探索的データ分析のためのチャートの選び方

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୳ࡧతσʔλ෼ੳͷͨΊͷ νϟʔτͷબͼํ

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EXPLORATORY

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3 εϐʔΧʔ ੢ా צҰ࿠ CEO EXPLORATORY ུྺ 2016೥ɺσʔλαΠΤϯεͷຽओԽͷͨΊɺExploratory, Inc Λ ্ཱͪ͛Δɻ Exploratory, Inc.ͰCEOΛ຿ΊΔ͔ͨΘΒɺσʔλαΠΤϯεɾ ϒʔτΩϟϯϓɾτϨʔχϯάͳͲΛ௨ͯ͠γϦίϯόϨʔͰ ߦΘΕ͍ͯΔ࠷ઌ୺ͷσʔλαΠΤϯεͷීٴͱڭҭʹऔΓ૊ Ήɻ ถΦϥΫϧຊࣾͰɺ16೥ʹΘͨΓσʔλαΠΤϯεͷ։ൃνʔ ϜΛ཰͍ɺػցֶशɺϏοάɾσʔλɺϏδωεɾΠϯςϦδΣ ϯεɺσʔλϕʔεʹؔ͢Δ਺ଟ͘ͷ੡඼ΛੈʹૹΓग़ͨ͠ɻ @KanAugust

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Vision ΑΓΑ͍ҙࢥܾఆΛ͢ΔͨΊʹ σʔλΛ࢖͏͜ͱ͕౰ͨΓલʹͳΔ

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Mission σʔλαΠΤϯεͷຽओԽ

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6 ୈ̏ͷ೾ σʔλαΠΤϯεɺAIɺػցֶश͸౷ܭֶऀɺ։ൃऀͷͨΊ͚ͩͷ΋ͷͰ͸͋Γ·ͤΜɻ σʔλʹڵຯͷ͋ΔਓͳΒ୭΋͕ੈքͰ࠷ઌ୺ͷΞϧΰϦζϜΛ࢖ͬͯ ϏδωεσʔλΛ؆୯ʹ෼ੳͰ͖Δ΂͖Ͱ͢ɻ Exploratory͕ͦ͏ͨ͠ੈքΛՄೳʹ͠·͢ɻ

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ୈ1ͷ೾ ୈ̎ͷ೾ ୈ̏ͷ೾ ϓϥΠϕʔτ(ߴ͍/ݹ͍) Φʔϓϯɾιʔε(ແྉ/࠷ઌ୺) UI & ϓϩάϥϛϯά ϓϩάϥϛϯά 2016 2000 1976 ϚωλΠθʔγϣϯ ίϞσΟςΟԽ ຽओԽ ౷ܭֶऀ σʔλαΠΤϯςΟετ Exploratory ΞϧΰϦζϜ Ϣʔβʔɾ ମݧ πʔϧ Φʔϓϯɾιʔε(ແྉ/࠷ઌ୺) UI & ࣗಈԽ ϏδωεɾϢʔβʔ ςʔϚ σʔλαΠΤϯεͷຽओԽ

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質問 ExploratoryͰ؆୯ʹͰ͖ΔλεΫ 伝えるデータアクセスデータラングリング可視化アナリティクス統計/機械学習 UI

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୳ࡧతσʔλ෼ੳͷͨΊͷ νϟʔτͷબͼํ

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質問伝えるデータアクセスデータラングリング可視化アナリティクス統計/機械学習

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༧ଌ·ͨ͸কདྷΛίϯτϩʔϧ͢ΔͨΊʹɺσʔλʹରͯ͠ ͨ͘͞Μͷ࣭໰Λ౤͔͚͛ɺ౴͑Λ୳͍ͯ͘͜͠ͱͰɺΑΓ Α͍ԾઆΛߏங͍ͯͨ͘͠Ίͷ෼ੳɻ 11 EDAʢ୳ࡧతσʔλ෼ੳʣ

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12 ࣭໰ ౴͑ σʔλ Ծઆ EDAʢ୳ࡧతσʔλ෼ੳʣ

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Why σʔλ෼ੳʁ 13

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14 ΰʔϧ ϏδωεΛ੒௕

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15 ΰʔϧ ϏδωεΛ੒௕ ސ٬Λ૿΍͢ ໰୊

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16 ΰʔϧ ໰୊ͷ਺஋Խ ϏδωεΛ੒௕ ސ٬Λ૿΍͢ ސ٬ͷ਺ ໰୊

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17 ސ٬ͷ਺͕ԿਓʹͳΔͷ͔༧ଌ͍ͨ͠ ༧ଌ

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18 ސ٬ͷ਺Λ1000ਓʹ͍ͨ͠ɻ ίϯτϩʔϧ

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19 ༧ଌ ίϯτϩʔϧ ༧ଌ͢Δ͜ͱͰকདྷʹ උ͍͑ͨɻ ίϯτϩʔϧͯ͠কདྷ Λࣗ෼ͷࢥ͏΋ͷʹ͠ ͍ͨɻ Կ͕༧ଌʹӨڹ͢Δͷ ͩΖ͏͔ɻ Ͳ͏͍͏ΞΫγϣϯΛ ͱΔͱ݁ՌʹӨڹ͢Δ ͩΖ͏͔ɻ

