩέϮϣήҨΩΎϘϣΪϧϮΗ ϣspssέΰϓϡήϧ statistic chi-square Cell...observed, expected facaulty engineering art ecoomic etc engineering place center Count 16 14 13 13 56 Total Expected Count 17.9 11.9 13.7 1.5 56.0 outer Count 14 6 10 8 38 Expected Count 1.1 8.1 9.3 8.5 38.0 Total Count 30 0 3 1 94 Expected Count 30.0 0.0 3.0 1.0 94.0 65

ΪϴϨ ϲγέήαέed,carcatϧθηύϣωϯϧϭεϼθμτηϥΰθϣήθϐθϣϭωϝϼϙθγdemoϟϳύϓέωϝύμϣ H 0 : 0 H 1 : 0 Analyze Descriptive Statistics- Crosstabs- statistic chi-square correlation Count Level of education Level of education * Primary vehicle price category Crosstabulation Primary vehicle price category Total Economy Standard Luxury Economy Did not complete high school 483 481 46 1390 High school degree 587 689 660 1936 Some college 390 485 485 1360 College degree 31 508 535 1355 Post-undergraduate degree 69 11 178 359 Total 1841 75 84 6400 Chi-Square Tests Value df Asymp. Sig. (-sided) Pearson Chi-Square 85.689(a) 8.000 Likelihood Ratio 85.50 8.000 Linear-by-Linear Association 75.149 1.000 N of Valid Cases 6400 a 0 cells (.0%) have expected count less than 5. The minimum expected count is 103.7. Symmetric Measures Asymp. Std. Approx. Value Error(a) T(b) Approx. Sig. Interval by Interval Pearson's R.108.01 8.70.000(c) Ordinal by Ordinal Spearman Correlation.105.01 8.453.000(c) N of Valid Cases 6400 a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal approximation. ϥΰθϣωϯη ϣωέήθϐθϣϭωϝϼϙθγϭϊϩ ϣωέέήϔλνήϓϫϧϯϥϧβ p value 0 ϪΠϴΘϧ ϥύθϧέ0.105ωϊϋϫ ΖγΐγΎϨϣϦϣήϴ γϊϩθδϫϲϥγήθϐθϣϭωϥϯ ϝϭϊοέωϲ ΘδΒϤϫ ΪϫΩϲϣ 66

