发布时间：2019-12-12 06:50:39|查询体彩大乐透开奖号码|
来源 ：去哪网

Updated: Feb. 28, 2019

1. This graph shows the length of daily delays on one woman’s 40-minute commute between Munich and a town in the German countryside during 2018. The graph originally appeared elsewhere on NYTimes.com.

The commuter knitted two rows each day. Gray for delays under five minutes, pink for up to 30 minutes, and red for a delay of more than a half-hour or delays in both directions.

After looking closely at the graph above (or at this full-size image), think about these three questions:

• What do you notice?• What do you wonder?What are you curious about that comes from what you notice in the graph?• What might be going on in this graph?Write a catchy headline that captures the graph’s main idea. If your headline makes a claim, tell us what you noticed that supports your claim.

The questions are intended to build on one another, so try to answer them in order. Start with “I notice,” then “I wonder,” and end with “The story this graph is telling is ….” and a catchy headline.

2. Next, join the conversation by clicking on the comment button and posting in the box that opens on the right. (Students 13 and older are invited to comment. Teachers of younger students are welcome to post what their students have to say, or they can have their students use this same activity on Desmos.)

3. After you have posted, read what others have said, then respond to someone else by posting a comment. Use the “Reply” button or the @ symbol to address that student directly.

On Wednesday, Feb. 27, our collaborator, the American Statistical Association, will facilitate this discussion from 9 a.m. to 2 p.m. Eastern Time to help students’ understanding go deeper. You might use their responses as models for your own.

4. On the afternoon of Thursday, Feb. 28, we will reveal more information about the graph at the bottom of this post. Students, we encourage you to post an additional comment after reading the reveal. How does the original New York Times article and the moderators’ comments help you see the graph differently? Try to incorporate the statistical terms defined in the Stat Nuggets in your response.

_________

• Read our introductory post, which includes information about using the “Notice and Wonder” teaching strategy.• Learn about how and why other teachers are using this feature, and use the 2018-19 “What’s Going On in This Graph?” calendar to plan ahead for the 25 Wednesday releases. • Go to the A.S.A. K-12 website, which includes This is Statistics, resources, professional development, student competitions, curriculum, courses and careers.

_________

Updated: Feb. 28, 2019

This graph appeared in the New York Times article “How Bad Was Her Commute? This ,650 Scarf Tells the Tale.” Read the article to learn more about the scarf graph’s story.

Some of you asked how a scarf can be a graph. David of California wrote, “I noticed within the first few seconds that it’s a scarf and not digital. Which I find odd for you The New York Times, but new is cool.” David, actually the knitter Claudia Weber is the cool one. She made the scarf. Kalyn of New York responded by sharing how this striped scarf is a graph. “It’s measuring this woman’s commute times and delays.” Graphs use numerical data, like times. Ms. Weber could have represented the data as a bar graph or time series, but isn’t it more engaging as a scarf? So engaging that someone paid ,650 for it on eBay. (Would they have paid less if the train were on time?)

Data visualization (see below Stat Nugget) knitting is now a rage among statisticians and their friends, especially using climate change data. In 2015, the University of Georgia marine scientist Joan Sheldon translated temperature change data into a scarf. Using a simple color coding system, she knitted “graphs” based on published data. Now, the Tempestry Project sells kits with yarn and National Oceanic and Atmospheric Administration data and there are Facebook groups and a website.

Here are some of the student headlines that really capture the meaning of this graph: “Scarf Makes Train Look Bad” by SlickNinja of Baltimore and “Knitting Congestion Into Art” by McBride of Denver; and these three from New Hampshire — “Weaving Through Heavy Traffic” by Daniel and Nate, “Warming Up with Delays” by Nathaniel, and “Knitting Through the Rows of Traffic” by Greg.

You may want to think critically about these additional questions.

• Estimate the proportion of days in the year that the train was on time. When was the train most frequently on time? Explain your reasoning.

• That big red bar — when did it happen and how many days or weeks was it? Explain how you arrived at your answers.

• You can make your own “data viz” scarf or other creative data visualization. Find some data that interests you, such as the temperature or sporting events attendance, and then graph the data for a year or longer. After color coding the data, you can visualize it on graph paper, a knit scarf or anything else. Your classmates could pick the same data set or different ones. Either way, have a discussion by comparing what you notice and wonder from these graphs.

