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TIL
  • MAIN
  • : TIL?
  • : WIL
  • : Plan
  • : Retrospective
    • 21Y
      • Wait a moment!
      • 9M 2W
      • 9M1W
      • 8M4W
      • 8M3W
      • 8M2W
      • 8M1W
      • 7M4W
      • 7M3W
      • 7M2W
      • 7M1W
      • 6M5W
      • 1H
    • ์ƒˆ์‚ฌ๋žŒ ๋˜๊ธฐ ํ”„๋กœ์ ํŠธ
      • 2ํšŒ์ฐจ
      • 1ํšŒ์ฐจ
  • TIL : ML
    • Paper Analysis
      • BERT
      • Transformer
    • Boostcamp 2st
      • [S]Data Viz
        • (4-3) Seaborn ์‹ฌํ™”
        • (4-2) Seaborn ๊ธฐ์ดˆ
        • (4-1) Seaborn ์†Œ๊ฐœ
        • (3-4) More Tips
        • (3-3) Facet ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-2) Color ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-1) Text ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-3) Scatter Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-2) Line Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-1) Bar Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (1-3) Python๊ณผ Matplotlib
        • (1-2) ์‹œ๊ฐํ™”์˜ ์š”์†Œ
        • (1-1) Welcome to Visualization (OT)
      • [P]MRC
        • (2๊ฐ•) Extraction-based MRC
        • (1๊ฐ•) MRC Intro & Python Basics
      • [P]KLUE
        • (5๊ฐ•) BERT ๊ธฐ๋ฐ˜ ๋‹จ์ผ ๋ฌธ์žฅ ๋ถ„๋ฅ˜ ๋ชจ๋ธ ํ•™์Šต
        • (4๊ฐ•) ํ•œ๊ตญ์–ด BERT ์–ธ์–ด ๋ชจ๋ธ ํ•™์Šต
        • [NLP] ๋ฌธ์žฅ ๋‚ด ๊ฐœ์ฒด๊ฐ„ ๊ด€๊ณ„ ์ถ”์ถœ
        • (3๊ฐ•) BERT ์–ธ์–ด๋ชจ๋ธ ์†Œ๊ฐœ
        • (2๊ฐ•) ์ž์—ฐ์–ด์˜ ์ „์ฒ˜๋ฆฌ
        • (1๊ฐ•) ์ธ๊ณต์ง€๋Šฅ๊ณผ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ
      • [U]Stage-CV
      • [U]Stage-NLP
        • 7W Retrospective
        • (10๊ฐ•) Advanced Self-supervised Pre-training Models
        • (09๊ฐ•) Self-supervised Pre-training Models
        • (08๊ฐ•) Transformer (2)
        • (07๊ฐ•) Transformer (1)
        • 6W Retrospective
        • (06๊ฐ•) Beam Search and BLEU score
        • (05๊ฐ•) Sequence to Sequence with Attention
        • (04๊ฐ•) LSTM and GRU
        • (03๊ฐ•) Recurrent Neural Network and Language Modeling
        • (02๊ฐ•) Word Embedding
        • (01๊ฐ•) Intro to NLP, Bag-of-Words
        • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Preprocessing for NMT Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Subword-level Language Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ2] RNN-based Language Model
        • [์„ ํƒ ๊ณผ์ œ] BERT Fine-tuning with transformers
        • [ํ•„์ˆ˜ ๊ณผ์ œ] Data Preprocessing
      • Mask Wear Image Classification
        • 5W Retrospective
        • Report_Level1_6
        • Performance | Review
        • DAY 11 : HardVoting | MultiLabelClassification
        • DAY 10 : Cutmix
        • DAY 9 : Loss Function
        • DAY 8 : Baseline
        • DAY 7 : Class Imbalance | Stratification
        • DAY 6 : Error Fix
        • DAY 5 : Facenet | Save
        • DAY 4 : VIT | F1_Loss | LrScheduler
        • DAY 3 : DataSet/Lodaer | EfficientNet
        • DAY 2 : Labeling
        • DAY 1 : EDA
        • 2_EDA Analysis
      • [P]Stage-1
        • 4W Retrospective
        • (10๊ฐ•) Experiment Toolkits & Tips
        • (9๊ฐ•) Ensemble
        • (8๊ฐ•) Training & Inference 2
        • (7๊ฐ•) Training & Inference 1
        • (6๊ฐ•) Model 2
        • (5๊ฐ•) Model 1
        • (4๊ฐ•) Data Generation
        • (3๊ฐ•) Dataset
        • (2๊ฐ•) Image Classification & EDA
        • (1๊ฐ•) Competition with AI Stages!
      • [U]Stage-3
        • 3W Retrospective
        • PyTorch
          • (10๊ฐ•) PyTorch Troubleshooting
          • (09๊ฐ•) Hyperparameter Tuning
          • (08๊ฐ•) Multi-GPU ํ•™์Šต
          • (07๊ฐ•) Monitoring tools for PyTorch
          • (06๊ฐ•) ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
          • (05๊ฐ•) Dataset & Dataloader
          • (04๊ฐ•) AutoGrad & Optimizer
          • (03๊ฐ•) PyTorch ํ”„๋กœ์ ํŠธ ๊ตฌ์กฐ ์ดํ•ดํ•˜๊ธฐ
          • (02๊ฐ•) PyTorch Basics
