Statistical analysis: A nhanho-ne-nhanho gwara

Tikugashirei pakuongorora kwako kwenhamba, chishandiso chehwaro chinoshandiswa munzvimbo dzakasiyana siyana sesainzi, hupfumi, uye sainzi yemagariro. Yakagadzirirwa vadzidzi nevatsvagiri, chinyorwa ichi chinokutungamira kuburikidza nekushandisa misimboti iyi kuti unzwisise yakaoma data uye kugadzirisa maitiro ekuita sarudzo. Kuziva matekiniki aya kunovandudza yako tsvakurudzo kugona, zvichikubvumidza kuti uite ongororo yakakwana uye kugadzira mhedzisiro dzakakosha.

Tichakufambisa nematanho akakosha anosanganisirwa muongororo yenhamba-kubva pakugadzira fungidziro uye kuronga yako tsvakurudzo kuunganidza data, kuita ongororo yakadzama, uye kududzira zvabuda. Chinangwa ndechekudzikisira nzira dzechiverengero uye kukupa simba neruzivo rwekushandisa nenzira ine chivimbo idzi matekiniki muzvidzidzo zvako uye zvehunyanzvi.

Ziva kuti ongororo yenhamba inogona kuvhura sei ruzivo uye kufambisa tsvakiridzo yako kumberi!

Kunzwisisa uye kushandisa statistical analysis

Statistical ongororo ndeyekuongorora kwakarongeka kwedata kuona mafambiro, mapatani, uye hukama mukati mehuwandu hweruzivo. Iyi nzira yakakosha pakuita sarudzo ine ruzivo uye inobudirira kuronga hurongwa muzvikamu zvakasiyana, kusanganisira dzidzo, hurumende, uye bhizinesi. Heano maitiro aungaita ongororo yenhamba:

• Kuronga uye hypothesis tsanangudzo. Tsanangura zvakajeka mafungiro ako uye gadzira chidzidzo chako nekunyatsotarisisa saizi yemuenzaniso uye nzira dzesampling kuti uve nechokwadi chakasimba uye chakavimbika mhedziso.
• Kuunganidzwa kwedata uye nhamba dzinotsanangura. Kuronga nekupfupikisa dhata uchishandisa nhamba dzinotsanangura ndiyo danho rekutanga rekuongorora mushure mekuunganidza data. Iyi nhanho inosimbisa maitiro epakati uye kusiyanisa mukati me data.
• Zviitiko zvisingaverengeki. Danho iri rinoshandisa mhedziso kubva kumuenzaniso kuenda kuhuwandu hwevanhu. Inosanganisira kuongororwa kwekufungidzira uye nzira dzekuverenga kuti usarudze kukosha kwenhamba yezviwanikwa.
• Kuturikira uye generalization. Danho rekupedzisira rinosanganisira kududzira dhata uye kuenzanisa mibairo kune yakakura mamiriro. Izvi zvinosanganisira kukurukura zvinorehwa nezvakawanikwa uye kupa mazano ekutsvagisa ramangwana.

Ongororo yeStatistical inosimudzira kugona kwesangano uye yekutsvagisa, ichiita basa rakakosha muzvisarudzo zvemitemo, kuvandudzwa kwechigadzirwa, uye kuvandudzwa kwehurongwa. Sezvo basa re data mukuita sarudzo richikura, kukosha kwekuongorora kwenhamba kunowedzera. Gwaro iri rine chinangwa chekupa hwaro hwakasimba hwekushandisa hunyanzvi uhu hwakakosha.

Mafungiro asiriwo akajairika mukuongorora kwenhamba

Pasinei nesimba rayo guru, kuongorora kwenhamba kunowanzova nemafungiro asina kururama akapararira. Kujekesa izvi kunogona kuvandudza zvakanyanya kurongeka uye kuvimbika kwekududzirwa kwetsvakiridzo. Hezvino zvimwe zvekusanzwisisana zvakanyanya mukuongorora kwenhamba:

• Kududzirwa zvisirizvo kwep-values. A p-value inowanzosanzwisiswa semukana wekuti null hypothesis ichokwadi. Muchokwadi, inoyera mukana wekuona data zvakanyanyisa se, kana zvakanyanyisa kupfuura, izvo zvakaonekwa chaizvo, kugamuchira iyo null hypothesis ndeyechokwadi. P-kukosha diki inoratidza kuti data rakadaro ringadai risingaite kana null hypothesis yanga iri yechokwadi, zvichiita kuti irambwe. Nekudaro, hazviyere mukana weiyo hypothesis pachayo kuva yechokwadi.
• Kuvhiringidzika pakati pekubatana uye kukonzera. Imwe kukanganisa kwakajairika mukuongorora kwenhamba ndeyekufungidzira kuti kuwirirana kunoreva causation. Nekuda kwekuti maviri akasiyana anoenderana hazvireve kuti imwe inokonzera imwe. Hukama hunogona kusimuka kubva kune yechitatu chinja inobata ese ari maviri kana kubva kune mamwe asina-causal hukama. Kugadzira causation kunoda kuedza kunodzorwa kana nzira dzenhamba dzakagadzirirwa kutonga kunze kwezvimwe zvinhu.
• Mafungiro asina kunaka pamusoro pekukosha kwenhamba uye saizi yemhedzisiro. Kukosha kwenhamba hakurevi zvakakosha. Mhedzisiro inogona kuve yakakosha asi ine mhedzisiro saizi idiki zvekuti haina kukosha kunoshanda. Zvakadai, mhedzisiro yenhamba isina kukosha haireve hazvo kuti hapana mhedzisiro; zvinogona zvakare kureva saizi yemuenzaniso yaive idiki kuti ione mhedzisiro. Kunzwisisa saizi yemhedzisiro inopa nzwisiso mukukosha kwekukanganisa, izvo zvakakosha pakuongorora zvinoshanda zvinorehwa nemhedzisiro.