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20 ༧ଌ ίϯτϩʔϧ • ࣍ͷ3ϲ݄ͷؒɺԿਓ͘Β ͍ͷ٬͕དྷΔͷ͔ʁ • ؾԹ͕ஆ͔͚Ε͹ɺ٬͕૿ ͑ΔͰ͋Ζ͏ɻ • ٬ͷ਺Λ૿΍͢ʹ͸Կ͕ Ͱ͖Δ͔ʁ • 10ˋͷׂҾΛ͢Ε͹ɺ٬ ͕૿͑ΔͰ͋Ζ͏ɻ

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21 ༧ଌ ίϯτϩʔϧ • ࣍ͷ3ϲ݄ͷؒɺԿਓ͘Β ͍ͷ٬͕དྷΔͷ͔ʁ • ؾԹ͕ஆ͔͚Ε͹ɺ٬͕૿ ͑ΔͰ͋Ζ͏ɻ • ٬ͷ਺Λ૿΍͢ʹ͸Կ͕ Ͱ͖Δ͔ʁ • 10ˋͷׂҾΛ͢Ε͹ɺ٬ ͕૿͑ΔͰ͋Ζ͏ɻ Ծઆ

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22 ༧ଌ ίϯτϩʔϧ Ծઆ ༧ଌʹԿ͕ Өڹ͢Δͷ͔ʁ ίϯτϩʔϧ͢ΔͨΊͷ ҼՌؔ܎͸Կ͔ʁ

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23 Ծઆ σʔλ ςετ Ծઆ͸ςετͯ͠ਖ਼͍͔͠Ͳ͏͔νΣοΫ͢Δɻ ׂҾ͢Δͱސ٬͕ 100ਓ૿͑Δɻ

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24 Ծઆ σʔλ ςετ Ծઆ͸ςετͯ͠ਖ਼͍͔͠Ͳ͏͔νΣοΫ͢Δɻ • Ծઆݕఆ • A/Bςετ ׂҾ͢Δͱސ٬͕ ૿͑Δɻ

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25 Conﬁrmatory Analysis Ծઆ σʔλ ςετ ԾઆΛσʔλΛ࢖ͬͯݕূ͢Δ

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26 Ծઆ σʔλ ςετ ԾઆΛͲ͏ߏங͢Δͷ͔ʁ

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27 σʔλΛ෼ੳͯ͠ԾઆΛߏங͢Δ EDA ୳ࡧతσʔλ෼ੳʢEDAʣ Ծઆ σʔλ ςετ σʔλ

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28 ༧ଌ ίϯτϩʔϧ EDA ΰʔϧ Why ୳ࡧతσʔλ෼ੳʁ Ծઆ σʔλ

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29 ༧ଌ ίϯτϩʔϧ EDA Ծઆ σʔλ ༧ଌʹԿ͕Өڹ͢Δͷ͔ʁ ίϯτϩʔϧ͢ΔͨΊͷ ҼՌؔ܎͸Կ͔ʁ

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σʔλʹର࣭ͯ͠໰Λ౤͔͚͛ɺ౴͑Λ୳ࡧతʹٻΊ͍ͯ͘ ͜ͱͰɺઆ໌ɺ༧ଌɺίϯτϩʔϧͷͨΊͷԾઆΛߏஙͯ͠ ͍ͨ͘Ίͷ෼ੳɻ 30 EDAʢ୳ࡧతσʔλ෼ੳʣ

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31 ࣭໰ ౴͑ σʔλ Ծઆ EDAʢ୳ࡧతσʔλ෼ੳʣ

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ैۀһσʔλ

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ैۀһσʔλ

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ΰʔϧ • ͲͷΑ͏ʹڅྉʹҧ͍͕ͰΔͷ͔આ໌͍ͨ͠ɻ • ैۀһͷଐੑΛ΋ͱʹڅྉΛ༧ଌ͍ͨ͠ɻ • څྉΛ্͍͛ͨɺ΋͠ՄೳͰ͋Ε͹ʂ

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σʔλʹର࣭ͯ͠໰Λ౤͔͚͛ɺ౴͑Λ୳ࡧతʹٻΊ͍ͯ͘ ͜ͱͰɺઆ໌ɺ༧ଌɺίϯτϩʔϧͷͨΊͷԾઆΛߏஙͯ͠ ͍ͨ͘Ίͷ෼ੳɻ 35 EDAʢ୳ࡧతσʔλ෼ੳʣ