          • (01๊ฐ•) Introduction to PyTorch
      • [U]Stage-2
        • 2W Retrospective
        • DL Basic
          • (10๊ฐ•) Generative Models 2
          • (09๊ฐ•) Generative Models 1
          • (08๊ฐ•) Sequential Models - Transformer
          • (07๊ฐ•) Sequential Models - RNN
          • (06๊ฐ•) Computer Vision Applications
          • (05๊ฐ•) Modern CNN - 1x1 convolution์˜ ์ค‘์š”์„ฑ
          • (04๊ฐ•) Convolution์€ ๋ฌด์—‡์ธ๊ฐ€?
          • (03๊ฐ•) Optimization
          • (02๊ฐ•) ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ - MLP (Multi-Layer Perceptron)
          • (01๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ณธ ์šฉ์–ด ์„ค๋ช… - Historical Review
        • Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Multi-headed Attention Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] LSTM Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] CNN Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Optimization Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] MLP Assignment
      • [U]Stage-1
        • 1W Retrospective
        • AI Math
          • (AI Math 10๊ฐ•) RNN ์ฒซ๊ฑธ์Œ
          • (AI Math 9๊ฐ•) CNN ์ฒซ๊ฑธ์Œ
          • (AI Math 8๊ฐ•) ๋ฒ ์ด์ฆˆ ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 7๊ฐ•) ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 6๊ฐ•) ํ™•๋ฅ ๋ก  ๋ง›๋ณด๊ธฐ
          • (AI Math 5๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ํ•™์Šต๋ฐฉ๋ฒ• ์ดํ•ดํ•˜๊ธฐ
          • (AI Math 4๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ๋งค์šด๋ง›
          • (AI Math 3๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ์ˆœํ•œ๋ง›
          • (AI Math 2๊ฐ•) ํ–‰๋ ฌ์ด ๋ญ์˜ˆ์š”?
          • (AI Math 1๊ฐ•) ๋ฒกํ„ฐ๊ฐ€ ๋ญ์˜ˆ์š”?
        • Python
          • (Python 7-2๊ฐ•) pandas II
          • (Python 7-1๊ฐ•) pandas I
          • (Python 6๊ฐ•) numpy
          • (Python 5-2๊ฐ•) Python data handling
          • (Python 5-1๊ฐ•) File / Exception / Log Handling
          • (Python 4-2๊ฐ•) Module and Project
          • (Python 4-1๊ฐ•) Python Object Oriented Programming
          • (Python 3-2๊ฐ•) Pythonic code
          • (Python 3-1๊ฐ•) Python Data Structure
          • (Python 2-4๊ฐ•) String and advanced function concept
          • (Python 2-3๊ฐ•) Conditionals and Loops
          • (Python 2-2๊ฐ•) Function and Console I/O
          • (Python 2-1๊ฐ•) Variables
          • (Python 1-3๊ฐ•) ํŒŒ์ด์ฌ ์ฝ”๋”ฉ ํ™˜๊ฒฝ
          • (Python 1-2๊ฐ•) ํŒŒ์ด์ฌ ๊ฐœ์š”
          • (Python 1-1๊ฐ•) Basic computer class for newbies
        • Assignment
          • [์„ ํƒ ๊ณผ์ œ 3] Maximum Likelihood Estimate
          • [์„ ํƒ ๊ณผ์ œ 2] Backpropagation
          • [์„ ํƒ ๊ณผ์ œ 1] Gradient Descent
          • [ํ•„์ˆ˜ ๊ณผ์ œ 5] Morsecode
          • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Baseball
          • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Text Processing 2
          • [ํ•„์ˆ˜ ๊ณผ์ œ 2] Text Processing 1
          • [ํ•„์ˆ˜ ๊ณผ์ œ 1] Basic Math
    • ๋”ฅ๋Ÿฌ๋‹ CNN ์™„๋ฒฝ ๊ฐ€์ด๋“œ - Fundamental ํŽธ
      • ์ข…ํ•ฉ ์‹ค์Šต 2 - ์บ๊ธ€ Plant Pathology(๋‚˜๋ฌด์žŽ ๋ณ‘ ์ง„๋‹จ) ๊ฒฝ์—ฐ ๋Œ€ํšŒ
      • ์ข…ํ•ฉ ์‹ค์Šต 1 - 120์ข…์˜ Dog Breed Identification ๋ชจ๋ธ ์ตœ์ ํ™”
      • ์‚ฌ์ „ ํ›ˆ๋ จ ๋ชจ๋ธ์˜ ๋ฏธ์„ธ ์กฐ์ • ํ•™์Šต๊ณผ ๋‹ค์–‘ํ•œ Learning Rate Scheduler์˜ ์ ์šฉ
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - ResNet ์ƒ์„ธ์™€ EfficientNet ๊ฐœ์š”
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - AlexNet, VGGNet, GoogLeNet
      • Albumentation์„ ์ด์šฉํ•œ Augmentation๊ธฐ๋ฒ•๊ณผ Keras Sequence ํ™œ์šฉํ•˜๊ธฐ
      • ์‚ฌ์ „ ํ›ˆ๋ จ CNN ๋ชจ๋ธ์˜ ํ™œ์šฉ๊ณผ Keras Generator ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ดํ•ด
      • ๋ฐ์ดํ„ฐ ์ฆ๊ฐ•์˜ ์ดํ•ด - Keras ImageDataGenerator ํ™œ์šฉ
      • CNN ๋ชจ๋ธ ๊ตฌํ˜„ ๋ฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐ๋ณธ ๊ธฐ๋ฒ• ์ ์šฉํ•˜๊ธฐ
    • AI School 1st