Nekugadzirisa izvi zvisirizvo mukutanga kwechidzidzo chekuongorora kwenhamba, unogona kudzivirira njodzi dzakajairika dzinogona kutungamira kune mhedziso dzisiridzo kana kududzira zvisirizvo data. Ongororo yenhamba, kana ikanzwisiswa uye ikashandiswa nemazvo, inogona kuvandudza zvakanyanya kutendeseka uye kukanganisa kwezviwanikwa zvako zvetsvagiridzo.

Advanced statistical techniques

Sezvo ndima yekuongororwa kwenhamba inofambira mberi, nzira dzakasiyana-siyana dzepamusoro dzave dzakakosha kune vaongorori vanobata makuru edataset uye mibvunzo yakaoma. Ichi chikamu chinopa mucherechedzo wakajeka weidzi nzira, kuratidza mashandisiro adzo epasirese uye zvakanakira:

Multivariate analysis

Multivariate ongororo inobvumira kuongororwa kweakawanda akasiyana-siyana panguva imwe chete kuti afumure hukama uye pesvedzero pakati pavo. Maitiro akajairika anosanganisira kuwanda kudzoreredza, factor analysis, uye MANOVA (Multivariate Analysis of Variance). Nzira idzi dzinonyanya kubatsira mumamiriro ezvinhu apo zvinhu zvakasiyana-siyana zvinokanganisa shanduko inotsamira, sekudzidza kukanganisa kwemaitiro akasiyana ekushambadzira pane maitiro evatengi. Kunzwisisa hukama uhwu kunogona kukubatsira kuona izvo zvinonyanya kupesvedzera zvinhu uye kugadzirisa nzira zvinoenderana.

Machine kudzidza algorithms mukuongorora data

Kudzidza kwemichina kunonatsiridza nzira dzechinyakare dzechiverengero nemaalgorithms akagadzirirwa kufanotaura uye kuronga data. Izvi zvinosanganisira nzira dzekudzidza dzakatariswa sedzekudzoreredza uye kuisa mumapoka miti, iyo yakanakira kufanotaura nezvekuchinja kwevatengi kana kuisa maemail mumapoka sespam kana asiri espam. Nzira dzekudzidza dzisina kutariswa senge kuunganidza uye principal component analysis yakanakira kutsvaga mapatani mudata. Semuenzaniso, vanogona kuunganidza vatengi nekutenga maitiro pasina akaiswa zvikamu.

Structural equation modelling (SEM)

SEM inyanzvi yezviverengero ine simba inoedza fungidziro nezvehukama pakati pezvinocherechedzwa uye zvakadzika zvakasiyana. Inobatanidza kuongorora kwechinhu uye kudzoreredza kwakawanda, ichiita kuti ive ine simba rekuongorora hukama hwakaoma hwekukonzeresa, sekunzwisisa kuti kugutsikana kwevatengi (chinochinja-chinja chisina kuyerwa zvakananga) chinokanganisa maitiro ekuvimbika. SEM inoshandiswa zvakanyanya musocial science, kushambadzira, uye psychology kuenzanisira yakaoma network yehukama.

Nguva-yakatevedzana kuongororwa

Nguva-yakatevedzana ongororo yakakosha pakuongorora mapoinzi edata akaunganidzwa nekufamba kwenguva, zvichibatsira kufanotaura mafambiro enguva yemberi kubva pamatanho apfuura. Iyi nzira inoshandiswa zvakanyanya mumisika yemari kufanotaura mitengo yemasheya, mune zvemamiriro ekunze kufanotaura shanduko yemamiriro ekunze, uye mune zvehupfumi kufungidzira zviitiko zvehupfumi zveramangwana. Matekiniki akaita seARIMA modhi uye kuparara kwemwaka anobatsira kugadzirisa maitiro akasiyana uye shanduko yemwaka mune data.

Kunzwisisa nekushandisa hunyanzvi hwepamberi uhwu hunoda hwaro hwakasimba mudzidziso yenhamba uye kazhinji kushandiswa kwehunyanzvi maturusi esoftware. Zvinokurudzirwa kuti vatsvakurudzi vaite dzidziso yakadzama uye, pazvinogoneka, vashande nenyanzvi dzenhamba. Iyi nzira yekubatana inogona kuvandudza zvakanyanya kuoma uye kurongeka kwetsvakiridzo yako.

Kugadzira kufungidzira uye kugadzira tsvagiridzo

Kuvaka pahunyanzvi hwepamusoro hwehuwandu hwakakurukurwa pakutanga, chikamu chino chinokutungamira kuburikidza nekushandisa kwavo kunoshanda muhurongwa hwekutsvagisa. Kubva pakushandisa multivariate ongororo mumagadzirirwo ekuedza kusvika pakushandisa muchina kudzidza maalgorithms ekuongorora dhata rekubatana, isu tichaongorora maitiro ekubatanidza dhizaini yako yekutsvagisa nematurusi ehuwandu ekuongorora kunoshanda. Iwe unozodzidza kugadzira kufungidzira uye kuronga dhizaini yekutsvagisa inoenderana nezvinangwa zvako, kuve nechokwadi chekuti data raunounganidza rakakosha uye rakasimba.

Kunyora manhamba ekufungidzira

Kunyora manhamba ekufungidzira idanho rakakosha mukuita tsvagiridzo, richiisa hwaro hwekuferefeta kwakarongeka. Mafungiro anopa tsananguro dzingangoitika kana fungidziro dzinogona kuyedzwa nesainzi uye kubva kumubvunzo wekutsvagisa uye chidzidzo chemashure. Nekutaura zvakajeka ese ari maviri asina maturo uye mamwe maitiro ekufungidzira, vaongorori vanoisa hwaro hwekuongorora kana data ravo rinotsigira kana kuramba kufanotaura kwavo kwekutanga. Heano maitiro aya ma hypotheses anowanzo kurongeka:

• Null hypothesis (H0). Inofungidzira kuti hapana mhedzisiro kana mutsauko, uye inoedzwa zvakananga. Ndiyo fungidziro yakajairwa yekuti hapana hukama pakati pezviyeredzwa zviviri zvakayerwa.
• Alternative hypothesis (H1). Inoisa mhedzisiro, musiyano, kana hukama, uye inogamuchirwa kana iyo null hypothesis yarambwa.