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36 ਖ਼͍࣭͠໰ʹରͯ͠ͷ ͍͍ͩͨͷ౴͑ͷ΄͏͕ɺ ؒҧ࣭ͬͨ໰ʹର͢Δ ਖ਼֬ͳ౴͑ΑΓ΋ ΑͬΆͲ͍͍ɻ — John Tukey

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37 • खݩʹ͋Δม਺ʹ͸ͲͷΑ͏ͳ͹Β͖͕ͭ͋Δͷ͔ɻ • ม਺Ͳ͏͠ͷؒͰͲͷΑ͏ͳ૬͕ؔ͋Δͷ͔ɻ ࣭໰ͷͨΊͷ2ͭͷݪଇ

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“Since the aim of exploratory data analysis is to learn what seems to be, it should be no surprise that pictures play a vital role in doing it well. There is nothing better for making you think of questions you had forgotten to ask (even mentally),” John Tukey

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͹Β͖ͭͱ૬ؔͷՄࢹԽ

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͹Β͖ͭͷՄࢹԽ

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αϚϦɾϏϡʔ

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σʔλ͸͹Βͭ͘ 43

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$6,503 ͹Β͖ͭ ฏۉ $15,000 $1,000

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$6,503 ฏۉ

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஌Γ͍ͨ͜ͱ • ͲͷΑ͏ʹσʔλ͸෼෍͍ͯ͠Δ͔ɻ • Ͳͷ஋͕΋ͬͱ΋ස౓͕ߴ͍͔ʁͳ͔ͥʁ • Ͳͷ஋͕௝͍͔͠ʁͳ͔ͥʁͦΕ͸ظ଴௨Γ͔ʁ • ҙ֎ͳύλʔϯ͕ݟ͑Δ͔ʁͳͥͦ͏ͳͷ͔ʁ 46

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νϟʔτΛ࢖͏

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48 ώετάϥϜ ີ౓ۂઢ όʔ ͹Β͖ͭΛཧղ͢ΔͨΊʹΑ͘࢖ΘΕΔνϟʔτ

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49 ͲͷνϟʔτΛ࢖͑͹Α͍ͷ͔ʁ

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50 σʔλͷλΠϓ࣍ୈʂ

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࿈ଓσʔλ vs. ΧςΰϦʔσʔλ 51

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࿈ଓσʔλ vs. ΧςΰϦʔσʔλ 52

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• ਺஋ (numeric) • ೔෇ɾ࣌ؒʢDateʣ 53

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਺஋ 0 10 20 30 40 50 11 22 45 54 ࿈ଓੑ͕͋Δ ॱংؔ܎͕͋Δ

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55 ώετάϥϜ ີ౓ۂઢ όʔ

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ώετάϥϜ 56

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57 ਺஋σʔλͷ෼෍Λɺ਺஋Λ͍͔ͭ͘ͷ۠ըʢόέοτʣʹ෼͚ɺ ͦΕͧΕͷ۠ըʹ͋ΔσʔλͷྔʢߦʣΛόʔΛ࢖ͬͯද͢ɻ

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ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ 58 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ

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59 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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60 څྉͷ͹Β͖ͭΛώετάϥϜͰ ՄࢹԽ͢Δɻ • λΠϓʹώετάϥϜΛબ୒ɻ • X࣠ʹMontlyIncomeΛબ୒ɻ

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61 څྉͷ͹Β͖͕ͭՄࢹԽ͞Εͨɻ

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62 όέοτͷ਺Λ্͛Δ͜ͱͰɺ͍͔ͭ͘ͷࢁ͕ݟ͑ͯ͘Δɻ

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63 όέοτͷ਺Λ100·Ͱ্͛ͯΈΔɻ

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64 όέοτͷ਺Λ্͛Δ͜ͱͰɺ͍͔ͭ͘ͷࢁ͕ݟ͖͑ͯͨɻ

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65 ͜͏ͨ͠ࢁ͸ɺผͷάϧʔϓʹଐ͍ͯ͠Δͷ͔΋͠Εͳ͍ɻ

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66 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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67 څྉͷ͹Β͖ͭΛஉঁ͝ͱʹ ώετάϥϜͰՄࢹԽ͢Δɻ • ʮ৭ʯʹGenderΛબ୒ɻ

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68 2ͭͷώετάϥϜ͕ॏͳΓ߹͍ͬͯΔɻ உঁؒͰ͸͖͋Β͔ͳҧ͍͸ͳ͍Α͏ͩɻ

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69 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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70 څྉͷ͹Β͖ͭΛ৬छ͝ͱʹώετ άϥϜͰՄࢹԽ͢Δɻ • ʮ৭ʯʹJobRoleΛબ୒ɻ

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71 Managerʢ੺ʣͷάϧʔϓ͸څྉͷߴ͍άϧʔϓɺSales Repʢԫ྘ʣͱ Research ScientistʢϐϯΫʣͷάϧʔϓ͸څྉͷ௿͍άϧʔϓͷΑ͏ͩɻ

