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1Àå. ¸ÞŸºÐ¼® meta analysis 1. ºñÀ²ÀÇ ºñ±³ 2. Ãâ°£ ÆíÇâ 3. Æò±ÕÀÇ ºñ±³ 4. ¡°meta¡±ÀÇ ´Ù¸¥ ±â´É
2Àå. ÀáÀç Ãþ ºÐ¼® latent class analysis 1. ¹üÁÖÇü ÀÀ´ä°ú ÀÌ¿¡ ´ëÇÑ Åë°èÀû ¸ðÇü 2. values »ç·Ê¿Í ¸ðÇü ¼±Åà 3. ÀáÀç Ãþ ȸ±Í ¸ðÇü 4. ÀÀ¿ë: ÀáÀç Ãþ¿¡ ´ëÇÑ ·ÎÁö½ºÆ½ ȸ±Í 5. ±× ¹ÛÀÇ È¥ÇÕºÐÆ÷ ¸ðÇü
3Àå. ¼ºÇâÁ¡¼ö ¸ÂÃß±â propensity score matching 1. ¹èÈÄ¿äÀÎÀÇ ¼öÁØ Â÷ÀÌ 2. ¼ºÇâÁ¡¼ö ¸ðÇü 3. ÃÖ±ÙÁ¢ ÀÌ¿ô ¸ÂÃß±â 4. ºÎ±¸°£ ¸ÂÃß±â 5. »ç·Ê: Lalonde ÀÚ·á 6. ±× ¹ÛÀÇ ¸ÂÃ߱⠱â¹ý°ú R ÆÑÅ°Áö
4Àå. ÃÖÀûÈ ¾Ë°í¸®Áò optimization algorithm 1. ÃÖ´ë°¡´Éµµ ÃßÁ¤ 2. ºñ¼±Çü ȸ±Í 3. TSP (traveling salesman problem)
5Àå. °áÃø°ª ´ëü missing value imputation 1. °áÃø°ª ´ëü 2. ´ëü ÀÚ·áÀÇ È°¿ë 3. ´ëü ¹æ¹ý°ú ¼Õ½Ç 4. MAR »óȲ
6Àå. ´ÙÂ÷¿ø ôµµÈ multidimensional scaling 1. °Å¸® Çà·Ä 2. °íÀüÀû MDS 3. ºñ°è·®Çü MDS 4. iso map 5. Â÷¿øÀÇ °áÁ¤
7Àå. Ç¥º»Å©±â¿Í °ËÁ¤·Â sample size and power 1. Á¤±ÔºÐÆ÷ÀÇ Á᫐ 2. t-°ËÁ¤ 3. ºñÀ²ÀÇ °ËÁ¤ 4. »ó°üÀÇ °ËÁ¤ 5. ±× ¹ÛÀÇ °ËÁ¤
8Àå. º×½ºÆ®·¦ ¹æ¹ý bootstrap method 1. º×½ºÆ®·¦ ¹æ¹ýÀ̶õ? 2. º×½ºÆ®·¦ »ç·Ê: »ó°ü°è¼ö 3. º×½ºÆ®·¦ »ç·Ê: ·ÎÁö½ºÆ½ ȸ±Í 4. º×½ºÆ®·¦ »ç·Ê: µÎ µ¶¸³Ç¥º»ÀÇ Á᫐ °£ Â÷ÀÌ
9Àå. ·Î¹ö½ºÆ® ȸ±Í¿Í ºÐÀ§¼ö ȸ±Í robust and quantile regression 1. ·Î¹ö½ºÆ® ȸ±Í 2. ºÐÀ§¼ö ȸ±Í
10Àå. ÀϹÝȼ±Çü¸ðÇü generalized linear model 1. ÀϹÝȼ±Çü¸ðÇüÀ̶õ? 2. Æ÷¾Æ¼Û ȸ±Í 3. ·ÎÁö½ºÆ½ ȸ±Í 4. °¨¸¶ ȸ±Í 5. ¿ä¾à
11Àå. ±¹¼ÒÀû ȸ±Í LOESS 1. ±¹¼Ò °¡ÁßÄ¡ 2. ±¹¼Ò ¼±ÇüÀÌÂ÷½ÄÀÇ ÀûÇÕ 3. Engine Exhaust Emissions »ç·Ê 4. Sulfate Deposits »ç·Ê 5. ±â¼úÀû ¼¼ºÎ »çÇ×
12Àå. ÀϹÝÈ°¡¹ý¸ðÇü generalized additive model 1. ±âº» »ç·Ê¿Í ¹æ¹ý·Ð 2. Áظð¼öÀû ȸ±Í¸ðÇü 3. ¸ðÀÇÀÚ·á »ç·Ê
13Àå. R Ä÷¯¿Í »êÁ¡µµ r colors and scatterplot 1. RÀÇ Ä÷¯ 2. »êÁ¡µµ ÀÀ¿ë: Á¦3ÀÇ º¯¼ö 3. »êÁ¡µµ ÀÀ¿ë: 4. »êÁ¡µµ ÀÀ¿ë: lowess ÆòÈ°
14Àå. Åë°è ±×·¡ÇÁ 1 statistical graph 1. ³ª¹« Áöµµ 2. ¸ðÀÚÀÌÅ© ÇÃ·Ô 3. ¿ Áöµµ
15Àå. Åë°è ±×·¡ÇÁ 2 statistical graph 1. 2º¯·® ÀÚ·áÀÇ ¹Ðµµ 2. 3º¯·® ÀÚ·áÀÇ ½Ã°¢È 3. ´Ùº¯·® ÀÚ·áÀÇ ½Ã°¢È
16Àå. Çà·Äµµ¿Í ´ëÀÀºÐ¼® biplot and correspondence analysis 1. Çà·Äµµ 2. ´ëÀÀºÐ¼® 3. ´ÙÁß´ëÀÀºÐ¼®
17Àå. SVM (Support Vector Machine) 1. ¼±Çü SVM ºÐ·ù 2. ºñ¼±Çü SVM ºÐ·ù 3. ¼±Çü ¹× ºñ¼±Çü SVM ȸ±Í
18Àå. ³ª¹« ¾Ë°í¸®Áò tree algorithm 1. CART 2. ·£´ý Æ÷¸®½ºÆ®
19Àå. KPCA¿Í LLE (kernel PCA and locally linear embedding) 1. Ä¿³Î PCA 2. LLE
20Àå. ¿¹ÃøÇÔ¼öÀÇ ½Ã°¢È visualizing predictive functions 1. Á¶°ÇºÎ ¿¹Ãø ±×·¡ÇÁ 2. ȸ±Í¸ðÇüÀÇ °æ¿ì 3. ¼³¸íº¯¼ö°¡ ¸¹Àº °æ¿ì
ºÎ·Ï I. R¿¡¼ µ¥ÀÌÅͼ¼Æ® ´Ù·ç±â (ÃʱÞ) manipulating datasets in R ºÎ·Ï II. R¿¡¼ µ¥ÀÌÅͼ¼Æ® ´Ù·ç±â (°í±Þ) manipulating datasets in R
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