Iyi nzira yehuviri-hypothesis inobatsira mukugadzirisa bvunzo dzenhamba uye kuchengetedza chinangwa mukutsvagisa nekuisa yakatarwa nzira yekutonga, yakakosha pakuvimbika uye kuvimbika kwezviwanikwa.

Mienzaniso yekufungidzira kwezvidzidzo zvekuedza uye zvekubatana:

• Null hypothesis (yekuedza). Kuunza maekisesaizi ekurangarira zuva nezuva munzvimbo yebasa hakuzovi nemhedzisiro pamazinga ekushushikana kwevashandi. • Alternative hypothesis (yekuedza). Kuunza maekisesaizi ekurangarira zuva nezuva munzvimbo yebasa kunoderedza kushushikana kwevashandi. • Null hypothesis (correlational). Iko hakuna hukama pakati penguva yekurangarira tsika uye hutano hwebasa-hupenyu hwepakati pakati pevashandi. • Alternative hypothesis (yakabatana). Nguva yakareba yekudzidzira pfungwa inobatanidzwa nehupenyu huri nani hwekushanda-hupenyu pakati pevashandi.

Kuronga Dhizaini Yako Yekutsvagisa

Dhizaini yakasimba yekutsvagisa yakakosha kune chero chidzidzo, ichitungamira kuti data rinounganidzwa sei uye nekuongororwa kusimbisa fungidziro dzako. Sarudzo yedhizaini — ingave inotsanangura, yekubatanidza, kana yekuyedza-inonyanya kukanganisa nzira dzekuunganidza data uye nzira dzekuongorora dzinoshandiswa. Zvakakosha kuenzanisa dhizaini nezvinangwa zvechidzidzo chako kuti ugadzirise zvinobudirira mibvunzo yako yetsvagiridzo, uye zvakakoshawo kuti unzwisise nzira dzakananga dzinozoshandiswa mukuita.

Mhando yega yega yedhizaini yekutsvagisa ine basa chairo, ringave rekuyedza mazano, kuongorora mafambiro, kana kutsanangura zviitiko pasina kuratidza chikonzero-ne-mhedzisiro hukama. Kuziva mutsauko uripo pakati pemagadzirirwo aya kwakakosha pakusarudza iyo yakanakisa yezvido zvako zvekutsvagisa. Heano mhando dzemagadzirirwo ekutsvaga:

• Ongororo yekugadzira. Edza chikonzero-uye-mhedzisiro hukama nekugadzirisa zvinosiyana uye nekutarisa zvabuda.
• Correlational magadzirirwo. Ongorora hukama hunogona kuitika pakati pezvakasiyana pasina kuzvishandura, zvichibatsira mukuziva mafambiro kana masangano.
• Magadzirirwo anotsanangura. Tsanangura maitiro ehuwandu kana chiitiko pasina kuedza kumisa hukama hwechikonzero-nemhedzisiro.

Mushure mekusarudza nzira yaunosangana nayo patsvagiridzo yako, zvakakosha kuti unzwisise nzira dzakasiyana dzinotsanangura marongero aungaita chidzidzo chako padanho rinoshanda. Aya maitiro anotsanangura kuti vatori vechikamu vanoiswa mumapoka uye nekuongororwa sei, izvo zvakakosha kuti uwane mhedzisiro uye ine mhedzisiro maererano nedhizaini yako yaunosarudza. Pano, isu tinodonongodza mamwe marudzi ekutanga edhizaini anoshandiswa mukati meyakafara nzira dzekutsvagisa:

• Pakati-zvidzidzo kugadzira. Inofananidza mapoka akasiyana evatori vechikamu ari pasi pemamiriro akasiyana. Zvinonyanya kukosha pakuona kuti marapirwo akasiyana anokanganisa sei mapoka akasiyana, zvichiita kuti zvive zvakanakira zvidzidzo apo kushandisa mamiriro akafanana kune vese vari kutora chikamu hazvigoneke.
• Mukati-zvidzidzo kugadzira. Inobvumira vaongorori kuona boka rimwechete revatori vechikamu pasi pemamiriro ese. Iyi dhizaini inobatsira pakuongorora shanduko nekufamba kwenguva kana mushure mekupindira kwakananga mukati mevanhu vakafanana, kuderedza mutsauko unobva mukusiyana pakati pevatori vechikamu.
• Yakasanganiswa dhizaini. Inobatanidza zvinhu zvezvose zviri pakati- uye mukati-zvidzidzo magadzirirwo, ichipa ongororo yakazara pane akasiyana siyana uye mamiriro.

Mienzaniso yezvidzidzo zvekugadzira tsvakurudzo:

Kuratidza kuti magadzirirwo aya anoshanda sei mukutsvagisa kwepasirese, funga zvinotevera mashandisirwo: • Ongororo yekugadzira. Ronga chidzidzo apo vashandi vanotora chikamu muchirongwa chekufunga, vachiyera kushushikana kwavo kusati kwaitwa uye mushure mechirongwa kuti vaongorore maitiro ayo. Izvi zvinofambirana neyekuyedza hypothesis ine chekuita nekushushikana. • Correlational design. Ongorora vashandi pane yavo yemazuva ese yekufunga kwekudzidzira nguva uye vaenderane izvi nekuzvizivisa kwavo-yehupenyu hwebasa-yehupenyu chiyero chekuongorora maitiro. Izvi zvinoenderana neiyo correlational hypothesis nezve nguva yekufunga uye yebasa-hupenyu chiyero.

Nekuona kuti nhanho yega yega yekuronga kwako yakanyatsotariswa, unovimbisa kuti inotevera kuunganidzwa kwedata, ongororo, uye dudziro yezvikamu zvakavakwa pahwaro hwakasimba, hunoenderana zvakanyanya nezvinangwa zvako zvekutanga zvekutsvagisa.