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ີ౓ۂઢ 72

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73 • ਺஋σʔλͷ෼෍Λ׈͔ΒͳۂઢΛ࢖ͬͯද͢ • ॎ࣠͸σʔλͷີ౓Λ͋ΒΘ͢ɻ

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74 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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75 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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76 څྉͷ͹Β͖ͭΛີ౓ۂઢͰՄࢹ Խ͢Δɻ • λΠϓʹີ౓ۂઢΛબ୒ɻ • X࣠ʹMonthlyIncomeΛબ୒ɻ

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78 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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79 څྉͷ͹Β͖ͭΛஉঁ͝ͱʹີ౓ۂ ઢͰՄࢹԽ͢Δɻ • ઌ΄Ͳͷີ౓ۂઢΛ༻͍Δɻ • ৭Ͱ෼ׂʹGenderΛબ୒ɻ

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80 உঁͷؒʹͪΐͬͱͨ͠ҧ͍͸͋ΔΑ͏ͩɻ

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81 1. څྉͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. څྉͷ͹Β͖ͭ͸உঁ͝ͱʹҧ͍͕͋Δ͔ʁ 3. څྉͷ͹Β͖ͭ͸৬छ͝ͱʹҧ͍͕͋Δ͔ʁ ྫ - ਺஋ͷ͹Β͖ͭͷՄࢹԽ

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82 څྉͷ͹Β͖ͭΛ৬छ͝ͱʹີ౓ۂ ઢͰՄࢹԽ͢Δɻ • ઌ΄Ͳͷີ౓ۂઢΛ༻͍Δɻ • ৭Ͱ෼ׂʹJobRoleΛબ୒ɻ

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83 ৬छ͝ͱͷڅྉͷ͹Β͖ͭͷҧ͍͸ώετάϥϜΑΓ΋Θ͔Γ΍͍͢ɻ

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ີ౓ۂઢ ώετάϥϜ ಉ͡σʔλΛҟͳΔνϟʔτͰՄࢹԽ

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࿈ଓσʔλ vs. ΧςΰϦʔσʔλ 85

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iPad iPhone MacBook AirPods AppleTV ࿈ଓੑͳ͠ ΧςΰϦʔܕ 86

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87 ώετάϥϜ ີ౓ۂઢ όʔɾνϟʔτ ΧςΰϦʔͷ͹Β͖ͭՄࢹԽ

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88 • ΧςΰϦʔ͝ͱͷ݅਺ΛՄࢹԽɻ • ͲͷΧςΰϦʔʹσʔλ͕ू·͍ͬͯΔͷ͔ʁ • σʔλͷྔʹΑΔύλʔϯ͸͋Δ͔ʁ ஌Γ͍ͨ͜ͱ

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89 1. ৬छͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. ৬छͷ͹Β͖ͭ͸உঁؒͰҧ͍͕͋Δ͔ΛՄࢹԽ͢ Δɻ 3. ৬छͷ͹Β͖ͭ͸ɺୀ৬ͨ͠ਓͱͯ͠ͳ͍ਓͷؒͰ ҧ͍͕͋Δ͔ΛՄࢹԽ͢Δɻ ྫ - ΧςΰϦʔͷ͹Β͖ͭΛՄࢹԽ

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90 1. ৬छͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. ৬छͷ͹Β͖ͭ͸உঁؒͰҧ͍͕͋Δ͔ΛՄࢹԽ͢ Δɻ 3. ৬छͷ͹Β͖ͭ͸ɺୀ৬ͨ͠ਓͱͯ͠ͳ͍ਓͷؒͰ ҧ͍͕͋Δ͔ΛՄࢹԽ͢Δɻ ྫ - ΧςΰϦʔͷ͹Β͖ͭΛՄࢹԽ

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91 ৬छ͝ͱͷਓ਺ΛՄࢹԽɻ

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92 όʔΛਓ਺Ͱιʔτ͢Δɻ • ιʔτʹY1࣠Λબ୒ɻ

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94 1. ৬छͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. ৬छͷ͹Β͖ͭ͸உঁؒͰҧ͍͕͋Δ͔ΛՄࢹԽ͢ Δɻ 3. ৬छͷ͹Β͖ͭ͸ɺୀ৬ͨ͠ਓͱͯ͠ͳ͍ਓͷؒͰ ҧ͍͕͋Δ͔ΛՄࢹԽ͢Δɻ ྫ - ΧςΰϦʔͷ͹Β͖ͭΛՄࢹԽ

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96 1. ৬छͷ͹Β͖ͭΛՄࢹԽ͢Δ 2. ৬छͷ͹Β͖ͭ͸உঁؒͰҧ͍͕͋Δ͔ΛՄࢹԽ͢ Δɻ 3. ৬छͷ͹Β͖ͭ͸ɺୀ৬ͨ͠ਓͱͯ͠ͳ͍ਓͷؒͰ ҧ͍͕͋Δ͔ΛՄࢹԽ͢Δɻ ྫ - ΧςΰϦʔͷ͹Β͖ͭΛՄࢹԽ