    • ํ˜„์—… ์‹ค๋ฌด์ž์—๊ฒŒ ๋ฐฐ์šฐ๋Š” Kaggle ๋จธ์‹ ๋Ÿฌ๋‹ ์ž…๋ฌธ
    • ํŒŒ์ด์ฌ ๋”ฅ๋Ÿฌ๋‹ ํŒŒ์ดํ† ์น˜
  • TIL : Python & Math
    • Do It! ์žฅ๊ณ +๋ถ€ํŠธ์ŠคํŠธ๋žฉ: ํŒŒ์ด์ฌ ์›น๊ฐœ๋ฐœ์˜ ์ •์„
      • Relations - ๋‹ค๋Œ€๋‹ค ๊ด€๊ณ„
      • Relations - ๋‹ค๋Œ€์ผ ๊ด€๊ณ„
      • ํ…œํ”Œ๋ฆฟ ํŒŒ์ผ ๋ชจ๋“ˆํ™” ํ•˜๊ธฐ
      • TDD (Test Driven Development)
      • template tags & ์กฐ๊ฑด๋ฌธ
      • ์ •์  ํŒŒ์ผ(static files) & ๋ฏธ๋””์–ด ํŒŒ์ผ(media files)
      • FBV (Function Based View)์™€ CBV (Class Based View)
      • Django ์ž…๋ฌธํ•˜๊ธฐ
      • ๋ถ€ํŠธ์ŠคํŠธ๋žฉ
      • ํ”„๋ก ํŠธ์—”๋“œ ๊ธฐ์ดˆ๋‹ค์ง€๊ธฐ (HTML, CSS, JS)
      • ๋“ค์–ด๊ฐ€๊ธฐ + ํ™˜๊ฒฝ์„ค์ •
    • Algorithm
      • Programmers
        • Level1
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์ˆซ์ž ๋ฌธ์ž์—ด๊ณผ ์˜๋‹จ์–ด
          • ์ž์—ฐ์ˆ˜ ๋’ค์ง‘์–ด ๋ฐฐ์—ด๋กœ ๋งŒ๋“ค๊ธฐ
          • ์ •์ˆ˜ ๋‚ด๋ฆผ์ฐจ์ˆœ์œผ๋กœ ๋ฐฐ์น˜ํ•˜๊ธฐ
          • ์ •์ˆ˜ ์ œ๊ณฑ๊ทผ ํŒ๋ณ„
          • ์ œ์ผ ์ž‘์€ ์ˆ˜ ์ œ๊ฑฐํ•˜๊ธฐ
          • ์ง์‚ฌ๊ฐํ˜• ๋ณ„์ฐ๊ธฐ
          • ์ง์ˆ˜์™€ ํ™€์ˆ˜
          • ์ฒด์œก๋ณต
          • ์ตœ๋Œ€๊ณต์•ฝ์ˆ˜์™€ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • ์ฝœ๋ผ์ธ  ์ถ”์ธก
          • ํฌ๋ ˆ์ธ ์ธํ˜•๋ฝ‘๊ธฐ ๊ฒŒ์ž„
          • ํ‚คํŒจ๋“œ ๋ˆ„๋ฅด๊ธฐ
          • ํ‰๊ท  ๊ตฌํ•˜๊ธฐ
          • ํฐ์ผ“๋ชฌ
          • ํ•˜์ƒค๋“œ ์ˆ˜
          • ํ•ธ๋“œํฐ ๋ฒˆํ˜ธ ๊ฐ€๋ฆฌ๊ธฐ
          • ํ–‰๋ ฌ์˜ ๋ง์…ˆ
        • Level2
          • ์ˆซ์ž์˜ ํ‘œํ˜„
          • ์ˆœ์œ„ ๊ฒ€์ƒ‰
          • ์ˆ˜์‹ ์ตœ๋Œ€ํ™”
          • ์†Œ์ˆ˜ ์ฐพ๊ธฐ
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์‚ผ๊ฐ ๋‹ฌํŒฝ์ด
          • ๋ฌธ์ž์—ด ์••์ถ•
          • ๋ฉ”๋‰ด ๋ฆฌ๋‰ด์–ผ
          • ๋” ๋งต๊ฒŒ
          • ๋•…๋”ฐ๋จน๊ธฐ
          • ๋ฉ€์ฉกํ•œ ์‚ฌ๊ฐํ˜•
          • ๊ด„ํ˜ธ ํšŒ์ „ํ•˜๊ธฐ
          • ๊ด„ํ˜ธ ๋ณ€ํ™˜
          • ๊ตฌ๋ช…๋ณดํŠธ
          • ๊ธฐ๋Šฅ ๊ฐœ๋ฐœ
          • ๋‰ด์Šค ํด๋Ÿฌ์Šคํ„ฐ๋ง
          • ๋‹ค๋ฆฌ๋ฅผ ์ง€๋‚˜๋Š” ํŠธ๋Ÿญ
          • ๋‹ค์Œ ํฐ ์ˆซ์ž
          • ๊ฒŒ์ž„ ๋งต ์ตœ๋‹จ๊ฑฐ๋ฆฌ
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
          • ๊ฐ€์žฅ ํฐ ์ •์‚ฌ๊ฐํ˜• ์ฐพ๊ธฐ
          • H-Index
          • JadenCase ๋ฌธ์ž์—ด ๋งŒ๋“ค๊ธฐ
          • N๊ฐœ์˜ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • N์ง„์ˆ˜ ๊ฒŒ์ž„
          • ๊ฐ€์žฅ ํฐ ์ˆ˜
          • 124 ๋‚˜๋ผ์˜ ์ˆซ์ž
          • 2๊ฐœ ์ดํ•˜๋กœ ๋‹ค๋ฅธ ๋น„ํŠธ
          • [3์ฐจ] ํŒŒ์ผ๋ช… ์ •๋ ฌ
          • [3์ฐจ] ์••์ถ•
          • ์ค„ ์„œ๋Š” ๋ฐฉ๋ฒ•
          • [3์ฐจ] ๋ฐฉ๊ธˆ ๊ทธ๊ณก
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
        • Level3
          • ๋งค์นญ ์ ์ˆ˜
          • ์™ธ๋ฒฝ ์ ๊ฒ€
          • ๊ธฐ์ง€๊ตญ ์„ค์น˜
          • ์ˆซ์ž ๊ฒŒ์ž„
          • 110 ์˜ฎ๊ธฐ๊ธฐ
          • ๊ด‘๊ณ  ์ œ๊ฑฐ
          • ๊ธธ ์ฐพ๊ธฐ ๊ฒŒ์ž„
          • ์…”ํ‹€๋ฒ„์Šค
          • ๋‹จ์†์นด๋ฉ”๋ผ
          • ํ‘œ ํŽธ์ง‘
          • N-Queen
          • ์ง•๊ฒ€๋‹ค๋ฆฌ ๊ฑด๋„ˆ๊ธฐ
          • ์ตœ๊ณ ์˜ ์ง‘ํ•ฉ
          • ํ•ฉ์Šน ํƒ์‹œ ์š”๊ธˆ
          • ๊ฑฐ์Šค๋ฆ„๋ˆ
          • ํ•˜๋…ธ์ด์˜ ํƒ‘
          • ๋ฉ€๋ฆฌ ๋›ฐ๊ธฐ
          • ๋ชจ๋‘ 0์œผ๋กœ ๋งŒ๋“ค๊ธฐ
        • Level4
    • Head First Python
    • ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ SQL
    • ๋‹จ ๋‘ ์žฅ์˜ ๋ฌธ์„œ๋กœ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์‹œ๊ฐํ™” ๋ฝ€๊ฐœ๊ธฐ
    • Linear Algebra(Khan Academy)
    • ์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜
    • Statistics110
  • TIL : etc
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Kubernetes
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Docker
      • 2. ๋„์ปค ์„ค์น˜ ์‹ค์Šต 1 - ํ•™์ŠตํŽธ(์ค€๋น„๋ฌผ/์‹ค์Šต ์œ ํ˜• ์†Œ๊ฐœ)
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  • ๊ฐ€์ƒํ™”ํ ํˆฌ์ž๊ณต์‹
  • PART3. ํ•ต์‹ฌ์€ ์ƒ์Šน์žฅ ํˆฌ์ž + ๋ฆฌ์Šคํฌ ๊ด€๋ฆฌ
  • ์›”์š”์ผ, ์˜ค์ „, ์›”๋ง๊ณผ ์›”์ดˆ๋ฅผ ๋…ธ๋ฆด ๊ฒƒ
  • ํˆฌ์ž๋Š” ์ƒ์Šน์žฅ์—์„œ๋งŒ ํ•˜๋Š” ๊ฒƒ
  • ์–ด๋– ํ•œ ๊ฒฝ์šฐ์—๋„ MDD๊ฐ€ 20%๊ฐ€ ๋„˜์œผ๋ฉด ์•ˆ๋œ๋‹ค.
  • ๋ถ„์‚ฐํˆฌ์ž๋Š” ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋‚ฎ์€ ์ฝ”์ธ์œผ๋กœ