Kuunganidza sampuli data yekuongorora kwenhamba

Mushure mekuongorora matekiniki ehuwandu uye kuronga tsvakiridzo yako, isu tave kusvika padanho rakakosha mukuita kwekutsvaga: kuunganidza data. Kusarudza sampuli chaiyo kwakakosha, sezvo inotsigira chokwadi uye kushanda kwekuongorora kwako. Iyi nhanho haingotsigire fungidziro dzakaitwa kare asi zvakare inoisa hwaro kune ese anotevera ongororo, zvichiita kuti ive yakakosha kuburitsa yakavimbika uye inoshanda zvakanyanya.

Maitiro ekuita sampling

Kusarudza nzira yesampling yakakodzera kwakakosha pakutendeseka kwemhedzisiro yako yekutsvagisa. Isu tinoongorora nzira mbiri dzekutanga, imwe neimwe iine mabhenefiti akasiyana uye matambudziko:

• Probability sampling. Iyi nzira inovimbisa nhengo yese yevagari mukana wakaenzana wekusarudza, kuderedza kusarura kwesarudzo uye kuvandudza kumiririra kwemuenzaniso. Inosarudzwa kune zvidzidzo uko generalizability kune yakafara ruzhinji kwakakosha. Iyi nzira inotsigira ongororo yakasimba yenhamba nekuona kuti zvakawanikwa zvinogona kutambidzwa zvakavimbika kune ruzhinji rwevanhu.
• Isinga-fungidziro sampling. Iyi nzira inosanganisira kusarudza vanhu zvichienderana nemaitiro asina kujairika, akadai sekureruka kana kuwanikwa. Kunyange zvazvo nzira iyi ichinyanya kudhura, inogona kusapa muenzaniso wemumiriri wevanhu vose, zvingangounza zvisizvo zvinogona kukanganisa zvakabuda muchidzidzo.

Kunyangwe paine mukana wekurerekera, kusaita sampling kunoramba kuchikosha, kunyanya kana kuwana ruzhinji rwese kuchinetsa kana kana zvinangwa zvetsvagiridzo zvisingade zvakakura. Kunyatsonzwisisa kuti nzira iyi inoshandiswa rini uye sei kwakakosha kuti usashandise zvisizvo uye kududzira zvisizvo, tichiva nechokwadi chekuti mhedziso dzinotorwa dzinoshanda mukati mechirevo chakataurwa.

Kushandisa nzira dzinoshanda dzesampling dzekuongorora kwenhamba

Anoshanda sampling masaramusi kuwanikwa kwezviwanikwa pamwe nekudiwa kweiyo yakasimba, inomiririra sampuli:

• Resource kuwanikwa. Tarisa kuti ndezvipi zviwanikwa nerutsigiro rwauinarwo, sezvo izvi zvichizoona kana uchigona kushandisa nzira dzekutsvaga dzinosvika kure kana kana uchida kuvimba nenzira dzakareruka, dzakachipa.
• Kusiyana kwevanhu. Edza semuenzaniso unoratidza kusiyana kwehuwandu hwevanhu kuti uvandudze kushanda kwekunze, kunyanya zvakakosha mumamiriro akasiyana.
• Nzira dzekupinza basa. Sarudza nzira dzinoshanda dzekubatanidza vangangove vatori vechikamu, senge dhijitari ads, kudyidzana nemasangano edzidzo, kana nharaunda nharaunda, zvichienderana nekwauri kutarisisa.

Kuve nechokwadi chekukwana kwemuenzaniso wekuongorora kwenhamba

Usati wapedzisa vatori vechikamu vako, ita shuwa saizi yako yemuenzaniso yakakwana kuti ipe simba rekuverenga rakavimbika:

• Sample size calculators. Shandisa maturusi epamhepo kuona kuti vangani vatori vechikamu vaunoda, uchifunga nezvehukuru hunotarisirwa hwemhedzisiro yauri kudzidza, kuvimba kwaunoda kuve mumhedzisiro yako, uye yako yakasarudzwa nhanho yechokwadi, inowanzoiswa pa5%. Zvishandiso izvi zvinowanzoda kuti iwe uise fungidziro yehukuru hwemhedzisiro kubva kuzvidzidzo zvekare kana bvunzo dzekutanga.
• Kugadzirisa kusiyanisa. Kana kudzidza kwako kuchisanganisira akawanda madiki kana madhizaini akaomarara, tarisa musiyano mukati nepakati pemapoka paunenge uchisarudza saizi inodiwa. Kusiyanisa kwepamusoro kazhinji kunoda masampuli akakura kuti aone mhedzisiro chaiyo.

Chaiyo-nyika mashandisirwo esampling matekiniki

Kuenderana nenhaurirano dzekutanga pamagadzirirwo ekutsvagisa, heino mienzaniso inoshanda yemasampling application:

• Kuedza sampling. Chidzidzo chekuongorora mhedzisiro yekurangarira maekisesaizi pamazinga ekushushikana kwevashandi chinosanganisira vashandi kubva kumadhipatimendi akawanda kuti ive nechokwadi chekuti sampuli inoratidza huwandu hwemabasa uye mazinga evakuru. Izvi zvakasiyana-siyana zvinobatsira mukugadzirisa zvakawanikwa munzvimbo dzakasiyana dzebasa rekuongorora nhamba. • Correlational sampling. Kuti uongorore hukama pakati pehurefu hwemaitiro ekufunga uye basa-hupenyu chiyero, shandisa masocial media mapuratifomu kunongedza vanhu vanogara vachidzidzira kufunga. Iyi nzira inofambisa inoshanda uye yakakodzera vatori vechikamu kubatanidzwa.

Pfupisa data rako nenhamba dzinotsanangura

Waunganidza data rako, nhanho inotevera yakakosha kuronga nekupfupikisa uchishandisa nhamba dzinotsanangura. Iyi nhanho inorerutsa iyo data mbishi, ichiita kuti igadzirire yakadzama ongororo yenhamba.

