Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
R を用いた分析(補講) (2) — 人工データの生成 / Analysis using R ...
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Kenji Saito
PRO
November 30, 2024
Technology
81
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
R を用いた分析(補講) (2) — 人工データの生成 / Analysis using R (supplementary) (2) - Generating artificial data
早稲田大学大学院経営管理研究科「企業データ分析」2024 冬のオンデマンド教材 第12回で使用したスライドです。
Kenji Saito
PRO
November 30, 2024
More Decks by Kenji Saito
See All by Kenji Saito
ロボティクスの技術 / Robotics Technology
ks91
PRO
0
14
インシデントレスポンス演習 I / Incident Response Exercise I
ks91
PRO
0
22
責任 2.0/3.0 ∼ 知的創造過程の脱領土化 / Responsibility 2.0/3.0 - The Deterritorialization of the Intellectual Creative Process
ks91
PRO
0
9
エージェント化するAI:現在地とその先に起きる変化 〜 おかわり / AI as Agents: The Current State and the Changes Ahead - a second helping
ks91
PRO
0
27
金融テクノロジーのガバナンス / Governance of Financial Technology
ks91
PRO
0
50
セキュリティの基礎とインシデントレスポンス / Security Fundamentals and Incident Response
ks91
PRO
0
87
やり抜く力を見せるエージェントたち / Agents Who Demonstrate Perseverance
ks91
PRO
0
35
ブロックチェーン / Blockchain
ks91
PRO
0
120
デジタルとコミュニケーション / Digital and Communication
ks91
PRO
0
53
Other Decks in Technology
See All in Technology
【Cyber-sec+】経営層を"動かす"ための考え方
hssh2_bin
0
130
就職⽀援サービスにおけるキャリアアドバイザーのシフトスケジューリング
recruitengineers
PRO
1
140
現地で盛り上がった WWDC26 Keynote
zozotech
PRO
1
200
「エンジニア進化論」2028年の開発完全自動化、エンジニアはどう進化するか
cyberagentdevelopers
PRO
5
4.5k
NAB Show 2026 動画技術関連レポート / NAB Show 2026 Report
cyberagentdevelopers
PRO
0
170
Kubernetesにおける学習基盤とLLMOpsの概要
ry
1
250
2026TECHFRESH畢業分享會 - Lightning Talk - 打造精準高效的 MCP 設計模式與測試實務
line_developers_tw
PRO
0
800
Oracle AI Database@Google Cloud:サービス概要のご紹介
oracle4engineer
PRO
6
1.5k
やさしいA2A入門
minorun365
PRO
12
1.7k
Djangoユーザが知っ得なPostgreSQL機能 - 設計の選択肢を増やす / Djang-use-PostgreSQL
soudai
PRO
1
230
エンジニアリング戦略の作り方 / Crafting Engineering Strategy
iwashi86
20
6.6k
自律型AIエージェントは何を破壊するのか
kojira
0
150
Featured
See All Featured
XXLCSS - How to scale CSS and keep your sanity
sugarenia
250
1.3M
How to train your dragon (web standard)
notwaldorf
97
6.7k
Why Our Code Smells
bkeepers
PRO
340
58k
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2.3k
We Have a Design System, Now What?
