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Mathematics Senior High

メネラウスの定理の使い方が曖昧です。それぞれの辺を選ぶのを間違えてしまいます。どうやって問題をとく時に判断すればいいですか。

例題 262 メネラウスの定理と面積比 M. N ★★★ し, ALとCNの交点をP, ALとBMの交点を Q, BM とCNの交点をR とする。 次の三角形の面積を △ABCの面積Sを用いて表せ。 (1) ABCR (2) APQR RoAction 高さ (底辺) の等しい三角形の面積比は, 底辺 (高さ)の比とせよ 逆向きに考える (ABCR から始めて、△ABCへ広げていくには、どの線分の比が必要だろうかっ 思考のプロセス △BCRと 見方を変える 似た構図 直接求めるか? M (2) APQR △ABC- (△PQR 以外の部分) と考えるか? B L ~ (1) C TOPE よって, △ABMと直線 CN につ いて, メネラウスの定理により 解 (1) AN:NB=1:2であり, CM:MA=1:2よりで交わることは、 CM: AC ③3 1:3eek M ために, BM BRをメネ △BCR → △BCM →△ABCと広げていく R める。 ラウスの定理を用いて表 B AS AC MR BN P 1 CM RB NA 3 MR 2 RM 1より 1 RB 1 BR 16 VMB LQ よって ゆえに RM:BR = 1:6 BM:BR = 7:6 例題 255 したがって 6 ABCR = = ABCM = 6 . 7 3 (2)(1)と同様に, △BCN と直線 AL, △CAL と直線 BM について, メネ ラウスの定理を用いると ・△ABC: = 27 S ACM: AC = 1:3 例題 255 BA NP CL =1より AN PC LB 3 NP =1 PC 6 1 PC 1 △CAP=△ABQ= 2 CN= UM よって NP:PC = 1: = -S R 7 B よって C △PQR =△ABC- (△BCR + △CAP + △ABQ) M9 MBL QA MC 3. LQ.2 1 QA 1 CBLQ.. AM =1よ =1 2 =S-3・ S= S 7 よって LQ:QA=!

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TOEIC・English Undergraduate

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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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