• The knitting commuter sold the scarf on eBay and contributed the 7,550 euros (,650) to her favorite charity. The winning bid was made by the digital team of the Deutsche Bahn — the state rail operator of the knitter’s train. Estimate the amount the bidder paid for every minute late during the knitter’s commute. Explain how you arrived at this estimate, including how you dealt with the time ranges corresponding to the different colors.

Below in the Stat Nuggets, we define and explain mathematical terms that apply to this graph. Look into the archives to see past Stat Nuggets.

Thank you for participating in “What’s Going On in This Graph?”, which is intended to help you think more critically about graphs and the underlying data. Critical thinking is an essential element of statistics, the science of learning from data. Data visualizations, like these graphs, are an important part of statistics. They help us to understand and learn from data.

Keep noticing and wondering. We continue to welcome your responses.

Join us Wednesday, March 6 to notice and wonder about vaping. We look forward to your responses between 9 a.m. - 2 p.m. Eastern Time during the live online moderation.

________

Stat Nuggets for “How Bad Was Her Commute? This ,650 Scarf Tells the Tale”

STACKED BAR GRAPH & TIME SERIES GRAPH

A stacked bar graph summarizes values from a categorical data set. From the graph, the proportion of the time that each value occurred can be compared.

A time series graph shows how the values from a quantitative variable change over time.

The commuter delay scarf graph is neither a stacked bar graph nor a time series, but incorporates aspects of each.

It is not a stacked bar graph because the three categories (delays of less than 5 minutes, delays of 5 - 30 minutes, and delays of more than 30 minutes or in both directions) are not grouped together but are distributed on a timeline.

It is not a time series graph because the variable is not quantitative. It is categorical: delays that are short (less than 5 minutes), medium (5 to 30 minute), and long (more than 30 minutes or in both directions.)

DATA VISUALIZATION or DATA VIZ

Data visualization is a means to represent data with graphs, infographics and other visual representations. Statisticians, computer scientists and graphic artists in the “data viz” field have the goal to clearly and efficiently communicate the story behind the data.

The commuter delay scarf graph is a data visualization representing data on daily commuter delays into knitted rows on a scarf, and it has become a media star among “data viz” aficionados.

_________

The graphs for “What’s Going On in This Graph?” are selected in partnership with Sharon Hessney, a mathematics teacher in Boston. Ms. Hessney wrote the “reveal” and Stat Nuggets with Roxy Peck, professor emerita at California Polytechnic State University San Luis Obispo, and moderated online with Jennifer Mueller, a mathematics teacher at Parkway North High School in St. Louis.

B:

查询体彩大乐透开奖号码【清】【晨】，【当】【阳】【光】【照】【耀】【在】【床】【上】【的】【时】【候】，【白】【小】【眠】【醒】【了】【过】【来】。 【阳】【台】【上】【的】【月】【季】【花】【散】【发】【着】【淡】【淡】【的】【清】【香】 【她】【习】【惯】【性】【的】【伸】【出】【双】【手】【触】【摸】【着】【枕】【边】【人】【的】【地】【方】。【不】【出】【意】【料】【的】，【那】【块】【属】【于】【顾】【廷】【爵】【的】【位】【置】【连】【余】【温】【都】【已】【经】【不】【存】。 【白】【小】【眠】【下】【意】【识】【的】【这】【才】【是】【睁】【开】【了】【眼】【睛】，【手】【臂】【收】【回】【的】【瞬】【间】，【接】【触】【碰】【到】【了】【湿】【润】【的】【枕】【头】。 “【阿】【爵】……” 【微】【弱】【的】【呼】【声】，【正】【是】【从】

【苏】【白】【没】【听】【到】【这】【些】【声】【音】，【眼】【睛】【里】【只】【有】【一】【个】【人】！【李】【泰】！ 【这】【些】【人】【高】【马】【大】【的】【亲】【兵】【在】【苏】**【前】【就】【仿】【佛】【是】【土】【鸡】【瓦】【狗】【一】【样】！【没】【有】【任】【何】【一】【人】【能】【挡】【得】【住】【苏】【白】【一】【拳】！【就】【在】【苏】【白】【马】【上】【就】【要】【冲】【到】【李】【泰】【身】【前】【十】【米】【的】【时】【候】，【忽】【然】【一】【声】【破】【空】【声】【响】【起】！ 【苏】【白】【就】【感】【觉】【自】【己】【脊】【梁】【骨】【一】【凉】，【背】【后】【瞬】【间】【冒】【出】【一】【阵】【冷】【汗】，【肌】【肉】【反】【应】，【身】【体】【下】【意】【识】【的】【铁】【马】【桥】【下】【腰】【躲】