morganepeng
55
8.2k
The untapped power of vector embeddings
frankvandijk
2
1.8k
Why Mistakes Are the Best Teachers: Turning Failure into a Pathway for Growth
auna
0
160
Data-driven link building: lessons from a $708K investment (BrightonSEO talk)
szymonslowik
1
1.1k
Accessibility Awareness
sabderemane
1
140
SEOcharity - Dark patterns in SEO and UX: How to avoid them and build a more ethical web
sarafernandez
0
200
Fantastic passwords and where to find them - at NoRuKo
philnash
52
3.7k
Avoiding the “Bad Training, Faster” Trap in the Age of AI
tmiket
0
170
Transcript
Boxes and whiskers — generated by Stable Diffusion XL v1.0
2024 12 R ( ) (2) — (WBS) 2024 12 R ( ) (2) — — 2024-11 – p.1/14
https://speakerdeck.com/ks91/collections/corporate-data-analysis-2024-winter 2024 12 R ( ) (2) — — 2024-11
– p.2/14
( 20 ) 1 • 2 R • 3 •
4 • 5 • 6 ( ) • 7 (1) • 8 (2) • 9 R ( ) (1) — Welch • 10 R ( ) (2) — χ2 • 11 R ( ) (1) — • 12 R ( ) (2) — • 13 GPT-4 14 GPT-4 15 ( ) LaTeX Overleaf 8 (12/16 ) / (2 ) OK / 2024 12 R ( ) (2) — — 2024-11 – p.3/14
N(µ, σ2) ρ 2 ( : ˆ y = a
+ b1 x1 + b2 x2 + e ) 2024 12 R ( ) (2) — — 2024-11 – p.4/14
N(µ, σ2) “rnorm()” set.seed(173205) # # N(50, 10^2) 100 x
<- rnorm(n=100, mean=50, sd=10) # x # hist(x) mean(x) sd(x) 2024 12 R ( ) (2) — — 2024-11 – p.5/14
Histogram of x x Frequency 10 20 30 40 50
60 70 80 0 5 10 15 20 25 30 35 mean(x) : 50.06994 sd(x) : 10.30096 2024 12 R ( ) (2) — — 2024-11 – p.6/14
ρ 2 (1/2) MASS “mvrnorm()” “ .R” # r =
0.9 # t = 3.7 # r = 15.2 # t = 7.5 # = -0.5 # <- matrix(c( r^2, * t * r, * r * t, t^2 ), nrow=2) 2024 12 R ( ) (2) — — 2024-11 – p.7/14
“mvrnorm()” = S xx S xy S xy S yy
= S xx rS x S y rS x S y S yy ( r = S xy S x S y ) 2 x, y x, y, z, . . . 2024 12 R ( ) (2) — — 2024-11 – p.8/14
ρ 2 (2/2) MASS “mvrnorm()” “ .R” # set.seed(28284) <-
mvrnorm(n=100, mu=c( r, t), Sigma= ) <- pmin(pmax( [,1], 13.0), 19.9) <- pmin(pmax( [,2], 0.0), 20.0) “ [,1]” “ [,2]” plot 2024 12 R ( ) (2) — — 2024-11 – p.9/14
0 5 10 15 20 13 14 15 16 17
18 ㈇ࡢ┦㛵ࡢ 㐌ᙜࡓࡾࡢㄢእ㐠ື㛫 100m㉮ࡢࢱ࣒ (⛊) r : -0.5932345 ( ) -0.5884094 ( ) 2024 12 R ( ) (2) — — 2024-11 – p.10/14
(1/2) “ .R” n <- 50 # a <- 49.4
# ( (158cm ) ) # r_father <- 0.306 mean_father <- 168.78 sd_father <- 3.2 # r_mother <- 0.37 mean_mother <- 155.32 sd_mother <- 2.45 2024 12 R ( ) (2) — — 2024-11 – p.11/14
(2/2) “ .R” <- round(rnorm(n=n, mean=mean_father, sd=sd_father), digits=1) <- round(rnorm(n=n,
mean=mean_mother, sd=sd_mother), digits=1) e <- rnorm(n=n, mean=0, sd=2.8) # <- round(a + r_father * + r_mother * + e, digits=1) 1 “round()” plot 2024 12 R ( ) (2) — — 2024-11 – p.12/14
ፉ㌟㛗 160 165 170 175 152 156 160 164 160
165 170 175 ∗㌟㛗 152 156 160 164 150 154 158 150 154 158 ẕ㌟㛗 : 34.2484 : 0.3545 : 0.4137 : 0.2831 2024 12 R ( ) (2) — — 2024-11 – p.13/14
2024 12 R ( ) (2) — — 2024-11 –
p.14/14