この英文を正確に並び替えて欲しいです。 お願いします🙇‍♀️

309 10. In New Zealand, there are [1 sheep/②six/③many times /⑤ as] as there are people.

8の英文を正確に並び替えて欲しいです。 お願いします🙇‍♀️

6. [the books/② the desk / 3 are/4 on ] my father's. 7. The Mississippi is [① longest/②fourth/③river/the] in th 8. He returned home [①could/②as/③as/he/⑤quickly ].

確率の問題なのですがなぜA2.A3の場合とA3.A2の場合を別々に分けているのかが分からないです。教えて頂きたいです。よろしくお願いいたします。

例題 めよ. 13-2 7/19 718 半径1の円に内接する正六角形の頂点を Au As, A. とする。これらから、 無作為に選んだ3点(重複を許す)を頂点とする三角形の面積の期待値(平均値)を求 考える。 【解答】 2つ以上が一致するような3点が得られたときは,三角形の面積は0と 六角形 A.A.AsA.AsA が内接する円の中心を0とする。 AL A6 As As A 無作為に選んだ1つの頂点をA1とし,固定して考える. このとき、他の2頂点の選び方の総数は62=36 (通り) あり,これ らは同様に確からしい. そして、次の4つの場合が考えられる. (ア)三角形A1A2A6 と合同な三角形ができる. 三角形A1A3A5 と合同な三角形ができる. (ウ)三角形A1A2A4と合同な三角形ができる. (エ) A1 を含めて2点以上が一致する. のとき,他の2頂点について, (A2, A3), (A3, A2), (A2, A6), (As A2), (A6, A5), (A5, Ag) の場合がある. よって, ※重複を許すので かくりつの合計」にならないことに 注意!! 対称性から1つの頂点は固定 して, 残り 2頂点の選び方を考 えればよい. 三角形の形で分類しておく. 6 1 (ア)の確率) = 36 6' 3146 63 (イ)のとき,他の2頂点について, (A3, As), (A5, Ag) の場合があ よって, 2 ((イ)の確率)= 1 31×2 36 18 (例)のとき、他の2頂点について, (A2, A4), (A4, A2), (A2, A5),

I was amazed that someone as gifted as him could also be so gentle and kind. この文の訳は「彼ほど才能のある人がここまで優しく思いやりもあることに驚いた。」です。 「as 〜 as」は「〜と同様に」... 続きを読む

並び順を教えてください。

トマトほど使い勝手の良い野菜はない。 ( many / the / vegetable / as / as / other / tomato/has/no / uses).

語彙問題(発展) カッコに入る( l- ) で始まる単語を教えていただけるとうれしいです。よろしくお願いします。

A: Frida, as assistant manager, I'd like more ( regarding the team on my own. ) to make decisions B: I'm sorry, Burt, but you've only been in the position a month, so I need you to report to me on everything, at least for now.

比較の問題です。教えてください。よろしくお願いします

⑩ younger than [帝塚山学院大 ] 1.次の文の( )に入る適当な語句を①~⑩から選びなさい。 (1) A: Can you believe she is 32 years old? I thought she was only 20. B: Yes, she looks ( ) she is. as young as as old as older than (2) Your public speech today was ( ) than the last one. more better much better ) the more for her honesty. good (3) I like her ( ①all (4) Tom has ( much ). ③to best far twice as many books as I have twice as many as books than I have as more twice as books of me as twice of many books as me ) qualified to do the job than she is. > much (5) I am no ( ①far more So 〔桜美林大〕 [ 名古屋学芸大 ] [関東学院大〕 [ 京都女子大 ]

分詞構文の問題です。見分け方について詳しく教えてもらいたです。(１)の問題の答えはウですが他の英文も同じような用法になる思うんです。例えばアはルールを知らなかったから私は間違えた（理由）とも訳せるくないですか？あやふやになっているので他の問題の解説も含めてよろしくお願いしますか

問2 以下の各文 (1)〜(IV) は、本文中に登場する分詞構文を含んだ文である。 それぞれの文が表す意味・用法 に最も近いものを、ア〜エの中から一つ選び、 記号で答えなさい。 (I) Checking recipes carefully, the head baker made sure everything was perfect. イ I Not knowing the rules, I made a mistake. Given the chance, he could do better. Hearing the bell, she stood up. He walked out, slamming the door behind him. A.7 (II) He walked from station to station, giving advice to the staff and encouraging them with kind words. 7 Wanting to help, I offered my seat. イ Running down the street, he waved at me. I Being hungry, he bought a sandwich. Being tired, he took a nap. A. T

2つめのandはwas toldとwere askedを繋いでると考えるのがダメな理由を教えてください

今 One group was told that they were going to get feedback, some X of which was good and some of which was bad, and were asked De obuloni which they wanted to hear first. t31.1

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