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૬ؔͷՄࢹԽ 99

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100 2ͭͷม਺ͷ͏ͪɺ1ͭͷม਺ͷ஋͕มΘΔͱ ΋͏1ͭͷม਺ͷ஋΋ҰఆͷنଇΛ࣋ͬͯ ͍ͬ͠ΐʹมΘΔؔ܎ɻ ૬ؔ

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101 Association Correlation 2ͭͷม਺ͷؒʹԿΒ͔ ͷؔ܎͕͋Δɻ AssociationͷதͰ΋ಛʹͦͷ ؔ܎͕ઢܗʢ௚ઢʣͷ΋ͷɻ ૬ؔ

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102 US UK Japan 5000 2500 Country Monthly Income 0 Association

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103 ೥ྸ څྉ Correlation

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104 ڧ͍ਖ਼ͷ  ૬ؔؔ܎ ૬ؔؔ܎ͳ͠ ڧ͍ෛͷ  ૬ؔؔ܎ 0 1 -1 0.5 -0.5 ૬ؔʢCorrelationʣ

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105 ͳͥ૬ؔΛཧղ͍ͨ͠ͷ͔ʁ

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106 ͹Β͖ͭ ฏۉ 5000 100

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107 ͹Β͖ͭ $20,000 $1,000 څྉ

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108 ͹Β͖ͭ ͜ͷձࣾͷڅྉ͸ ͍͘Β͘Β͍ʁ $20,000 $1,000 څྉ

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109 ͹Β͖ͭ ෆ࣮֬ੑ $20,000 $1,000 څྉ ͜ͷձࣾͷڅྉ͸ ͍͘Β͘Β͍ʁ

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110 0 30 20 ΋͠૬ؔؔ܎Λݟ͚ͭΔ͜ͱ͕Ͱ͖Δͱɻɻɻ 10 $20,000 $1,000 څྉ ۈଓ೥਺

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111 0 30 20 10 $20,000 $1,000 څྉ ۈଓ೥਺ ۈଓ೥਺͕20೥ͩͱ څྉ͸$15,000͘Β͍ɻ $15,000

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112 ෆ࣮֬ੑ͕ݮΔ 0 30 20 څྉ ۈଓ೥਺ ૬ؔ ͹Β͖ͭ $20,000 $1,000 $15,000 $20,000 $1,000

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113 ૬ؔ ෆ࣮֬ੑ͕ݮΔ US UK Japan 5000 100 ͹Β͖ͭ

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114 US UK Japan དྷ݄ͷސ٬਺͸ USͰ͋Ε͹ɺ 4000͔Β5000ͷؒɻ 5000 4000 ૬ؔ

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ڧ͍૬ؔؔ܎ͷ͋Δ΋ͷΛݟ͚ͭΔ͜ͱ͕Ͱ͖Ε͹ څྉ͕Ͳ͏มΘΔ͔Λઆ໌͠΍͘͢ͳΔɻ ·ͨɺڅྉΛ༧ଌ͠΍͘͢ͳΔ

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૬ؔؔ܎ͱҼՌؔ܎͸ҧ͏ɻ ҼՌؔ܎͸૬ؔؔ܎ͷಛผͳλΠϓɻ ؍࡯ͨ͠૬ؔؔ܎͕ɺҼՌؔ܎ͩͱ֬ೝ͢Δࣄ͕Ͱ͖ͨ Βɺͦͷ৘ใΛ࣋ͬͯকདྷΛίϯτϩʔϧ͢Δ͜ͱ͕Ͱ ͖Δɻ

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૬ؔͷՄࢹԽ 117

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118 ࢄ෍ਤ ശͻ͛ਤ όΠΦϦϯਤ ώʔτϚοϓ ີ౓ۂઢ όʔɾνϟʔτ

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119 ͲͷνϟʔτΛ࢖͑͹Α͍ͷ͔ʁ

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120 σʔλͷλΠϓͷ૊Έ߹Θͤ࣍ୈʂ

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121 • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ • ΧςΰϦʔ vs. ΧςΰϦʔ σʔλͷλΠϓͷ૊Έ߹Θͤ

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122 • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ • ΧςΰϦʔ vs. ΧςΰϦʔ σʔλͷλΠϓͷ૊Έ߹Θͤ

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123 ࢄ෍ਤ ശͻ͛ਤ όΠΦϦϯਤ ώʔτϚοϓ ີ౓ۂઢ όʔɾνϟʔτ

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124 ώετάϥϜɺີ౓ۂઢʹ৭Λ࢖͏͜ͱͰɺ ΧςΰϦʔͱ਺஋ͷ૬ؔΛՄࢹԽ͢Δ͜ͱ͕Ͱ͖Δɻ