【谁】【也】【没】【有】【想】【到】【闻】【音】【的】【态】【度】【会】【强】【硬】【到】【如】【此】【的】【地】【步】，【可】【仔】【细】【一】【想】，【却】【又】【能】【理】【解】，【虽】【然】【在】【这】【个】【时】【代】，【女】【人】【并】【不】【受】【人】【重】【视】，【哪】【怕】【是】【同】【族】【的】【女】【人】，【可】【那】【也】【看】【这】【个】【女】【人】【是】【谁】，【听】【訞】，【可】【是】【闻】【音】【的】【女】【儿】，【地】【位】【根】【本】【不】【可】【能】【不】【是】【普】【通】【女】【人】【所】【媲】【美】【的】，【更】【别】【说】【听】【訞】【自】【身】【的】【才】【华】【也】【极】【其】【出】【众】，【若】【是】【没】【有】【她】，【每】【到】【炎】【热】【之】【时】，【他】【们】【还】【只】【能】【用】【草】【裙】【遮】

“【小】【姑】【娘】，【往】【后】【退】【一】【点】，【别】【站】【在】【门】【边】！” “【知】【道】【了】！” 【秋】【玹】【提】【声】【回】【应】【着】【后】【退】，【头】【顶】【的】【灯】【泡】【也】【随】【即】【应】【景】【般】【地】【闪】【烁】【两】【下】。【她】【几】【乎】【是】【与】【身】【边】【的】【恶】【鬼】【擦】【身】【而】【过】，【咽】【了】【下】【口】【水】【指】【尖】【滑】【进】【口】【袋】【深】【处】【的】【小】【人】【吊】【坠】，【不】【管】【怎】【么】【说】【还】【是】【提】【前】【做】【好】【了】【两】【手】【准】【备】。【因】【为】“【它】”【刚】【才】【也】【说】【了】，【大】【门】【是】【不】【可】【能】【被】【打】【开】【的】，【而】【何】【况】…… 【轰】—

【本】【书】【到】【这】【里】【就】【告】【一】【段】【落】【了】。 【主】【要】【的】【剧】【情】【力】【所】【能】【及】【的】【填】【了】【坑】，【现】【在】【很】【少】【有】【书】【会】【解】【释】【的】“【系】【统】”【的】【来】【源】【等】，【也】【尽】【量】【在】【符】【合】【克】【苏】【鲁】【框】【架】【的】【前】【提】【下】【进】【行】【了】【解】【答】，【阿】【卡】【姆】【的】【种】【种】【也】【尽】【可】【能】【给】【予】【了】【说】【明】【和】【交】【代】。【眼】【睛】【不】【好】【的】【原】【因】，【最】【后】【一】【个】【月】【的】【字】【码】【的】【很】【痛】【苦】，【但】【终】【于】【还】【是】【把】【它】【完】【成】【了】。 【不】【断】【波】【折】【的】【干】【眼】【症】（【还】【有】【感】【染】……）查询体彩大乐透开奖号码……【罗】【艺】【龙】【母】【亲】【就】【直】【直】【的】【坐】【立】【了】【起】【来】，【惊】【悚】【得】【田】【珩】【媛】【抱】【紧】【罗】【艺】【龙】【父】【亲】。 “【我】【妈】【听】【见】【你】【内】【心】【埋】【葬】【的】【爱】【了】，【所】【以】【她】【活】【过】【来】【了】，【至】【于】【把】【你】【恐】【怖】【成】【这】【样】？” 【罗】【艺】【龙】【冷】【笑】【着】，【似】【乎】【准】【备】【询】【问】【田】【珩】【媛】【有】【关】“【天】【国】【之】【人】”【的】【事】。 【但】【田】【珩】【媛】【却】【猛】【然】【拽】【紧】【罗】【艺】【龙】【宽】【厚】【的】【手】【臂】，【一】【个】【劲】【儿】【的】【摇】【来】【晃】【去】。 “【龙】【龙】，【我】【想】【了】【好】【久】，