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  1. 2021 TIL
  2. APR

30 Fri

๊ฐ€์ƒํ™”ํ ํˆฌ์ž๊ณต์‹

PART3. ํ•ต์‹ฌ์€ ์ƒ์Šน์žฅ ํˆฌ์ž + ๋ฆฌ์Šคํฌ ๊ด€๋ฆฌ

์›”์š”์ผ, ์˜ค์ „, ์›”๋ง๊ณผ ์›”์ดˆ๋ฅผ ๋…ธ๋ฆด ๊ฒƒ

  • ์ˆ˜์ต์€ ์˜ค์ „์— ๋ฐœ์ƒ

    • ์˜ค์ „, ํŠนํžˆ ์ƒˆ๋ฒฝ์— ๊ฑฐ๋ž˜๋Ÿ‰์ด ๊ธ‰๊ฐ

    • ๊ฒฝ์Ÿ์ด ์น˜์—ดํ•˜์ง€ ์•Š์Œ

      • ์ž์‹ ์ด ์›ํ•˜๋Š” ๋งค์ˆ˜๊ฐ€ ์•ž์œผ๋กœ 30์–ต๊ณผ 3์–ต์ค‘ ์–ด๋А ๊ธˆ์•ก์ด ๋ฌถ์—ฌ์žˆ๋Š”๊ฒŒ ์ข‹์€๊ฐ€?

    • ์ž‘์ „ ์„ธ๋ ฅ์ด ๊ฐ€๊ฒฉ์„ ์˜ฌ๋ฆฌ๊ธฐ ์‰ฌ์›€

  • ์›”์š”์ผ๊ณผ ๋ชฉ์š”์ผ์ด ์ˆ˜์ต๋ฅ ์ด ์ข‹๋‹ค.

    • ์ด์œ ๋Š” ๊ธ€์Žผ?

    • ์—ฌ๊ธฐ์„œ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์€ ํ†ต๊ณ„์  ์ž๋ฃŒ๋กœ ์–ป์„ ์ˆ˜ ์žˆ๋Š” ํŒ๋‹จ.

    • ํŠนํžˆ, ์›”์š”์ผ์ด ์†์ต๋น„๊ฐ€ ์••๋„์ ์œผ๋กœ ๋†’๋‹ค

  • ์›”์ค‘ ํšจ๊ณผ

    • ์›”๋ง์— ์›”๊ธ‰์„ ๋ฐ›์•„ ๊ฑฐ๋ž˜๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๊ณ , ์›”์ดˆ์— ๋ˆ์ด ์—†์–ด ๊ฑฐ๋ž˜๋Ÿ‰์ด ๊ธ‰๊ฐํ•œ๋‹ค.

    • ์›”๋ง, ์›”์ดˆ์—๋งŒ ํˆฌ์žํ•˜๊ฑฐ๋‚˜ ๋˜๋Š” ์›”๋ง, ์›”์ดˆ์˜ ํˆฌ์ž ๋น„์ค‘์„ ๋†’์ผ ๊ฒƒ

  • ๊ทธ๋ ‡๋‹ค๊ณ  ๋ฌด์‹ํ•˜๊ฒŒ, ์˜ค์ „, ์›”์š”์ผ, ์›”๋ง ๋˜๋Š” ์›”์ดˆ์—๋งŒ ํˆฌ์žํ•˜๋ผ๋Š” ๋œป์€ ์•„๋‹ˆ๋‹ค.

    • ๋‚˜์˜์ง€ ์•Š์€ ์ „๋žต์ž„์€ ๋ถ„๋ช…

ํˆฌ์ž๋Š” ์ƒ์Šน์žฅ์—์„œ๋งŒ ํ•˜๋Š” ๊ฒƒ

  • ์ƒ์Šน์žฅ๊ณผ ํ•˜๋ฝ์žฅ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ์ ˆ๋Œ€์  ๊ธฐ์ค€์€ ์—†๋‹ค.

    • ํˆฌ์ž์ž๊ฐ€ ์ •ํ•œ ๊ธฐ์ค€์— ๋”ฐ๋ผ ์ƒ์Šน์žฅ๊ณผ ํ•˜๋ฝ์žฅ์ด ๊ตฌ๋ถ„๋œ๋‹ค.

    • ์ด ๊ธฐ์ค€์ด ๋ฌด์—‡์ด๋ƒ๋Š” ๊ตฌ์ฒด์ ์œผ๋กœ ์ค‘์š”ํ•˜์ง€ ์•Š์œผ๋ฉฐ, ํˆฌ์ž์ž๊ฐ€ ์ฒ ์ €ํ•˜๊ฒŒ ์ƒ์Šน์žฅ์—๋งŒ ํˆฌ์žํ–ˆ๋ƒ๊ฐ€ ์ค‘์š”ํ•˜๋‹ค

  • ์ด๋™ ํ‰๊ท , moving average

    • ์ตœ๊ทผ ๋ฉฐ์น  ๊ฐ„์˜ ํ‰๊ท ์„ ์˜๋ฏธํ•œ๋‹ค.

