# data[].attributes.body
Student Names : Vanessa Johns  (P.4)  Jane Doe (P.4) Betty Sue (P 1)

Topic: Student Athletes

Question: Is there an association between student athletes and the amount of sleep received per night?

Resource Link

http://globalsportsdevelopment.org/good-nights-sleep-student-athletes/
From Mrs. Y 4/30 4:35- Do you mean you are comparing nonathletes vs.  athletes?

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Student Names: Annyka Mendoza (p.1) Jaevin Poquiz (p.1) Nicole Lillie (p.3)
Topic: Teen exercise
Question: On average, how often do teens go out on walks before and after the corona virus outbreak?
Resource Link: https://www.cdc.gov/healthyschools/physicalactivity/facts.htm
From Mrs. Y - You should use a 2 sample t test for the diff of two mean  Group B

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Student names: Jaedyn Oliver (P.1) Valerie Hernandez (P.1) Adanna Ogbogu (P.1)

# data[].attributes.subject
Student names: Jaedyn Oliver (P.1) Valerie Hernandez (P.1) Adanna Ogbogu (P.1)

# data[].attributes.body
Topic: Snacks in a day
Question: Is there a major change in snack consumption of teens vs. Adults during quarantine?
Resource link: https://www.forbes.com/sites/carlieporterfield/2020/04/28/in-coronavirus-quarantine-were-eating-more-processed-snacks/#134b43b523c3
From Mrs Y - I love the article. What is your survey going to ask?     Group C       C

# data[].attributes.headline
Student Names: Kylie Houghton (P.3) Kayla Borden (P.3) 

# data[].attributes.subject
Student Names: Kylie Houghton (P.3) Kayla Borden (P.3) 

# data[].attributes.body
Topic: Sleep changes during quarantine
Question: How have sleep habits changed since quarantine?
Resource Link: https://www.insider.com/why-youre-sleeping-more-during-the-coronavirus-pandemic-2020-4
from Mrs. Y 4/30/4:40-  you need to do a paired t-test.  ( A before and after test)     Group D

# data[].attributes.headline
Student Names: Miracle Veney (P.2) Cameryn Brown (P.2) Ian Taylor (P.2) 

# data[].attributes.subject
Student Names: Miracle Veney (P.2) Cameryn Brown (P.2) Ian Taylor (P.2) 

# data[].attributes.body
Topic: Damage of Social Media interaction on Teens                                                  Question: How much social interaction is harmful to teens?
Resource Link: https://thehiringadvisors.com/smartphones-ruined-social-interaction-teens/
From Mrs. Y - I like this but your link is preCovid.  I do not think your collected data  (survey) will give you info about the preCovid temperament        GroupE

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Topic: Panic Buying Causing More Online Shopping
Question: How many times a week do you shop online rather than go to a store in person?
Resource Link: https://www.bigcommerce.com/blog/covid-19-ecommerce/#is-it-safe-to-order-online-during-covid-19
From Mrs. Y - 4/30 4:45I like it but how are you going to quantify this. Need more specifics       Group F

# data[].attributes.headline
Student names: Michaiah Stanford (P.2) Emma Muiruri (P.2) Hailey Hillstock (P.3)

# data[].attributes.subject
Student names: Michaiah Stanford (P.2) Emma Muiruri (P.2) Hailey Hillstock (P.3)

# data[].attributes.body
Topic: Teens spent time doing homework
Question: How much time on average does a teenager spend doing homework in high school?
Resource Link: https://www.hhs.gov/ash/oah/facts-and-stats/day-in-the-life/index.html
from Mrs. Yar 4/30 at 4:30   1 sample t test                   Group H

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Student Names: Halie Montes (P.2) Kayla Johnson (P.2)

# data[].attributes.subject
Student Names: Halie Montes (P.2) Kayla Johnson (P.2)

# data[].attributes.body
Topic: Teens' usage of phones during quarantine
Question: How much time do teens in America spend on their phone during quarantine?
Resource Link: https://www.nytimes.com/2020/02/06/learning/what-students-are-saying-about-how-much-they-use-their-phones-and-whether-we-should-be-worried.html
From Mrs. Y 4/30 4:41 Is this pre vs. post Covid?  Be more specific   Group I

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Student names: Tiffany Dane (P.1) Cassie Jimenez 

# data[].attributes.subject
Student names: Tiffany Dane (P.1) Cassie Jimenez 

# data[].attributes.body
Topic: Amount of homework given
Question: Is there a difference between the amount of homework given to students (in hours) from regular in person high school to online high school?
Resource link: https://www.brookings.edu/blog/brown-center-chalkboard/2017/08/10/analyzing-the-homework-gap-among-high-school-students/
from Mrs. Y 4/30 4:32- You will be doing a 2 sample t test for the difference of mu1 - mu 2    Group J

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Who does this belong to?

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Research

Researchers have struggled for decades to identify a causal, or even correlational, relationship between time spent in school and improved learning outcomes for students. Some studies have focused on the length of a school year while others have focused on hours in a day and others on hours in the week.

In this blog post, we will look at time spent outside of school-specifically time spent doing homework-among different racial and socio-economic groups. We will use data from the American Time Use Survey (ATUS) to shed light on those differences and then attempt to explain those gaps, using ATUS data and other evidence.

What we know about out-of-school time

Measuring the relationship between out-of-school time and outcomes like test scores can be difficult. Researchers are primarily confounded by an inability to determine what compels students to choose homework during their time off over other activities. Are those who spend more time on homework just extra motivated? Or are they struggling students who need to work harder to keep up? What role do social expectations from parents or peers play?

Previous studies have examined the impact of this outside time use on educational outcomes for students. A 2007 study using data from Berea College in Kentucky identified a causal relationship between hours spent studying and a student's academic performance through an interesting measure. The researchers took advantage of randomly assigned college roommates, paying attention to those who came to campus with a video game console in tow. They hypothesized students randomly assigned to a roommate without a video game console would study more, since all other factors remained equal. That hypothesis held up, and that group also received significantly higher grades, demonstrating the causal relationship.

Other research has relied on data collected through the American Time Use Survey, a study of how Americans spend their time, and shown the existence of a gender gap and a parental education gap in homework time. Other studies have looked at the relationship between holding a job and student's time use in discretionary activities, like sleep, media consumption, and time spent on homework. We are curious about out-of-school differences in homework time by race and income.

Descriptive statistics of time use

We began with a general sample of 2,575 full-time high school students between the ages of 15 and 18 from the ATUS, restricting the sample to their answers about time spent on homework during weekdays and school months (September to May). Among all high school students surveyed (those that reported completing their homework and those that did not), the time allocated to complete homework amounted to less than an hour per day, despite the fact that high school teachers report they assign an average of 3.5 hours of homework per day.

To explore racial or income-based differences, in Figure 1, we plot the minutes that students reporting spending on homework separately by their racial/ethnic group and family income. We observed a time gap between racial groups, with Asian students spending the most time on homework (nearly two hours a day). Similarly, we observe a time gap by the students' family income.

We can also use ATUS data to isolate when students do homework by race and by income. In Figure 2, we plot the percentage of high school students in each racial and income group doing homework by the time of day. Percentages remain low during the school day and then expectedly increase when students get home, with more Asian students doing more homework and working later into the night than other racial groups. Low-income students reported doing less homework per hour than their non-low-income peers.

Initial attempts to explain the homework gap

We hypothesized that these racial and income-based time gaps could potentially be explained by other factors, like work, time spent caring for others, and parental education. We tested these hypotheses by separating groups based on particular characteristics and comparing the average number of minutes per day spent on homework amongst the comparison groups.

Students who work predictably reported spending less time on educational activities, so if working disproportionately affected particular racial or income groups, then work could help explain the time gap. Students who worked allocated on average 20 minutes less for homework than their counterparts who did not work. Though low-income students worked more hours than their peers, they largely maintained a similar level of homework time by reducing their leisure or extracurricular activities. Therefore, the time gap on homework changed only slightly with the inclusion of work as a factor.

We also incorporated time spent taking care of others in the household. Though a greater percentage of low-income students take care of other household members, we found that this does not have a statistically significant effect on homework because students reduce leisure, rather than homework, in an attempt to help their families. Therefore, this variable again does not explain the time gaps.

Finally, we considered parental education, since parents with more education have been shown to encourage their children to value school more and have the resources to ensure homework is completed more easily. Our analysis showed students with at least one parent with any post-secondary degree (associate or above) reported spending more time on homework than their counterparts whose parents do not hold a degree; however, gaps by race still existed, even holding parental education constant. Turning to income levels, we found that parental education is more correlated with homework time among low-income students, reducing the time gap between income groups to only eight minutes.

Societal explanations

Our analysis of ATUS could not fully explain this gap in time spent on homework, especially among racial groups. Instead, we believe that viewing homework as an outcome of the culture of the school and the expectations of teachers, rather than an outcome of a student's effort, may provide some reasons for its persistence.

Many studies, including recent research, have shown that teachers perceive students of color as academically inferior to their white peers. A 2016 study by Seth Gershenson et al. showed that this expectations gap can also depend on the race of the teacher. In a country where minority students make up nearly half of all public school students, yet minority teachers comprise just 18 percent of the teacher workforce, these differences in expectations matter.

