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英語 中学生

4(4),(6) 6(2)が分かりません。何が入るのか教えて欲しいです また間違いがありましたら教えて頂きたいです🙇‍♀️

19 間接疑問文 151 4 <間接疑問文〉 次の各組の文がほぼ同じ内容を表すように、空所に適語を書きなさい。 Do you know his address? □ (1) Do you know I don't know his birthday. what he lives □(2) II don't know when he was bom de I don't know what I should do. 〈日本大第一高〉 < 東明館高 > 明治大付明治高〉 □(3) I don't know what to Do you know my age ? □ (4) Do you know how 〈大阪教育大附平野高〉 ? I want to know the name of your cat. □(5) I want to know what your cat hawe called. Ask him the number of students in his class. <慶應義塾高改〉 ☐ (6) Ask him students are in his class. Please tell her what time she should start. 〈成城学園高 > □(7) Please tell her when to start. 5 <間接疑問文〉 次の日本文の意味を表すように、空所に適語を書きなさい。 □(1) 彼はどこに住んでいるのだろう。 I wonder where ohe □(2) だれも将来何が起こるかわからない。 lives one knows what □(3) 彼女はなぜ今日休んでいるのだろう。 I dont know why □(4) 誰がこの野球チームのキャプテンだと思いますか。 do you is the □ (5) あなたは,彼がいつここを出発すると思いますか。 When do You □(6)このスポーツに興味があるのはだれかしら。 I wonder who TS 〈清風高〉 <早稲田実業学校高等部改〉 will happen in the future. ske captain think 〈 お茶の水女子大附高改〉 absent today. <早稲田大高等学院〉 of this baseball team? 〈慶應義塾志木高改〉 he will leave here? 〈お茶の水女子大附高改〉 interested in this sport. 6 〈間接疑問文〉 次の文を( )内の指示にしたがって書きかえなさい。 □(1) Can I get there in time? I don't know it. (1つの文に) I don't show how can get in time. □ (2) Where are my friends? (do you think と組み合わせて1つの文に Wheredo van think □(3) Does, Betty come back soon? Please ask Betty. (1つの文に) ■注 Please ask Betty when you come back □age 年齢 future 将来 〈大阪星光学院高〉 〈久留米大附設高〉 <土佐塾高 >

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英語 中学生

これわかんない笑笑 7時までに誰かといてー

科書 P.58 学習日 ◆ 英東・2 月 Lv2 Lv2 ょう。 Lv2 ③2 (4) You (am you study. 3125 Must イ Can No, you don't have to. 2(6) You (ア can イ may it's cold today. ◆英東・2 イ was ウ must I have) not play the game when ↓ [ ] 例文 ウ May Will)I_go there? P ] 例文 should Q will) wear a sweater because 例文 2 次の日本語に合うように、 に適当な語を書きなさい。 す。) 1年 Unit 2 Lv2 きません。) 1年 Unit 2 You mugn4 run きますか。) 1年 Unit 2 もいいですか。 ) Lv2 32(2) may not Unit 1 she ③2 (1) あなたたちはここで走ってはいけません。 2 (2) 彼は今日の午後、ベッキーを訪問しないかもしれません。 He Lv ③12 (3) 彼女は3時に家にいるでしょうか。 Lv ③12 (4) 彼はこの重いコンピュータを運ぶことができます。 He 3 次の日本語に合うように、 ( 例文 carry this heavy computer. 日 内の語を並べかえ、正しい英文にしなさい。 Lv1 1 32(1)彼らは10時前に寝なければなりません。 例文① (to / before / they / ten / go / must / bed). 例文 here. 例文 visit Becky this afternoon. 例 at home at three? Lv2 (2)私たちは今日の午後、公園に行くべきです。 例文の いかもしれません。) (the / go / afternoon/to/park/we / should / this か。) ません。) P.8 Lv2 P.8 ③32(3) 授業中にスマートフォンを使ってもよいですか。 (smartphone/I/class/ may / my / in / use )? 例文 さい。 norrow. ] [例文 4 次の英文を ( 内の指示にしたがって書きかえなさい。 なりませんか。) Lv1 です。) I his feelings. What can say when he's giving mel Lv1 Lv1 2 (1) Jane plays tennis well. (「・・・できない」 という文に 例文 〇 ⑩ and gead/valbat) ③2 (2) We can play soccer after school. (「…してもいいですか」という文に 下部の ③2 (3) He makes dinner this evening. (「・・・かもしれない」という文 Gakes Lv3 L ? かな? 例文 ② 例文 0 ③2 (4) Do I have to open the window? (助動詞を使ってほぼ同じ意味の文に) 例文1 luestion? ]JXO Lv2 ③12 (5) She is going to visit your house. (助動詞を使ってほぼ同じ意味の文に) 例文 6 Lv1 [ 例文 ③12 (6) Naoki may listen to music. (否定文に) 例文① -25-

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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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