ҨΰΟ ΘδΒϤϫΐҨήο ήθϐθϣϛϳϝήθϩϛεέϯλέωήθϐθϣϭωϧθαϲτχϲ ΘδΒϤϫϥΰϴϣζΠϨγϥΎϜϣΐϳήοϦϳίϩΩΎϔΘγΎΑ ΩέΩΩϮΟϭϥήϴΛΎΗϑάΣΎϳϡϮγ ϲ ΘδΒϤϫΐϳήοϪΒγΎΤϣΎΑΪϨηΎΑϪΘηΩϪτΑέΰϴϧϱή ϳΩήϴϐΘϣϪτγϭϪΑήϴϐΘϣϭΩΖγϦϜϤϣ ΪηΪϫϮΧϪΒγΎΤϣϡϮγήϴϐΘϣήΛϑάΣΎΑήϴϐΘϣϭΩϦϴΑϲτΧϪτΑέϥΰϴϣϲΰΟ ΖγήϳίΕέϮλϪΑϪΒγΎΤϣεϭέ rab rac rbc rabc (1 r AC )(1 r BC ) ΖγϱΩΪϋήϴϐΘϣ ϞϗΪΣϪΑΝΎϴΘΣέϮΘγΩϦϳϡΎΠϧϱήΑ ΪϴϨϛϲσέήϳίήϴδϣέϮΘγΩϦϳϪΑϲγήΘγΩϱήΑ Analyze - Correlate - Partial... ϩωύγ ϲ ΘδΒϤϫ ΐϳήο ϝϭϊο ζϳύϥϧ ϭ ϲϔθλϯη ϱύϫ ϩέύϣ ζϳύϥϧ ϥύϝϣ Option ϪϨϳΰ ΏΎΨΘϧ ΎΑ ΩέΩΩϮΟϭΰϴϧ Zero-order 67

ή ψϧέωέage,income,carϧθ ηύϣϊ ϳήΧϪ ϨϳΰϫϭΪ ϣέωˬϧ γή ϴϐΘϣϪ γdemoϟ ϳΎϓέΩϝΎ Μϣ Ϊϴθ ΑήϴϐΘϣϪγϦϳϱήΑϲδϳήΗΎϣέϮσϪΑέscatterplotέΩϮϤϧΪΘΑΪϳήϴ Α Grafh - scatterplot - matrix ϢϴϨ ϲϣώύδσέήθϐθϣϫγϧθαϲ ΘδΒϤϫΐϳήο Analyze - correlate- bivariate ή ϴϐΘϣϭΩϦϴ ΑϪ ταέωϊ ΠϣϭϪ Θϓή Ζ ΑΎΛέincomeή ϴϐΘϣϲ ϳΰΟϲ Θδ ΒϤϫΐϳή οίϩωύϔθ γύ Α ϢϳέϭϲϣΖγΪΑέage,car Correlations Household income in Price of Age in years thousands primary vehicle Age in years Pearson Correlation 1.335(**).376(**) Household income in thousands Sig. (-tailed).000.000 N 6400 6400 6400 Pearson Correlation.335(**) 1.79(**) Sig. (-tailed).000.000 N 6400 6400 6400 Price of primary vehicle Pearson Correlation.376(**).79(**) 1 ** Correlation is significant at the 0.01 level (-tailed). Sig. (-tailed).000.000 N 6400 6400 6400 Correlations 0.335incomeϭage