【龙】【王】【没】【有】【吓】【唬】【韩】【朵】【朵】。 【别】【说】【被】【龙】【血】【改】【造】【了】，【就】【算】【是】【普】【通】【人】【靠】【着】【一】【步】【步】【修】【炼】，【最】【后】【修】【炼】【成】【为】【强】【者】，【这】【样】【的】【强】【者】【其】【血】【肉】【对】【普】【通】【人】【来】【说】【也】【具】【有】【莫】【大】【的】【功】【效】，【普】【通】【人】【吃】【了】【之】【后】，【也】【能】【提】【升】【身】【体】【素】【质】。 【就】【像】【许】【多】【小】【说】【里】【写】【的】【食】【用】【妖】【兽】【血】【肉】【一】【样】。 【当】【然】，【巨】【龙】【一】【族】【做】【为】【多】【元】【宇】【宙】【的】【顶】【尖】【种】【族】，【龙】【血】【的】【效】【果】【是】【最】【顶】【尖】【的】，【远】

【默】【默】【的】【将】【两】【个】【头】【箍】【都】【收】【了】【起】【来】。 【洛】【璃】【烟】【抬】【起】【头】，【便】【看】【见】【坐】【在】【最】【前】【方】【的】【邵】【熠】【阳】，【回】【头】【看】【了】【自】【己】【一】【眼】。 【不】【过】【坐】【在】【她】【这】【个】【方】【向】【的】【所】【有】【小】【太】【阳】，【都】【认】【为】【自】【家】【哥】【哥】【这】【是】【在】【看】【自】【己】。 【全】【部】【都】【扯】【在】【嗓】【子】【尖】【叫】【了】【起】【来】。 【那】【声】【浪】【都】【快】【要】【把】【场】【馆】【的】【顶】【部】【给】【掀】【翻】【了】。 【洛】【璃】【烟】【觉】【得】【自】【己】【全】【身】【的】【血】【液】，【再】【次】【热】【腾】【了】【起】【来】。 【果】

【她】【这】【话】【就】【是】【明】【确】【告】【诉】【虚】【神】“【我】【明】【白】【你】【穿】【成】【这】【样】【的】【用】【意】【了】”，【好】【了】，【入】【正】【题】【吧】。 【虚】【神】【的】【道】【行】【不】【在】【她】【之】【下】，【闻】【言】【自】【然】【也】【笑】【得】【和】【善】，【温】【声】【细】【语】【道】：“【并】【无】【要】【事】。【不】【过】【天】【尊】【大】【人】【既】【然】【将】【神】【庭】【内】【的】【事】【务】【交】【与】【我】【手】，【日】【常】【的】【巡】【视】【自】【然】【不】【能】【懈】【怠】。” “【嗯】？”【白】【玲】【珑】【略】【一】【思】【忖】。 【原】【本】【黎】【天】【就】【对】【虚】【神】【委】【以】【重】【任】，【内】【部】【事】【务】【都】【交】

“【梵】【儿】【小】【心】！” “【小】【梵】【儿】！” 【殷】【离】【修】【和】【孤】【南】【翼】【同】【时】【惊】【呼】【出】【声】，【调】【转】【方】【向】，【然】【而】，【他】【们】【距】【离】【太】【远】，【眼】【看】【着】【黑】【熊】【已】【经】【到】【了】【慕】【梵】【希】【和】【小】【非】【跟】【前】。 【慕】【梵】【希】【怔】【愣】【瞬】【间】，【一】【把】【抓】【住】【小】【非】【的】【手】【往】【后】【退】，【就】【在】【这】【时】，【眼】【前】【一】【个】【黑】【影】【闪】【过】，【黑】【鳞】【竟】【然】【跳】【飞】【起】【来】【冲】【到】【了】【黑】【熊】【脑】【袋】【上】，【那】【犀】【利】【的】【爪】【子】【一】【下】【子】【抓】【住】【了】【黑】【熊】【的】【眼】【睛】！

（责任编辑：粘佩璇）

新闻排行

独家原创

热点专题