Kuongorora data rako

Chekutanga, ongorora data rako kuti ubate kugovera kwayo uye nekunongedza chero kunze, izvo zvakakosha pakusarudza akakodzera maitiro ekuongorora:

• Frequency kugovera matafura. Nyora kuti kangani kukosha kwega kwega kunooneka, izvo zvinobatsira kuziva mhinduro dzakajairika kana dzisingawanzo, sekuwanda kwemamwe mazinga ekushushikana pakati pevashandi muchidzidzo chedu chekufunga.
• Bar chart. Inobatsira pakuratidza kugoverwa kwe data data, semuenzaniso, madhipatimendi anobatanidzwa mukudzidza kwekurangarira.
• Kuparadzira zvirongwa. Aya marongero anogona kuratidza hukama pakati pezvinosiyana, senge chinongedzo pakati penguva yekufunga kwekudzidzira uye kuderedza kushushikana.

Ongororo iyi inobatsira kuona kana data rako rakajairwa kana kugovaniswa zvisina tsarukano, zvichitungamira sarudzo yako yekutevera bvunzo dzehuwandu.

Kuverenga zviyero zvepakati maitiro

Aya ma metrics anopa ruzivo mukati mepakati kukosha kwedataset yako:

• fashoni. Iyo inonyanya kuitika kukosha. Semuenzaniso, iyo yakajairika nhanho yekuderedza kushushikana inoonekwa muvatori vechikamu.
• Median. Iko kukosha kwepakati ndipo apo ese mapoinzi data akaiswa. Izvi zvinobatsira, kunyanya kana data rako rakatsveyamiswa.
• Kureva. Ivhareji kukosha inogona kupa mhedziso yemazinga ekushushikana pre- uye post-mindfulness zvikamu.

Kuverenga zviyero zvekusiyana

Aya manhamba anotsanangura kuti yakawanda sei data yako inosiyana:

• dungwerungwe. Inoratidza span kubva papasi kusvika pahukoshi hwepamusoro, zvichiratidza kusiyana kwekuita kwekurangarira.
• Interquartile range (IQR). Inobata yepakati 50% yedata rako, ichipa mufananidzo wakajeka wepakati maitiro.
• Standard kutsauka uye kusiyana. Aya matanho anoratidza kuti mapoinzi e data anotsauka sei kubva pazvinoreva, zvinobatsira pakunzwisisa kusiyana kwemhedzisiro yekuderedza kushushikana.

Mienzaniso yenhamba dzinotsanangura dziri kushandiswa

Kuratidza kuti nhamba idzi dzinoshandiswa sei:

• Kuedza kugadzirisa. Fungidzira iwe wakaunganidza pre-test uye post-yedzo yekushushikana nhanho mamakisi kubva kuvashandi vari kudzidziswa kungwarira. Kuverenga zvinoreva uye kwakajairwa kutsauka kunobatsira kuseta shanduko mumazinga ekushushikana pamberi uye mushure mechirongwa:

Chiyero	Kureva stress score	Kusiyana kwekutsauka
Pre-test	68.4	9.4
Post-test	75.2	9.8

Migumisiro iyi inoratidza kuderera kwekushungurudzika, tichifunga kuti zvibodzwa zvepamusoro zvinoratidza kuderera kwekunetseka. Kuenzanisa kwakasiyana kunogona kuratidza kukosha kwekuchinja uku.

• Correlational kudzidza. Paunenge uchiongorora hukama pakati pekurangarira kudzidzira nguva uye kugara zvakanaka, iwe unoongorora kuti aya akasiyana anopindirana sei:

tsananguro	ukoshi
Avhareji yenguva yekudzidzira	Maminitsi makumi maviri nemashanu pachikamu
Avhareji yehutano hwakanaka	3.12 kunze kwe5
Kubatana kwakaringana	Kuverengerwa

Iyi nzira inojekesa kusimba kwehukama pakati penguva yekudzidzira uye kugara zvakanaka.

Nekupfupisa data rako zvinobudirira, unoisa hwaro hwakasimba hwekumwe kuongorora kwenhamba, uchifambisa mhedziso dzine njere pamusoro pemibvunzo yako yekutsvagisa.

Ongorora data rako neinferential statistics

Mushure mekupfupisa data rako nenhamba dzinotsanangura, danho rinotevera nderekutora mhedziso pamusoro pehuwandu hwehuwandu uchishandisa inferential statistics. Iyi nhanho inoedza fungidziro dzakagadzirwa panguva yekuronga tsvakiridzo chikamu uye inodzamisa ongororo yehuwandu.

Kuedza kufungidzira uye kuita fungidziro

Inferential statistics inobvumira vaongorori kufanotaura hunhu hwehuwandu zvichienderana nesample data. Nzira dzakakosha dzinosanganisira:

• Kufungidzira. Kuita fungidziro dzakadzidziswa nezvehuwandu hwevanhu paramita, idzo dzinoratidzwa se:
  • Zvinofungidzirwa. Single values ​​inomiririra parameter, seyerevo yekushushikana mwero.
  • Interval fungidziro. Marenji anogona kunge achisanganisira iyo parameter, ichipa buffer yekukanganisa uye kusagadzikana.
• Hypothesis kuyedza. Kuedza kufanotaura nezvehuwandu hwehuwandu hunoenderana nemuenzaniso data. Izvi zvinotanga nekutenda kuti hapana mhedzisiro iripo (null hypothesis) uye inoshandisa ongororo yenhamba kuona kana izvi zvichigona kurambwa nekuda kwekuonekwa mhedzisiro (alternative hypothesis).

Kukosha kwenhamba kunoongorora kana zvawanikwa zvichikonzerwa nemukana. A p-value isingasviki 0.05 kazhinji inoratidza mhedzisiro yakakosha, ichipa humbowo hwakasimba hunopesana neiyo null hypothesis.

