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数学 中学生

数学の条件付き確率の問題で、解説の意味がわからなかったので教えていただきたいです! 問題は↓↓ 20本のくじの中にあたりが5本ある。 このくじから1本ずつ順に、引いたくじはもとに戻さずに2本を引いたら、2本の中に当たりくじがあることがわかった。 このとき、1本目... 続きを読む

家はない よって n 42=0 (n+6xn-7)=( 2≤n であるから n=7 したがって、赤玉の個数は7個 314 2本の中に当たりくじがあるという事象を A. 1本目のくじが当たりくじであるという事象をB とする。 とも 事象Aは「2本ともはずれくじである」という 事象の余事象であるから 15 14 P(A)=1- × 20 19 21 17 1- 38 38 5 1 また P(A∩B)=P(B)= 201 求める確率はP(B) であるから 互い PA(B)= P(A∩B) 1 17 P(A) 4 - 38 1 38 19 × 17 34 (2) 場合 Bから取り出す時点で、Bに 王3個が入っている。 よって、この場合の確率は ICSCLXICOC C2 C2 [3] A,Bともに黒王2個を Bから取り出す時点で、B 王4個が入っている。 よって、この場合の確率 5 注意 事象 Bは事象Aに含まれるから, BC2X4C2 C2 CC 18 したがって、求める確率は 5 20 5 + + 63 63 63 317 抜き取った製品が、 るという事象をそれぞ 取った製品が不良品で る。 P(A∩B)=P (B) である。 =P(B)である。 215 [1]1回目に赤玉を取り出す場合 赤玉 抜き取った製品が不良 このとき,A,B,C

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TOEIC・英語 大学生・専門学校生・社会人

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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