    • ์ฃผ๋กœ 3, 5, 10, 20์ผ์„ ๊ธฐ์ค€์œผ๋กœ ํ‰๊ท ์„ ๊ตฌํ•œ๋‹ค.

    • ํ˜„์žฌ ๊ฐ€๊ฒฉ์ด ์ด๋™ํ‰๊ท  ๋ณด๋‹ค ๋†’์œผ๋ฉด ์ƒ์Šน์žฅ, ๋ฐ˜๋Œ€๋ฉด ํ•˜๋ฝ์žฅ์ด๋‹ค.

  • ์ด๋™ ํ‰๊ท ์„  ๋น„์œจ๋กœ ํˆฌ์ž ๊ธˆ์•ก์„ ์กฐ์ ˆํ•˜๋Š” ๋ฐฉ๋ฒ•

    1. 3์ผ, 5์ผ, 10์ผ, 20์ผ ์ด๋™ํ‰๊ท ์„ ์„ ๊ฐ๊ฐ ๊ตฌํ•œ๋‹ค

    2. ๊ฐ€๊ฒฉ์ด ํ•ด๋‹น ์ด๋™ ํ‰๊ท ์„ ๋ณด๋‹ค ์œ„์— ์žˆ์œผ๋ฉด 1์  ์•„๋ž˜์— ์žˆ์œผ๋ฉด 0์ 

    3. 4๊ฐœ์˜ ์ด๋™ํ‰๊ท ์„ ์˜ ์ ์ˆ˜๋ฅผ ๊ตฌํ•œ๋‹ค

    4. ์—ฌ๋Ÿฌ ์ฝ”์ธ๋“ค์— ๋Œ€ํ•ด ์ด๋ฅผ ๊ตฌํ•ด๋ณด๊ณ  ์ด ์ ์ˆ˜์— ๋”ฐ๋ผ ํˆฌ์ž ๋น„์ค‘์„ ๊ฒฐ์ •ํ•œ๋‹ค.

  • ์‹ธ๊ฒŒ ์‚ฌ์„œ ๋น„์‹ธ๊ฒŒ ํŒŒ๋Š” ๊ฒƒ๊ณผ ๋–จ์–ด์ง€๋Š” ๊ฑธ ์‚ฌ์„œ ์˜ค๋ฅด๋Š” ๊ฑธ ํŒ”์•„๋ผ๋Š” ์ „ํ˜€ ๋‹ค๋ฅธ ๋ง์ด๋‹ค.

    • ๊ฐœ๋ฏธ๋Š” ์—ญ์ถ”์„ธ ํˆฌ์ž๋ฅผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊นจ์ง„๋‹ค

    • ์ถ”์„ธ๋ผ๋Š” ์†์„ฑ์ด ์กด์žฌํ•˜์ง€๋งŒ ์–ธ์ œ๋“ ์ง€ ๋ฐ˜๋“ฑํ•˜๊ฑฐ๋‚˜ ํ•˜๋ฝํ•  ๋•Œ๋„ ์žˆ์ง€ ์•Š๋А๋ƒ?

      • ๊ฐ€๊ฒฉ์˜ ๋ฐฉํ–ฅ์„ฑ์ด ์œ ์ง€๋œ๋‹ค๋Š” ์–˜๊ธฐ๊ฐ€ ์•„๋‹ˆ๋‹ค. ๋ฐ”๋€Œ๊ธฐ ์ „๊นŒ์ง€ ์ผ๋ฐ˜์ ์œผ๋กœ ์ถ”์„ธ๊ฐ€ ์œ ์ง€๋œ๋‹ค๋Š” ๋ง์ด๋‹ค.

    • ์ด๋™ ํ‰๊ท ์„ ์„ ์ด์šฉํ•ด์„œ ์ƒ์Šน์žฅ๊ณผ ํ•˜๋ฝ์žฅ์„ ๊ตฌ๋ถ„ํ•ด๋ผ.

      • ์–ด์ œ ํ•˜๋ฝ์žฅ์ธ ์ฝ”์ธ์„ ์ƒ€๋Š”๋ฐ, ์˜ค๋Š˜ ๋” ๋–จ์–ด์ง€๋ฉด ์–ด์ œ ๋น„์‹ธ๊ฒŒ ์‚ฐ ๊ฒƒ์ด ์•„๋‹Œ๊ฐ€?

      • ์™„๋ฒฝํ•œ ์ €์ ์—์„œ ์‚ด ์ˆ˜ ์—†๊ณ , ๊ณ ์ ์—์„œ ํŒ” ์ˆ˜ ์—†๋‹ค.

      • ๊ทธ๋Ÿฌ๋‹ˆ, ์ƒ์Šน์žฅ์„ ํŒ๋‹จํ•ด์„œ ์‚ฌ๋Š” ๊ฒƒ์ด ์ด์ƒ์ 

    • ์ถ”์„ธ ์ถ”์ข… ์ „๋žต์ด ํ•ญ์ƒ ํ†ตํ•˜์ง€๋Š” ์•Š๋Š”๋‹ค. ํšก๋ณด ๊ตฌ๊ฐ„์—๋Š” ์†์‹ค์ด ๋ˆ„์ ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์žฅ๊ธฐ์ ์œผ๋กœ๋Š” ์ด๋“์ด ๋˜๋Š”๋ฐ, ํฐ ์ถ”์„ธ๋Š” ํ•œ๋ฒˆ ์ง€์†๋˜๋ฉด ์›€์ง์ž„์˜ ํญ์— ์ œํ•œ์ด ์—†๊ธฐ ๋–„๋ฌธ์ด๋‹ค.

    • ํšก๋ณด ๊ตฌ๊ฐ„์—์„œ๋Š” ์—ญ์ถ”์„ธ ์ „๋žต์ด ์ˆ˜์ต์„ ๋ณผ ์ˆ˜๋„ ์žˆ๋‹ค. ์ด๋ก ์ƒ ๊ณ ์ ๊ณผ ์ €์ ์„ ์ž˜ ์บ์น˜ํ•œ๋‹ค๋ฉด ์ข‹์€ ์ „๋žต์ด์ง€๋งŒ, ์–ด๋ ค์šธ ํ…๋ฐ...