Kuita ongororo dzenhamba

Sarudzo yezviyedzo zvenhamba inogadzirirwa dhizaini yekutsvagisa uye data data:

• Paired t-bvunzo. Inoongorora shanduko muzvidzidzo zvakafanana pamberi uye mushure mekurapa, yakanakira pre-yekuongorora uye mushure mebvunzo kuenzanisa muzvidzidzo senge kupindira kwedu kwepfungwa.
  • muenzaniso. Kuenzanisa kushushikana kwezvibodzwa zvisati zvaitika (Zvinoreva = 68.4, SD = 9.4) uye mushure (Zvinoreva = 75.2, SD = 9.8) kudzidziswa kwekufungisisa kuti uongorore kuchinja kukuru.
• Correlation test. Inoyera simba rekubatana pakati pezvinhu zviviri zvakasiyana, senge nguva yekudzidzira pfungwa uye kugara zvakanaka.
  • Pearson correlation bvunzo. Inotaridza kuti shanduko yenguva yekufunga inoenderana sei nekuchinja kwehutano hwevashandi.

Mienzaniso inoshanda uye mamiriro ezvinhu

• Ongororo yekuongorora. Kushandisa paired t-test paruzivo rwekurangarira kunoratidza kuderera kukuru kwemazinga ekushushikana, net-value ye3.00 uye p-value ye0.0028, zvichiratidza kuti kudzidziswa kwekufunga kunobatsira kuderedza kushushikana kwebasa. Kuwana uku kunotsigira kushandiswa kwemaitiro ekugara achifunga sekupindira kunobatsira pakuderedza kushushikana munzvimbo yebasa. • Correlational kudzidza. Kubatana kwakanaka kwepakati (r = 0.30) kwakasimbiswa nekuongororwa kwenhamba (t-value = 3.08, p-value = 0.001) inoratidza kuti nguva refu yekufunga inovandudza hutano. Kuwedzera nguva yenguva yekurangarira kunogona kuvandudza hutano hwese pakati pevashandi.

Tichifunga nezvekufungidzira uye mafambiro emangwana

Kuti tinyatsonzwisisa zvinorehwa nezvatinowana, zvakakosha kuti ticherechedze fungidziro dziripo uye nzira dzinogona kuitika dzekuenderera mberi nekuongorora:

• Fungidziro nemiganhu. Kuvimbika kwemhedzisiro yedu kunoenderana nekufungidzira kuti data rinotevera yakajairika pateni uye imwe neimwe data point yakazvimirira pane imwe. Kana iyo data, senge zvibodzwa zvekushushikana, ikasatevedzera iyi yakajairwa patani, inogona kutenderedza mibairo uye inogona kutungamira kune zvisiri izvo.
• Zvinhu zvinooneka. Kubatanidza magirafu nematafura anoratidza kugoverwa kwemashure ekuongorora uye mushure mekuedzwa, pamwe chete nehukama huri pakati penguva yekufunga kwekuita uye kugara zvakanaka, zvinokurudzirwa kuita kuti zvakawanikwa zvijeke uye zvibatane. Izvi zvinoonekwa zvinobatsira kuratidza maitiro akakosha uye mapatani, kuvandudza kududzira kweiyo data.
• Kuwedzera kutsvagisa. Zvidzidzo zvenguva yemberi zvinogona kuongorora zvimwe zvinhu zvinokanganisa kugara zvakanaka uchishandisa multivariate ongororo kana machine learning. Izvi zvinogona kuburitsa ruzivo rwakadzama mumhando dzakasiyana dzinopesvedzera kuderedza kushushikana.
• Kuongorora kwepamusoro. Kushandisa maitiro akawanda ekudzoreredza kunogona kubatsira kunzwisisa kuti zvinhu zvakasiyana zvinosangana sei kukanganisa kushushikana uye kugara zvakanaka, zvichipa maonero akazara emhedzisiro yekufunga.

Nekugadzirisa fungidziro idzi uye kuongorora mafambiro aya, unovandudza manzwisisiro ako ekubudirira kwekurangarira kupindira, kutungamira tsvakiridzo yeramangwana uye kuzivisa sarudzo dzemitemo.

Kududzira zvaunowana

Magumo ekuongorora kwako kwenhamba anosanganisira kududzira zvaunowana kuti unzwisise zvazvinoreva uye zvine chekuita nekufungidzira kwako kwekutanga.

Kunzwisisa kukosha kwenhamba

Statistical kukosha kwakakosha mukuyedzwa kwekufungidzira, zvichibatsira kutsanangura kana mhedzisiro ingangoita nekuda kwemukana. Iwe unoisa izvi nekuenzanisa yako p-kukosha kunopesana neyakatemerwa chikumbaridzo (kazhinji 0.05).

Heino mienzaniso inoshanda kubva kuongororo yedu yekufunga kuratidza kuti kukosha kwenhamba kunodudzirwa sei:

• Ongororo yekuedza. Nekuda kwekuchinja kwedanho rekushushikana mukudzidza kwekurangarira, p-value ye0.0027 (pazasi pechikumbaridzo 0.05) inotitungamira kuti tirambe fungidziro isina maturo. Izvi zvinoratidza kudzikiswa kwakanyanya kwekushushikana kunokonzerwa nekuita kwekurangarira, kwete kungosiyana kwakangoitika. • Ongororo yeCorrelational. P-value ye0.001 muchidzidzo ichiongorora nguva yekufunga uye kugara zvakanaka inoratidza kuwirirana kwakakosha, kutsigira pfungwa yekuti nguva refu inosimudzira kugara zvakanaka, kunyangwe zvisingareve kukonzeresa.

