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NEW QUESTION: 1
A. Option A
B. Option B
C. Option C
D. Option D
Answer: C
NEW QUESTION: 2
ユーザーがネットワークのセグメントで断続的なパフォーマンスの問題を報告します。トラブルシューティングの方法論によれば、技術者は顧客から初期情報を収集した後、次の手順のどれをNEXTにすべきですか？
A. 推定原因の理論を確立します。
B. 調査結果、アクション、および結果を文書化します。
C. 可能であれば、問題を複製します。
D. 問題を解決するための行動計画を確立します。
Answer: C
NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、記載された目標を達成する可能性のある独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合もあれば、正しい解決策がない場合もあります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
複数の列に欠損値を含む数値データセットを分析しています。
機能セットの次元に影響を与えることなく、適切な操作を使用して欠損値を消去する必要があります。
すべての値を含めるには、完全なデータセットを分析する必要があります。
解決策：最後の観測キャリーフォワード（IOCF）メソッドを使用して、欠落したデータポイントを補完します。
ソリューションは目標を達成していますか？
A. いいえ
B. はい
Answer: A
Explanation:
Instead use the Multiple Imputation by Chained Equations (MICE) method.
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Last observation carried forward (LOCF) is a method of imputing missing data in longitudinal studies. If a person drops out of a study before it ends, then his or her last observed score on the dependent variable is used for all subsequent (i.e., missing) observation points. LOCF is used to maintain the sample size and to reduce the bias caused by the attrition of participants in a study.
References:
https://methods.sagepub.com/reference/encyc-of-research-design/n211.xml
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/