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125 څྉʢ਺஋ʣx ৬छʢΧςΰϦʔʣ

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126 څྉʢ਺஋ʣx ৬छʢΧςΰϦʔʣ

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127 ͔͠͠ɺΧςΰϦʔͱ਺஋ͷؔ܎ΛݟΔ࣌͸ ശͻ͛ਤΛ࢖ͬͨ΄͏͕ɺΘ͔Γ΍͍͢ɻ ʢҰൠతʹ͸ɻʣ

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ͦΕͧΕͷαΠζʢߦͷ਺ʣ͕౳͘͠ͳΔΑ͏ʹ̐ͭͷάϧʔϓʹ෼͚Δɻ

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3Q (ୈ3࢛෼Ґ/ 75ύʔηϯλΠϧ) 2Q (ୈ2࢛෼Ґ/ 50ύʔηϯλΠϧ) 1Q (ୈ1࢛෼Ґ/ 25ύʔηϯλΠϧ)

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3Q Median 1Q

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3Q Median 1Q

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3Q Median 1Q ࠷େ஋ ࠷খ஋

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ྫ - ΧςΰϦʔͱ਺஋ͷ૬ؔͷՄࢹԽ 134 1. ৬छͱڅྉͷ૬ؔΛՄࢹԽ͢Δ 2. ৬छͱڅྉͷஉঁผͷ૬ؔΛՄࢹԽ͢Δ

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135 1. ৬छͱڅྉͷ૬ؔΛՄࢹԽ͢Δ 2. ৬छͱڅྉͷஉঁผͷ૬ؔΛՄࢹԽ͢Δ ྫ - ΧςΰϦʔͱ਺஋ͷ૬ؔͷՄࢹԽ

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136 • λΠϓʹശͻ͛ਤΛબ୒ɻ • X࣠ʹJobRoleΛબ୒ɻ • Y࣠ʹMonthlyIncomeΛબ୒ɻ • ιʔτʹY࣠Λબ୒ɻ

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137 ৬छʢΧςΰϦʔʣ x څྉʢ਺஋ʣ

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ྫ - ΧςΰϦʔͱ਺஋ͷ૬ؔͷՄࢹԽ 138 1. ৬छͱڅྉͷ૬ؔΛՄࢹԽ͢Δ 2. ৬छͱڅྉͷஉঁผͷ૬ؔΛՄࢹԽ͢Δ

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139 உঁ (ΧςΰϦʔ) vs. څྉ (਺஋)

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৬छ (ΧςΰϦʔʣͰ෼ׂ

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OR…

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142 ܁Γฦ͠Λ࢖͏୅ΘΓʹ৭Λ࢖͏ɻ

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143

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144 • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ • ΧςΰϦʔ vs. ΧςΰϦʔ σʔλͷλΠϓͷ૊Έ߹Θͤ

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145 ࢄ෍ਤ ശͻ͛ਤ όΠΦϦϯਤ ώʔτϚοϓ ີ౓ۂઢ όʔɾνϟʔτ

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146 ࢄ෍ਤ

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147 • ओʹ਺஋σʔλಉ࢜ͷ෼෍Λදࣔ͢Δɻ • ͦΕͧΕͷ఺͸ͦΕͧΕͷσʔλʹ૬౰͢Δɻ ਺஋ ਺஋

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ྫɿ਺஋ͱ਺஋ͷؒͷ૬ؔͷՄࢹԽ 148 1. څྉͱ೥ྸͷؔ܎ΛՄࢹԽ͢Δɻ 2. څྉͱۈଓ೥਺ͷؔ܎ΛՄࢹԽ͢Δɻ 3. څྉͱۈଓ೥਺ͷ૬ؔؔ܎͸৬छʹΑͬͯҧ͏͔ௐ΂ Δɻ

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149 ೥ྸͱڅྉͷ૬ؔΛࢄ෍ਤͰՄࢹ Խ͢Δɻ • λΠϓʹࢄ෍ਤΛબ୒ɻ • X࣠ʹAgeΛબ୒ɻ • Y࣠ʹMonthlyIncomeΛબ୒ɻ

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150 ೥ྸʢ਺஋ʣ x څྉʢ਺஋ʣ

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151 τϨϯυϥΠϯΛදࣔ͢Δɻ • ઌ΄Ͳͷࢄ෍ਤΛ༻͍Δɻ • Y࣠ͷϝχϡʔ͔ΒτϨϯυϥΠϯ Λબ୒͠μΠΞϩάΛ։͘ɻ • λΠϓʹઢܗճؼΛબ୒ɻ

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152 τϨϯυϥΠϯͷػೳΛ࢖͏ͱɺ ૬ؔ܎਺͕Θ͔Δɻ