Kuongorora mhedzisiro saizi

Effect saizi inoyera kusimba kwemhedzisiro, ichisimbisa kukosha kwayo kwekuita kupfuura kungoiratidza nenhamba. Pazasi, iwe unogona kuona mienzaniso yehukuru hwekuita kubva pachidzidzo chedu chekufunga:

• Effect size in experimental research. Kuverengera Cohen's d yekuchinja kwemazinga ekushushikana nekuda kwekurangarira, unowana kukosha kwe0.72, ichikurudzira yepakati kusvika kune yakakwirira inoshanda maitiro. Izvi zvinoratidza kuti kurovedza pfungwa hakungodzikisi kushushikana chete nenhamba asi kunodaro kusvika padanho rine musoro mukuita. Kune avo vasina kujairana neCohen's d, inoyera saizi yemusiyano pakati penzira mbiri maererano neyakajairwa kutsauka kweiyo data data. Heino gwara pfupi rekududzira Cohen's d.
• Effect size in correlational research. Tichifunga nezvemaitiro aCohen, iyo Pearson's r kukosha kwe0.30 inowira muchikamu chepakati chimiro saizi. Izvi zvinoratidza kuti nguva yekudzidzira pfungwa ine mwero, hukama hwakakosha nekugara zvakanaka kwevashandi. Pearson's r inoyera simba remubatanidzwa wemutsara pakati pezviviri zvakasiyana. Kuti uwane zvakawanda pamusoro pePearson's r nekududzirwa kwayo, tinya pano.

Kufunga kukanganisa mukuita sarudzo

Mukuongorora kwenhamba, zvakakosha kuti urangarire kukanganisa kwesarudzo, izvo zvinogona kukanganisa zvakanyanya mhedziso dzakatorwa kubva mu data rekutsvagisa:

• Type I kukanganisa zvinoitika kana iwe ukaramba zvisirizvo iyo yechokwadi null hypothesis, pamwe ichikurudzira kuti chirongwa chinoshanda kana chisiri. Izvi zvinowanzonzi "false positive".
• Type II kukanganisa zvinoitika kana iwe ukatadza kuramba manyepo null hypothesis, ingangopotsa iwo chaiwo mhedzisiro yekupindira, inozivikanwa se "manyepo asina kunaka."

Kuenzanisa njodzi dzezvikanganiso izvi kunosanganisira kunyatsotarisisa kukosha uye kuve nechokwadi chesimba rakakwana mukugadzira kwako kudzidza. Matanho ekudzikisa zvikanganiso izvi anosanganisira:

• Kuwedzera saizi yemuenzaniso. Sampuli dzakakura dzinoderedza huwandu hwekukanganisa uye kuwedzera simba rechidzidzo, izvo zvinodzikisira mukana wekuita Type II zvikanganiso.
• Kushandisa mazinga akakodzera ekukosha. Kugadzirisa iyo alpha level (semuenzaniso, kubva 0.05 kusvika 0.01) inogona kuderedza mukana weType I zvikanganiso, kunyangwe izvi zvinogona zvakare kuderedza simba rekuona chaiwo maitiro kunze kwekunge saizi yemuenzaniso yakagadziriswa zvinoenderana.
• Kuita ongororo yesimba. Usati waunganidza data, kuita ongororo yesimba kunobatsira kuona hushoma saizi yemuenzaniso inodiwa kuona mhedzisiro yehukuru hwakapihwa ine nhanho inodiwa yekuvimba, nokudaro kugadzirisa ese ari maviri Type I uye Type II kukanganisa kukanganisa.

Kuve nechokwadi chekuvimbika mune zvedzidzo

Mushure mekududzira zvawawana uye musati mapedza tsvakiridzo yenyu, zvakakosha kuti muve nechokwadi chekuvimbika nekururama kwebasa renyu. Shandisa yedu plagiarism checker kuti usimbise mavambo ekuongorora kwako uye chirevo chakakodzera chezvinyorwa. Ichi chishandiso chepamberi chinopa yakadzama yakafanana mamaki, inoshandisa yakaomesesa algorithms kuona zvisizvo zviitiko zve. kubirira, uye inosanganisira chibodzwa chenjodzi chinoratidza mukana wekuti zvikamu zvekuongorora kwako zvionekwe sezvisiri izvo. Inoitawo ongororo yekuona kuti mareferensi ese anonyatso zivikanwa, ichisimbisa kuvimbika kwetsvagiridzo yako iyo yakakosha mune zvese zvedzidzo uye zvehunyanzvi zvigadziriso.

Uyezve, yedu document revision service nyatsoongorora gwaro rako rakanyorwa, uchigadzirisa zvikanganiso zvegirama nezviratidzo kuti uve nechokwadi chekujeka uye kusachinja-chinja. Vagadziri vedu vane hunyanzvi havangoverenge zvinyorwa zvako asi zvakare vanovandudza kuyerera uye kuverenga, zvichiita kuti ongororo yako yenhamba iwedzere kumanikidza uye nyore kunzwisisa. Nekukwenenzvera zvirimo, chimiro, mutauro, uye chimiro, tinokubatsira kuti utaure zvaunowana zvinobudirira kune vateereri vako.

Kubatanidza masevhisi aya kunosimudzira kuvimbika kwezvawawana, kunowedzera kuomarara kwesainzi, uye kunosimudzira kuratidzwa kwetsvakiridzo yako mukuongorora kwenhamba. Uku kutarisisa kune zvakadzama kunovimbisa kuti gwaro rako rekupedzisira rinosangana nemhando yepamusoro yekuvimbika kwedzidzo uye kugona kwehunyanzvi.

Zvishandiso zveSoftware zvekunyatsoongorora nhamba

Sezvo isu tichiongorora mashandisirwo anoshanda uye theoretical pasi pekuongorora kwenhamba, kusarudza maturusi esoftware akakodzera kunoratidzika kwakakosha. Zvishandiso izvi zvinonatsiridza kushanda nekudzika kwetsvagiridzo yako uye zvinobvumira ongororo dzakanyanya uye dzakajeka kunzwisisa. Pazasi, isu tinodonongodza mamwe eanonyanya kushandiswa manhamba esoftware maturusi, achitsanangura masimba avo uye akajairwa ekushandisa makesi kukubatsira iwe kusarudza yakanyanya kukwana kune zvaunoda.

R

R inzvimbo yemahara software yakatsaurirwa kune manhamba komputa uye mifananidzo. Inozivikanwa nekuwanda kwayo kwepakeji uye kugona kwakasimba mukuomarara kwenhamba yekuenzanisa, R inonyanya kubatsira kune vaongorori vanoda maitiro epamusoro ehuwandu. Iyo inotsigira yakakura kugadzirisa uye yakadzama graphical inomiririra, ichiita kuti ive yakanaka kune yakaoma ongororo.