์–ด๋– ํ•œ ๊ฒฝ์šฐ์—๋„ MDD๊ฐ€ 20%๊ฐ€ ๋„˜์œผ๋ฉด ์•ˆ๋œ๋‹ค.

  • MDD๋Š” ํŠน์ • ๊ตฌ๊ฐ„์—์„œ ํˆฌ์ž์ž๊ฐ€ ๊ฒช๋Š” ์ตœ๋Œ€ ์†์‹ค์ด๋‹ค.

    • ์›Œ๋ Œ ๋ฒ„ํ•์€ MDD 50%๋ฅผ ์†Œํ™”ํ•  ๊ฐ์˜ค๊ฐ€ ์—†์œผ๋ฉด ์ฃผ์‹์„ ํ•˜์ง€ ๋ง๋ผ๊ณ  ์ด์•ผ๊ธฐ ํ–ˆ๋‹ค.

      • ์ผ๋ฐ˜์ธ์€ 10%๋งŒ ๊นจ์ ธ๋„ ๊ฐ€์Šด์ด ๋›ฐ๊ณ  20% ์ด์ƒ์€ ๋ฉ˜๋ถ•์ด ๋œ๋‹ค.

    • MDD์™€ ๋ณ€๋™์„ฑ์„ ๋‚ฎ์ถ”๋ฉด ์ˆ˜์ต์€ ์ž๋™์œผ๋กœ ๋”ฐ๋ผ์˜จ๋‹ค.

  • ์‹ค์ œ๋กœ ์‹ฌ๋ฆฌ์ ์ธ ๋ถ€๋ถ„๋ฟ๋งŒ์ด ์•„๋‹ˆ๋‹ค. ์ˆ˜ํ•™์  ์ด์œ ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.

์†์‹ค๋ฅ 

๋ณธ์ „ ๋งŒํšŒ์— ํ•„์š”ํ•œ ์ˆ˜์ต๋ฅ 

10

11.1

20

25

30

43.7

50

100

66.7

200

  • 10% ์žƒ์œผ๋ฉด 10%์„ ๋ฒˆ๋‹ค๊ณ  ๋งŒํšŒ๋˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋‹ค.

    • 20%๋ฅผ ์žƒ์œผ๋ฉด 25%๋ฅผ ๋ฒŒ์–ด์•ผ ํ•œ๋‹ค. ๊ทผ๋ฐ ์—ฌ๊ธฐ๊นŒ์ง€๋Š” ์–ด๋–ป๊ฒŒ ๊ฐ€๋Šฅํ•  ์ˆ˜ ์žˆ๋‹ค.

    • 30%๋ฅผ ์žƒ์œผ๋ฉด 43.7%์˜ ์ˆ˜์ต์„ ๋‚ธ ์ฝ”์ธ์„ ๋งค๋„ํ•ด์•ผ ๋œ๋‹ค๋Š” ๊ฑด๋ฐ ์ด๊ฑด ๊ต‰์žฅํžˆ ์–ด๋ ต๋‹ค. ํŠนํžˆ 30%์˜ ์†ํ•ด๋ฅผ ๋‚ธ ์‚ฌ๋žŒ์ด๋ผ๋ฉด ๋”๋”์šฑ.

    • ๊ทธ๋ž˜์„œ 20% ์ดํ•˜๋กœ ์œ ์ง€ํ•˜๋ผ๊ณ  ํ•˜๋Š” ๊ฒƒ

    • ๊ทผ๋ฐ, ์•„๋ฌด๋ฆฌ MDD ๋น„์œจ์ด ๋‚ฎ๋”๋ผ๋„ MDD 5%๋ฅผ 5๋ฒˆ ํ•˜๋ฉด 20% ์ด์ƒ ๊นŒ๋จน๋Š” ๊ฒƒ ์•„๋‹Œ๊ฐ€?

  • ๊ทธ๋ž˜์„œ ํ˜„๊ธˆ ๋น„์ค‘์„ ๋Š˜๋ ค ๋ณ€๋™์„ฑ์„ ์กฐ์ ˆํ•ด์•ผ ํ•œ๋‹ค.

๋ณ€๋™์„ฑ

๋ณ€๋™์„ฑ์ด๋ž€ ์ธก์ • ๋ฐฉ๋ฒ•์ด ๋งค์šฐ ๋‹ค์–‘ํ•œ๋ฐ, ๊ฐ€์žฅ ์‰ฌ์šด ๋ฐฉ๋ฒ•์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.

  • 1์ผ ๋ณ€๋™์„ฑ = (์ „์ผ ๊ณ ๊ฐ€ - ์ „์ผ ์ €๊ฐ€)/์‹œ๊ฐ€ x 100 (๋‹จ์œ„๋Š” ๋ฐฑ๋ถ„์œจ)

    • ์‹œ๊ฐ€๋Š” ์‹œ์ž‘๊ฐ€

    • ๊ณ ๊ฐ€๋Š” ๋‹น์ผ ์ตœ๊ณ ๊ฐ€

    • ์ €๊ฐ€๋Š” ๋‹น์ผ ์ตœ์ €๊ฐ€

    • ์ข…๊ฐ€๋Š” ์ข…๋ฃŒ๊ฐ€์ด๋ฉฐ, ์ „์ผ ์ข…๊ฐ€๋Š” ์ „์ผ ๋‹ค์Œ๋‚ ์˜ ์‹œ๊ฐ€์™€ ๋™์ผํ•˜๋‹ค.