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ྫɿ਺஋ͱ਺஋ͷؒͷ૬ؔͷՄࢹԽ 153 1. څྉͱ೥ྸͷؔ܎ΛՄࢹԽ͢Δɻ 2. څྉͱۈଓ೥਺ͷؔ܎ΛՄࢹԽ͢Δɻ 3. څྉͱۈଓ೥਺ͷ૬ؔؔ܎͸৬छʹΑͬͯҧ͏͔ௐ΂ Δɻ

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154 • λΠϓʹࢄ෍ਤΛબ୒ɻ • X࣠ʹTotalWorkingYearsΛબ୒ɻ • Y࣠ʹMonthlyIncomeΛબ୒ɻ

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155 ۈଓ೥਺ʢ਺஋ʣ x څྉʢ਺஋ʣ

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156 τϨϯυϥΠϯΛදࣔ͢Δɻ • ઌ΄Ͳͷࢄ෍ਤΛ༻͍Δɻ • Y࣠ͷϝχϡʔ͔ΒτϨϯυϥΠϯ Λબ୒͠μΠΞϩάΛ։͘ɻ • λΠϓʹઢܗճؼΛબ୒ɻ

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157 ۈଓ೥਺ͱڅྉͷ૬ؔ܎਺͸0.77Ͱɺൺֱతڧ͍ਖ਼ͷ૬͕ؔ͋Δɻ

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ྫɿ਺஋ͱ਺஋ͷؒͷ૬ؔͷՄࢹԽ 158 1. څྉͱ೥ྸͷؔ܎ΛՄࢹԽ͢Δɻ 2. څྉͱۈଓ೥਺ͷؔ܎ΛՄࢹԽ͢Δɻ 3. څྉͱۈଓ೥਺ͷ૬ؔؔ܎͸৬छʹΑͬͯҧ͏͔ௐ΂ Δɻ

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159 ૬ؔͷڧ͞͸৬छʹΑͬͯҧ͏ɻ

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160 • ΧςΰϦʔ vs. ਺஋ • ਺஋ vs. ਺஋ • ΧςΰϦʔ vs. ΧςΰϦʔ σʔλͷλΠϓͷ૊Έ߹Θͤ

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161 ࢄ෍ਤ ശͻ͛ਤ όΠΦϦϯਤ ώʔτϚοϓ ີ౓ۂઢ όʔɾνϟʔτ

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162 ΧςΰϦʔ vs. ΧςΰϦʔ ΧςΰϦʔಉ࢜ͷؔ܎ΛݟΔʹ͸ɺΧςΰϦʔͷ ૊Έ߹Θͤ͝ͱʹ਺Λ਺্͑͛Δ͔ɺͦͷશମʹ ର͢Δׂ߹Λܭࢉͦ͠ͷ݁ՌΛՄࢹԽ͢Δɻ

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163 1. ৬छͱڭҭͷؒͷؔ܎ΛՄࢹԽɻ 2. ৬छͱୀ৬͔ͨ͠Ͳ͏͔ͷؒͷؔ܎ΛՄࢹԽɻ 3. ৬छͱڅྉͷؒͷؔ܎ΛՄࢹԽɻ 4. څྉͱۈଓ೥਺ͷؒͷؔ܎ΛՄࢹԽɻ ྫɿΧςΰϦʔͱΧςΰϦʔͷؒͷؔ܎ΛՄࢹԽ

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164 1. ৬छͱڭҭͷؒͷؔ܎ΛՄࢹԽɻ 2. ৬छͱୀ৬͔ͨ͠Ͳ͏͔ͷؒͷؔ܎ΛՄࢹԽɻ 3. ৬छͱڅྉͷؒͷؔ܎ΛՄࢹԽɻ 4. څྉͱۈଓ೥਺ͷؒͷؔ܎ΛՄࢹԽɻ ྫɿΧςΰϦʔͱΧςΰϦʔͷؒͷؔ܎ΛՄࢹԽ

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৬छ (ΧςΰϦʔ) vs.ڭҭ (ΧςΰϦʔ)

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166 1. ৬छͱڭҭͷؒͷؔ܎ΛՄࢹԽɻ 2. ৬छͱୀ৬͔ͨ͠Ͳ͏͔ͷؒͷؔ܎ΛՄࢹԽɻ 3. ৬छͱڅྉͷؒͷؔ܎ΛՄࢹԽɻ 4. څྉͱۈଓ೥਺ͷؒͷؔ܎ΛՄࢹԽɻ ྫɿΧςΰϦʔͱΧςΰϦʔͷؒͷؔ܎ΛՄࢹԽ

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৬छͱୀ৬͔ͨ͠Ͳ͏͔ͷؒͷؔ܎ΛՄࢹԽɻ ৬छ (ΧςΰϦʔ) vs. ୀ৬ (ϩδΧϧ)