Python

Kureruka kwePython uye kuita kwakasiyana-siyana kwaita kuti ive nhanho mukuongororwa kwenhamba, inotsigirwa nemaraibhurari seNumPy, SciPy, uye pandas. Mutauro uyu wakakwana kune avo vanotanga mukuongorora data, vachipa yakatwasuka syntax uye ine simba data manipulation kugona. Python inokunda mumapurojekiti anobatanidza kudzidza kwemichina uye hukuru-hukuru hwekuongorora data.

SPSS (Statistical package yesocial science)

SPSS inofarirwa kune yayo mushandisi-inoshamwaridzika interface, ichiita yakaomesesa ongororo yenhamba kuti iwanikwe kune vanotsvaga vasina ruzivo rwakakura rwehurongwa. Inonyanya kushanda pakuongorora dhata uye kumwe kutsvagisa kunowanzoitwa musocial science. Yayo Graphical User Interface (GUI) inobvumira vashandisi kuita bvunzo dzenhamba kuburikidza nemamenu akareruka uye dialog mabhokisi, pane yakaoma coding, zvichiita kuti ive yakavimbika uye intuitive chishandiso cheanotsanangura manhamba.

SAS (Statistical analysis system)

SAS inonyanya kuzivikanwa nekuvimbika kwayo mune advanced analytics, bhizinesi njere, uye manejimendi data, zvichiita kuti ive sarudzo inosarudzika mumaindasitiri akaita sehutano nemishonga. Iyo inonyatso gadzirisa mahombe dataseti uye inopa yakadzama kuburitsa kune multivariate ongororo, iyo yakakosha pakuona chokwadi uye kuenderana kwezvawawana.

Kuenzanisa kupfupisa kwenhamba yekuongorora software

Software	Strengths	Mashandisiro akajairika	mutengo	User community
R	Yakakura mapakeji, advanced modeling	Complex statistical analysis	Free	Yakakura, inoshanda
Python	Versatility, nyore kushandisa	Kudzidza kwemichina, hukuru-hukuru hwekuongorora data	Free	Zvakawanda, zviwanikwa zvakawanda
SPSS	Mushandisi-ane hushamwari GUI, yakanaka kune vanotanga	Ongororo data, inotsanangura nhamba	Paid	Inotsigirwa zvakanaka neIBM, academia
SAS	Inobata datasets hombe, kuburitsa kwakasimba	Healthcare, mishonga	Paid	Nyanzvi, indasitiri yakasimba

Kutanga ne statistical software

Kune avo vatsva kune aya maturusi, akawanda online tutorials uye zviwanikwa zvinogona kubatsira kuvhara mukaha pakati peruzivo rwe theoretical uye inoshanda application:

• R. Vatangi vekutanga vanofanirwa kutanga nepakati R pasuru, vachibata izvo zvekutanga zvevheji, matrices, uye data mafuremu. Kuongorora mamwe mapakeji kubva kuCRAN, senge ggplot2 yemifananidzo yepamusoro kana kutarisira kudzidza muchina, kunogona kuwedzera kuvandudza kugona kwako kwekuongorora.
• Python. Tanga nekutanga Python tutorials pa Python.org. Mushure mekudzidza izvo zvekutanga, isa maraibhurari ekuongorora data sePandas uye maraibhurari ekuona seMatplotlib kuti uwedzere hunyanzvi hwako hwekuongorora.
• SPSS. IBM, iyo kambani yakagadzira SPSS, inopa akadzama zvinyorwa uye yemahara miedzo kubatsira vashandisi vatsva kunzwisisa kugona kweSPSS, kusanganisira yayo Syntax Mharidzo yemabasa otomatiki. Kuwana uku kunonyanya kubatsira kune avo vatsva kune manhamba software, ichipa mushandisi-ane hushamwari sumo kune yakaoma manhamba mabasa.
• SAS. Iyo SAS University Edition inopa yemahara yekudzidza chikuva, chakanakira vadzidzi nevatsvaguri vanotarisa kudzamisa kunzwisisa kwavo kweSAS hurongwa uye ongororo yenhamba.

Nekusarudza software yakakodzera uye nekupa nguva yekudzidza mashandiro ayo, unogona kuvandudza zvakanyanya kunaka uye chiyero chewongororo yako yenhamba, zvichitungamira kune mhedziso dzine hungwaru uye mhedzisiro yetsvakiridzo ine simba.

mhedziso

Gwaro iri rakasimbisa basa rakakosha rekuongororwa kwenhamba mukushandura data yakaoma kuita maonero anogoneka munzvimbo dzakasiyana siyana. Kubva pakugadzira fungidziro uye kuunganidza data kusvika pakuongorora nekududzira zvabuda, nhanho yega yega inovandudza yako yekuita sarudzo uye hunyanzvi hwekutsvagisa-yakakosha pakuvandudza mudzidzo nehunyanzvi. Kubata maturusi ezviverengero akaita seR, Python, SPSS, uye SAS kunogona kuve kwakaoma, asi mabhenefiti-akapinza njere, sarudzo dzakangwara, uye kutsvagisa kwakasimba-kwakakosha. Chishandiso chega chega chinopa hunyanzvi hwekugona kubata yakaoma data kuongororwa zvinobudirira. Batanidza hupfumi hwezviwanikwa zvepamhepo, zvidzidzo, uye rutsigiro rwenharaunda kunatsa hunyanzvi hwako hwehuwandu. Izvi zviwanikwa zvinorerutsa kuoma kwekuongorora kwenhamba, kuve nechokwadi kuti unogara uine hunyanzvi. Nekurodza hunyanzvi hwako hwekuongorora manhamba, unovhura mikana mitsva mukutsvaga kwako uye hupenyu hwehunyanzvi. Ramba uchidzidza nekushandisa matekiniki aya, uye rangarira — yese dataset ine nyaya. Nematurusi akakodzera, wakagadzirira kuzvitaura zvine musoro.