  • ๋ณ€๋™์„ฑ = ์ตœ๊ทผ 5์ผ์˜ 1์ผ ๋ณ€๋™์„ฑ ํ‰๊ท 

๋‚ ์งœ

์‹œ๊ฐ€

๊ณ ๊ฐ€

์ €๊ฐ€

์ข…๊ฐ€

1์ผ ๋ณ€๋™์„ฑ

17.12.24

201430

203000

184900

192400

8.99%

17.12.25

192400

197600

191105

196595

3.38%

17.12.26

196600

220000

195500

215105

12.46%

17.12.27

215295

223500

211220

218295

5.70%

17.12.28

218150

218500

187000

194500

14.44%

17.12.29

๋ณ€๋™์„ฑ

8.99%

  • ์˜ˆ๋ฅผ ๋“ค์–ด ์–ด๋–ค ์ฝ”์ธ์˜ ๋ณ€๋™์„ฑ์ด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค๊ณ  ํ•˜์ž

    • 28์ผ์˜ ๋ณ€๋™์„ฑ์€ (218500-187000)/218150 = 14.44% ์ด๋‹ค.

    • ์ด ์ˆ˜์น˜๋Š” ๊ฐ€๊ฒฉ์ด ์ตœ๊ณ  14.44% ์›€์ง์˜€๋‹ค๋Š” ๋œป์ด๋‹ค.

    • ์ด ๋ง์€ ๋‚ด ์žฌ์‚ฐ์ด ์ตœ๋Œ€ 14.44% ์˜ฌ๋ž์„ ์ˆ˜๋„, ๋–จ์–ด์กŒ์„ ์ˆ˜๋„ ์žˆ๋‹ค๋Š” ๋œป์ด๋‹ค.

    • ๊ต‰์žฅํžˆ ์‚ด๋ฒŒํ•œ ์ˆ˜์น˜์ด๋‹ค. ์–ป์œผ๋ฉด ๋ฌผ๋ก  ์ข‹์ง€๋งŒ ์žƒ์—ˆ์„ ๊ฒฝ์šฐ์— ๊ต‰์žฅํžˆ ํฐ ์ˆ˜์น˜์ด๋‹ค. ๋งŒ์•ฝ ์ด๊ฒƒ์ด ํ•˜๋ฃจ ์ดํ‹€์ด ์•„๋‹ˆ๋ผ๋ฉด?

  • ์—ฌ๊ธฐ์„œ ๋ฌ˜์ˆ˜๊ฐ€ ๋‚˜์˜ค๋Š”๋ฐ, ์ „ ์žฌ์‚ฐ์˜ 10%๋งŒ ํˆฌ์žํ•˜๊ณ  90%๋Š” ํ˜„๊ธˆ์œผ๋กœ ๋ณด์œ ํ•˜๋Š” ๊ฒƒ์ด๋‹ค.

    • ์ด๋ ‡๊ฒŒ ๋˜๋ฉด ๋ณ€๋™์„ฑ์€ 14.44%์—์„œ 1.444%๋กœ ํ•˜๋ฝํ•œ๋‹ค. ์ž์‚ฐ์˜ 10%๋งŒ ํˆฌ์žํ–ˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค.

    • ๋งŒ์•ฝ 10์ผ ์—ฐ์†์œผ๋กœ 14.44%์”ฉ ์žƒ์–ด๋„ ๋‚ด ์ž์‚ฐ์€ 85%์ด์ƒ์ด ์‚ด์•„์žˆ๊ฒŒ ๋œ๋‹ค.

    • ๊ทธ๋งŒํผ 10%๋งŒ ์ˆ˜์ต์„ ์–ป๋Š”๊ฑฐ ์•„๋‹ˆ๋ƒ๊ณ ?

      • ๋งž๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฆฌ์Šคํฌ๋ฅผ ์ตœ์†Œํ™”ํ•˜๋ฉด์„œ ์ˆ˜์ต์„ ์–ป์„ ๋•Œ ํˆฌ์ž๊ฐ€ ๋˜๋Š” ๊ฒƒ์ด์ง€, ํ•˜์ด ๋ฆฌ์Šคํฌ ํ•˜์ด ๋ฆฌํ„ด์„ ๋ฐ”๋ผ๋Š” ๊ตฌ์กฐ๋Š” ํˆฌ๊ธฐ์— ๊ฐ€๊น๋‹ค.

      • ๋งŒ์•ฝ, ์–ด๋–ค ํŒ๋‹จ ๊ทผ๊ฑฐ์— ์˜ํ•ด์„œ ํ˜„์žฌ ์ฝ”์ธ์žฅ ์Šน๋ฅ ์ด ๋ญ˜ํ•ด๋„ 60-70%์ด๊ณ , ์†์ต๋น„๋„ ๋†’๋‹ค๋ฉด ํˆฌ์ž ๋น„์ค‘์„ ๋†’์—ฌ๋„๋œ๋‹ค. ๊ทผ๋ฐ ๊ทธ๊ฑธ ์–ด๋–ป๊ฒŒ ์•Œ๊ณ ? ์•„๋‹ˆ๋ฉด ๊ทธ๊ฒŒ ์–ผ๋งˆ๋‚˜ ๊ฐˆ์ง€ ์•Œ๊ณ ?

  • ์ด๋ณด๋‹ค ๋” ์ข‹์€ ๋ฐฉ๋ฒ•๋„ ์žˆ๋‹ค! ๋ฐ”๋กœ ๋ณ€๋™์„ฑ์„ ๋‚ด๊ฐ€ ๊ฐ๋‹นํ•  ์ˆ˜ ์žˆ๋Š” ์ˆ˜์ค€๊นŒ์ง€ ์ค„์ด๋Š” ๊ฒƒ์ด๋‹ค.

    • ๋” ์ข‹์€ ๋ฐฉ๋ฒ•์ด๋ผ ํ•จ์€, ๋ฆฌ์Šคํฌ๋ฅผ ๊ฐ์•ˆํ•˜๋”๋ผ๋„ ์ˆ˜์ต์„ ํ‚ค์šฐ๊ฑฐ๋‚˜, ์ˆ˜์ต์„ ๋œ ์–ป๋”๋ผ๋„ ๋ฆฌ์Šคํฌ๋ฅผ ์ ๊ฒŒ ๋ฐ›๋Š” ์„ ํƒ์ ์ธ ๋ฐฉ๋ฒ•์„ ์ทจํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค.