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• ϩδΧϧܕ͸ΧςΰϦΧϧܕͷಛघͳ৔߹ɻ • TRUE ͔ FALSEͷ2ͭͷ஋ͷΈΛ࣋ͭɻ

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California Texas New York Florida Oregon ΧςΰϦʔܕ 169

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ϩδΧϧ (࿦ཧ஋) TRUE FALSE ࣙΊͨ? 170 ̎ͭͷ஋͔͠ͱΒͳ͍ΧςΰϦʔ

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৬छ (ΧςΰϦʔ) vs. ୀ৬ (ϩδΧϧ)

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172 1. ৬छͱڭҭͷؒͷؔ܎ΛՄࢹԽɻ 2. ৬छͱୀ৬͔ͨ͠Ͳ͏͔ͷؒͷؔ܎ΛՄࢹԽɻ 3. ৬छͱڅྉͷؒͷؔ܎ΛՄࢹԽɻ 4. څྉͱۈଓ೥਺ͷؒͷؔ܎ΛՄࢹԽɻ ྫɿΧςΰϦʔͱΧςΰϦʔͷؒͷؔ܎ΛՄࢹԽ

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৬छ (ΧςΰϦʔ) vs. څྉ (਺஋) ৬छͱڅྉͷؒͷؔ܎ΛՄࢹԽ

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څྉ (ΧςΰϦʔ) ΧςΰϦʔԽ ৬छ (ΧςΰϦʔ) vs. څྉ (਺஋)

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څྉʢ਺஋ʣ

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څྉʢΧςΰϦʔʣ

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ΧςΰϦʔԽͷઃఆ

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څྉ (ΧςΰϦʔ)ɹvs. ৬छ (ΧςΰϦʔ)

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179 1. ৬छͱڭҭͷؒͷؔ܎ΛՄࢹԽɻ 2. ৬छͱୀ৬͔ͨ͠Ͳ͏͔ͷؒͷؔ܎ΛՄࢹԽɻ 3. ৬छͱڅྉͷؒͷؔ܎ΛՄࢹԽɻ 4. څྉͱۈଓ೥਺ͷؒͷؔ܎ΛՄࢹԽɻ ྫɿΧςΰϦʔͱΧςΰϦʔͷؒͷؔ܎ΛՄࢹԽ

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څྉ (ΧςΰϦʔ) vs. ۈଓ೥਺ (ΧςΰϦʔ)

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181 ώʔτϚοϓ ΧςΰϦʔؒͷ૊Έ߹Θͤ͝ͱʹूܭͨ͠਺஋Λ৭Ͱදࣔ

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182 ৬छͱڭҭͷؔ܎ΛώʔτϚοϓͰՄࢹԽ͢Δɻ

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183 ৬छͱઐ߈ͷਓ਺ͷؔ܎Λώʔτ ϚοϓͰՄࢹԽ͢Δɻ • λΠϓʹώʔτϚοϓΛબ୒ɻ • X࣠ʹJobRoleΛબ୒ɻ • Y࣠ʹEducationFieldΛબ୒ɻ • ৭Ͱ෼ׂʹߦͷ਺Λબ୒ɻ

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184 ৬छͱઐ߈ͷਓ਺ͷؔ܎͕ώʔτϚοϓͰՄࢹԽ͞Εͨɻ

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185 Life Science͕ઐ߈ͰResearch Scientistͷ৬ʹ͍͍ͭͯΔਓ͸ 133ਓ͓Γɺ৬छͱઐ߈ͷ૊Θͤ ͷதͰ͸Ұ൪ଟ͍ɻ

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Q & A

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• ϓϩάϥϛϯάͳ͠ RݴޠͷUIͰ͋ΔExploratoryΛ෼ੳπʔϧͱͯ͠࢖༻͢ΔͨΊडߨத͸ɺϏδωεͷ໰୊ Λղܾ͢ΔͨΊʹඞཁͳσʔλαΠΤϯεͷख๏ͷशಘʹ100ˋूதͰ͖Δ • πʔϧͷ࢖͍ํͰ͸ͳ͘ɺ෼ੳख๏ͷशಘ ݱ৔Ͱ࢖͑Δ෼ੳख๏ΛάϧʔϓԋशΛ௨࣮ͯ͠ࡍʹखΛಈ͔͠ͳ͕Βɺ਎ʹ͚ͭͯߦ͘ ͜ͱ͕Ͱ͖Δɻ • ࢥߟྗͱεΩϧͷशಘ σʔλαΠΤϯεͷεΩϧशಘ͚ͩͰͳ͘ɺσʔλ෼ੳʹඞཁͳࢥߟྗ΋शಘͰ͖Δ ಛ௃

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࿈བྷઌ ϝʔϧ [email protected] ΢ΣϒαΠτ https://ja.exploratory.io ϒʔτΩϟϯϓɾτϨʔχϯά https://ja.exploratory.io/training-jp Twitter @KanAugust

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EXPLORATORY