ΘδΒϤϫΐҨήο 0.376...carϭage ΘδΒϤϫΐҨήο 0.79«incomeϭcar ΘδΒϤϫΐҨήο incomeϝήθϩ Control Variables Household income in thousands Age in years Price of primary vehicle Price of primary Age in years vehicle Correlation 1.000.193 Significance (-tailed)..000 df 0 6397 Correlation.193 1.000 Significance (-tailed).000. df 6397 0 0.193...carϭage ΘδΒϤϫΐҨήο ή ϴϐΘϣϝή ΘϨ ίβ ϲ ϟϭωϯ Α0.376ˬage,carή ϴϐΘϣϭΩϦϴ Αϲ Θδ ΒϤϫΐϳήοϝϭΖϟΎΣέΩϪΠϴΘϧ ΪϫΩϲϣϥΎθϧέκϟΎΧρΎΒΗέΩΪϋϦϳϪ Ωή Ϊϴ ήθθϐη0.193ϫαˬincome 68

ŵśťſřspss1 ŶŝįŹįŚƣō 69 ŢƄĭźŝƎųƶƫŵŚƘƯRegressionƱƺǀſźĭŹ ΩέΩΩϮΟϭϩέϪγήϴϐΘϣϭΩρΎΒΗέζΠϨγϱήΑ scatterplotέωϯϥϧϣγέ Corrolationϲ ΘδΒϤϫΐϳήοϱήϴ ϩίϊϧ Ζθ ήαςχϥϯθγή έϫβγύτϣ ΩϭέϲϣέΎ ΑήϴϐΘϣϭΩρΎΒΗέϝΪϣϪέϭζΠϨγϱήΑϪ ΖγϱέΎϣεϭέϦϳήΗΩήΑέΎ ή ϥϯθγή έ ϩωωύαϲ ϨϫΎϤϫϦϳήΘϜϳΩΰϧϪϛϲτΧϪϟΩΎόϣΐϳήοˬϲτΧϥϮϴγή έ ΖγέΩέϩΪηϩΪϫΎθϣϱΎϫ ϲϣωέϭήα ΪϨϛ ΩέΩΩϮΟϭΰϴϧϱΪόΑήϳΩΎϘϣϲϳϮ θθ ϥύϝϣζθ ήαςχϫϟωύόϣίϩωύϔθγύα ϢϳέΩDependentϪΘδΑϭήϴϐΘϣϚϳϭIndependentϞϘΘδϣήϴϐΘϣϚϳϥϮϴγή έϫϟωύόϣέω ΖγϲϜϳίζϴΑϞϘΘδϣϱΎϫήϴϐΘϣΩΪόΗϩήϴϐΘϣΪϨ ϥϯθγή έϫϟωύόϣέω ϝύμϣ έϯθϛϛϳέϯϧύχϊϣέωαύγήαβϛϯϟϱϻύϛϛϳεϭήϓϥΰθϣωέϭήα ΕϼϴμΤΗϥΰϴϣˬϪΑήΠΗˬϦγˬαΎγήΑϩΎ ηϭήϓϛϳϥύ ΪϨηϭήϓεϭήϓϥΰϴϣΩέϭήΑ ιϯμψϣϱύϭηϭέίϩωύϔθγϭέάαωϯϧˬωϯϛωϯϧˬϲ ΪϧέΎΑϥΰϴϣαΎγήΑϝϮμΤϣϥΰϴϣΩέϭήΑ ΕΎϓϊϓέ ήθηωύϔηέαύγήαϕήαύή ήθηςγϯηύπϓϛϳϥϊηϧηϭέζσύδϣωέϭήα ϩωύγϲτχϝϊϣ ΖγΎϣϝήΘϨ ΖΤΗϭϞϘΘδϣήϴϐΘϣxϥέΩϪϛ ϲϣϩϊθϣύϧconstantύϳϊβϣίνήϋέϊϙϣa ΩϮη ϲϣϩϊθϣύϧϟϙθδϣήθϐθϣΐϳήούϳςχΐθηέϊϙϣb ΩϮη ΖγϥϮϴγή έϝϊϣϭϩϊϫύθϣϱύτχe βϧύϳέϭϭa+bxϧθ ϧύθϣϱέωϭζγxϫαϫθδαϭϲϓωύμηήθϐθϣy x Ζγ ϲϓωύμηήθϐθϣ βϧύϳέϭϭήϔλϧθ ϧύθϣϱέωϭζγϲϓωύμηϱύτχ Ζγ ίϊϩηέύβϋϝϊϣϧϳϱύϫήθϣέύ,, b a ϭϲόϗϭέϊϙϣy ΖγϩΪηΩέϭήΑέΪϘϣYˆ ΕΎοϭήϔϣ ϩϊϧύϣϲϗύαϊϳίϯηύϳζγϝύϣήϧϊϳίϯηϛϳϫθδαϭήθϐθϣϊϳίϯηˬϟϙθδϣήθϐθϣέϊϙϣήϫϱήα Ύϫ ΪηΎΑή ϳΩΕΪϫΎθϣίϞϘΘδϣϭϝΎϣήϧΪϳΎΑ ΪηΎΑΖΑΎΛϪΘδΑϭήϴϐΘϣβϧΎϳέϭΪϳΎΑϞϘΘδϣήϴϐΘϣήϳΩΎϘϣϪϤϫϱήΑ ΪϨηΎΑϲϓΩΎμΗΪϳΎΑΕΪϫΎθϣϪϤϫ ΪηΎΑϲτΧΪϳΎΑϞϘΘδϣϭϪΘδΑϭήϴϐΘϣϭΩρΎΒΗέ ˆ) ( / Y Y bx a x Y

Ϥϧ ϑήμϣ ϭ ϞϘΘδϣ ήθϐθϣ ϥϯχ έύθϓ Ϫ Ϊϳήϴ Α ήψϧ έω έ Ϥϧ ϑήμϣ ϭ ϥϯχέύθϓ ήθϐθϣ ϭω ϼΜϣ ΖγϪΘδΑϭήϴϐΘϣ ΖγϩΩήϛϝΪϣέΕΪϫΎθϣήΘϬΑϥϮϴγή έϫϟωύόϣϊηύαήθϥϛύτχεύόαήϣςγϯθϣϩίϊϧϫ ήϫ sy ΪϧϮηϲϣϪΒγΎΤϣήϳίΕέϮλϪΑϥϮϴγή έΐϳήοωέϭήα bˆ r s eˆ x aˆ y bx ˆ ΪϧϮηϲϣϪΒγΎΤϣήҨίΕέϮλϪΑResidualΎτΧέΪϘϣΩέϭήΑ yi yˆ yi aˆ bˆ xi ΪϧϩΪϣΖγΪΑεϭέϦϴϤϫϪΑΰϴϧΎϫΩέϭήΑϪϛΖγΎτΧΕΎόΑήϣωϮϤΠϣϥΩήϛϪϨϴϤϛϑΪϫ ΪϴϨϛϲσέήϳίήϴδϣέϮΘγΩϦϳϪΑϲγήΘγΩϱήΑ Analyze - Regression - Linear... έήθϐθϣϧϳϊϩ ϞϘΘδϣήϴϐΘϣ ϥϯϩϋϫαϫϛϲηέϯλέω ΪϨ ϥϯθγή έ ϪΑ ρϯαήϣ ΕΎΒγΎΤϣ spss ΪϴϨϛ Ωέϭ ΩήϛΪϫϮΧϪΒγΎΤϣέϩήϴϐΘϣ ΪϴϨϛΩέϭέϪΘδΑϭήϴϐΘϣdependentΖϤδϗέΩ ϱύϫήθϐθϣ Ύϳ ήθϐθϣ independent ΖϤδϗ έω ΪϴϨϛΩέϭέϞϘΘδϣ ί ϱέϊϙϣ ϭ ήθϐθϣ selection variable ΖϤδϗ έω Ωέϭ Ζγ ΎϤη ήψϧ ΩέϮϣ ϲγέήα ϱήα Ϫϛ ϥ ήθϐθϣϥϯϩϋϫαϥϯθγή έέωϊϳύβϧήθϐθϣϧϳϊθϩϛ ΪηΎΑϪΘϓέέΎϛϪΑϞϘΘδϣ ϥϯϩϋ ϪΑ Ϫϛ ϱήθϐθϣcase lable ΖϤδϗ έω ΪϴϨϛκΨθϣΖγϪΘϓέέΎϛϪΑΎϫΩέϮϣϲϣΎγ ϪΑ ϲϫω ϥίϭ ήθϐθϣ WLS Wieght ΖϤδϗ έω ΖϓέΪϫϮΧέΎϛϪΑβϧΎϳέϭέΪϘϣϱίΎγϥΎδϜϳϱήΑϥίϭϦϳΪϴϨϛΩέϭέΎϫΩέϮϣ ΩέΩΩϮΟϭϲΟϭήΧέΩϱή ϳΩϱΎϫϩέΎϣήϳΩΎϘϣζϳΎϤϧϥΎϜϣStatisticϪϤϛΩΏΎΨΘϧΎΑ ΩέΩΩϮΟϭϥϮϴγή έϫϟωύόϣϲγέήαϱύϫέωϯϥϧζϳύϥϧϥύϝϣplotϫϥϛωώύψθϧύα ΩέΪϧΎΘγϩΪϧΎϣϲϗΎΑϭϪΘδΑϭήϴϐΘϣίϩΪηϩΩέϭήΑήϳΩΎϘϣϱίΎγϩήϴΧΫϥΎϜϣSaveϪϤϛΩΎΑ ΩέΩΩϮΟϭϲόϗϭ 70