    • 24์ผ ๋ณ€๋™์„ฑ์„ ๋ณด๋ฉด 8.99%์ด๋‹ค. ์ด ๋ณ€๋™์„ฑ์„ 1%๋กœ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์ด ์žˆ๋‹ค. ๋ฐ”๋กœ ๋‚ด ์ž์‚ฐ์˜ 1/8.99 = 11.12%๋งŒ ํˆฌ์žํ•˜๋ฉด ๋˜๋Š” ๊ฒƒ์ด๋‹ค.

    • ํ•˜๋ฃจ ์ฝ”์ธ์ด 8.99% ์›€์ง์—ฌ๋„, ๋‚ด ์ž์‚ฐ์˜ 11.12%๋งŒ ํˆฌ์ž…ํ–ˆ๊ธฐ ๋•Œ๋ฌธ์— 8.99% X 11.12% = 1%์˜ ๋ณ€๋™์„ฑ์œผ๋กœ ์œ ์ง€๋œ๋‹ค.

    • ๋ณดํ†ต, ๋ฆฌ์Šคํฌ๋ฅผ ํ˜์˜คํ•˜๋Š” ํˆฌ์ž์ž๋Š” 0.5%, ๋ณดํ†ต์€ 1%, ๋ฆฌ์Šคํฌ๋ฅผ ๊ฐ์ˆ˜ํ•˜๋”๋ผ๋„ ๋งˆ์ธ๋“œ๊ฐ€ ๊ฐ•ํ•œ ํˆฌ์ž์ž๋Š” 2%๋ฅผ ์ถ”์ฒœํ•œ๋‹ค.

      • ์™œ 0.5~2% ์ธ๊ฐ€?

      • 10๋ฒˆ ์—ฐ์†์œผ๋กœ 1% ์žƒ์œผ๋ฉด ์ด 9.6% ์žƒ๋Š”๋‹ค. 10๋ฒˆ ์—ฐ์†์œผ๋กœ 2% ์žƒ์œผ๋ฉด 19.3% ์žƒ๋Š”๋‹ค. ์–ด๋–ค ์ผ์ด ์žˆ๋”๋ผ๋„ MDD๋Š” 20%๋ฅผ ๋„˜์œผ๋ฉด ์•ˆ๋œ๋‹ค๋Š” ์ „๋žตํ•˜์— ์ธก์ •๋œ ๋ณ€๋™์„ฑ ์ˆ˜์น˜์ด๋‹ค.

    • ์ด๋ ‡๊ฒŒ ์ „์ฒด ์ž์‚ฐ์˜ ์ผ์ • ๋น„์œจ์— ๊ณ ์ •ํ•ด ํˆฌ์ž ๋น„์ค‘์„ ๊ฒฐ์ •ํ•˜๋Š” ์ž๊ธˆ ๊ด€๋ฆฌ๋ฒ•์€ ๊ณ ์ • ๋น„์œจ ์ž๊ธˆ ๊ด€๋ฆฌ๋ฒ• ์ด๋ผ๊ณ  ํ•œ๋‹ค.

๋ถ„์‚ฐํˆฌ์ž๋Š” ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋‚ฎ์€ ์ฝ”์ธ์œผ๋กœ

  • ๊ฐ€์ƒํ™”ํ ๊ฐ„ ์ƒ๊ด€์„ฑ์ด ์กด์žฌํ•œ๋‹ค. ์ด ๋•Œ ์ƒ๊ด€์„ฑ์ด ๋‚ฎ์€ ์ข…๋ชฉ๋ผ๋ฆฌ ํˆฌ์žํ•ด์•ผํ•œ๋‹ค.

  • ์œ„ ๋งํฌ์—์„œ ์ฝ”์ธ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณผ ์ˆ˜ ์žˆ๋‹ค.

    • ๋ช‡ ์•ˆ๋˜๋Š” ํ•œ๊ธ€ํŒ ์‚ฌ์ดํŠธ์ด๋‹ค.

์ƒ๊ด€๊ด€๊ณ„ ์ˆ˜์น˜

์„ค๋ช…

0.5~1

๊ฐ€๊ฒฉ์ด ๋ฐ€์ ‘ํ•˜๊ฒŒ ์›€์ง์ž„

0.3~0.5

๊ฐ€๊ฒฉ์ด ๊ฑฐ์˜ ๊ฐ™์ด ์›€์ง์ž„

-0.3~0.3

๊ฐ€๊ฒฉ์ด ํฌ๊ฒŒ ๊ด€๊ณ„๊ฐ€ ์—†์Œ

-0.5~-0.3

๊ฐ€๊ฒฉ์ด ๊ฑฐ์˜ ๋ฐ˜๋Œ€๋กœ ์›€์ง์ž„

-0.5~-1

๊ฐ€๊ฒฉ์ด ๋ฐ€์ ‘ํ•˜๊ฒŒ ๋ฐ˜๋Œ€๋กœ ์›€์ง์ž„

  • ํšจ๊ณผ์ ์ธ ๋ฆฌ์Šคํฌ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ๋ณ€๋™์„ฑ ์กฐ์ ˆ์ด ํ•„์ˆ˜์ด๋ฉฐ, ๋ถ„์‚ฐํˆฌ์ž๋Š” ๋ณ€๋™์„ฑ ์กฐ์ ˆ๊ณผ ๊ฒฐํ•ฉํ•  ๊ฒฝ์šฐ MDD๋ฅผ ์กฐ๊ธˆ ๋” ๋‚ฎ์ถ”๋Š” ๋ณด์กฐ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค.

  • ๋ถ„์‚ฐํˆฌ์ž๋ฅผ ์ˆ˜๋ฐฑ ๊ฐœ๋กœ ๋Š˜๋ฆฐ๋‹ค๊ณ  ํ•ด์„œ ํˆฌ์ž์˜ ์œ„ํ—˜๋„๊ฐ€ ์‚ฌ๋ผ์ง€์ง€๋Š” ์•Š๋Š”๋‹ค.

  • ๊ทธ๋Ÿฌ๋‚˜ '๊ฐœ์žก์ฝ”์ธ'์— ํˆฌ์žํ–ˆ๋‹ค๊ฐ€ ํœด์ง€์กฐ๊ฐ์ด ๋˜์–ด๋ฒ„๋ฆฌ๋Š” ๊ฐ€๋Šฅ์„ฑ์€ ์ ์–ด์ง€๊ธด ํ•œ๋‹ค.

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