Students of color are also less likely to attend high schools that offer advanced courses (including Advanced Placement courses) that would likely assign more homework, and thus access to rigorous courses may partially explain the gaps as well.

Research shows a similar, if less well-documented, gap by income, with teachers reporting lower expectations and dimmer futures for their low-income students. Low-income students and students of color may be assigned less homework based on lower expectations for their success, thus preventing them from learning as much and creating a self-fulfilling prophecy.

In conclusion, these analyses of time use revealed a substantial gap in homework by race and by income group that could not be entirely explained by work, taking care of others, or parental education. Additionally, differences in educational achievement, especially as measured on standardized tests, have been well-documented by race and by income. These gaps deserve our attention, but we should be wary of blaming disadvantaged groups. Time use is an outcome reflecting multiple factors, not simply motivation, and a greater understanding of that should help raise expectations-and therefore, educational achievement-all around.
Sarah Novicoff contributed to this post.

The Brookings Institution is committed to quality, independence, and impact.
We are supported by a diverse array of funders. In line with our values and policies, each Brookings publication represents the sole views of its author(s).

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Analyzing 'the homework gap' among high school students | Brookings

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author-bio] Researchers have struggled for decades to identify a causal, or even correlational, relationship between time spent in school and improved learning outcomes for students. Some studies have focused on the length of a school year while others have focused on hours in a day and others on hours in the week.

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Student Names: Jonathan Luna (Per.3) Christian Wilson (Per.3)

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Student Names: Jonathan Luna (Per.3) Christian Wilson (Per.3)

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Topic: Video games and metal health during Covid-19

Question: Is there a correlation between video games and better mental health during the COVID-19 pandemic versus not playing video games?
From Mrs. Y -5/1 1:34pm  You need % .  Also we have not done correlation HT.  My suggestion is that you do a preCovid vs now situation and not attach the health aspect.  There is no quantifiable support               Group K
Resource Link:

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Don't Feel Bad if Your Kids Are Gaming More Than Ever

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My NBA2K sessions with my teenage son are a highlight of my day. Experts say games offer benefits for quarantined kids and adults alike.

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Internet Usage; Annabelle Erickson (P2), Micah Brennan (P3)

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Internet Usage; Annabelle Erickson (P2), Micah Brennan (P3)

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Topic: Internet usage
Question: How has internet usage been affected since the start of quara vbxcvdfcntine? Has internet been slower, and what is causing that affect?
https://www.nytimes.com/interactive/2020/04/07/technology/coronavirus-internet-use.html
From Mrs. Yarbrough ( AEMB) - Nice data - You will be doing a 1 prop z test Group L

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Topic Question: Is there an association between the academic performance of students and sports?
Recourse Link: https://www.fnu.edu/the-link-between-sports-and-academic-performance/
From Mrs Y 5/1 3:326 How are you going to measure this?  The resource like is about 10 college students 2) increased # of exercise and college GPA.  This will not work.  It needs to be adjusted     Group M

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Topic: Corona Virus and jobs
Question: Are jobs and household income being affected because of the stay at home rule?
Resource Link: https://www.nytimes.com/2019/01/16/business/economy/federal-workers-shutdown.html

From Mrs. Y - 5/1 3:32    1.  This article does not appear  because I do not have a NYTimes subscription . So resubmit it another way/fin another article.
2. How are you going to quantify this?  I guess I will be able  to tell you the type of test once I see the article   Group N

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COVID-19 vs. Employment.    Benjamin Baker (P.1) and Sasha Zamarripa (P.2)

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COVID-19 vs. Employment.    Benjamin Baker (P.1) and Sasha Zamarripa (P.2)

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Topic: Employment during covid-19
Question: How has the corona virus effected employment amongst citizens? How bad has unemployment been since the outbreak?
Resource link: https://www.vox.com/future-perfect/2020/4/1/21201700/coronavirus-covid-19-unemployment-rate
From Mrs. Y - 5/1 4:03 This will be a  2 prop z test   Group 0

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This story is part of a group of stories called

Finding the best ways to do good.

On March 24, 2020, the Federal Reserve Bank of St. Louis, to not much fanfare, predicted that unemployment in the United States was about to skyrocket to a point higher than that reached at the peak of the Great Depression: 32 percent.

That is an astonishing - even incomprehensible - figure. Just four weeks ago, before the coronavirus pandemic plunged us into a recession, unemployment was at 3.5 percent. During the depths of the Great Recession, the highest unemployment reached was 10 percent. Nearly a century ago, in the Great Depression, the worst figure was 24.9 percent - lower if you count people in work relief jobs as employed.

The so-called back-of-the-envelope calculation by the St. Louis Fed is simply staggering. If anywhere close to accurate, it suggests Americans have barely come to grips with the coming economic calamity.

To be clear, the 32.1 percent figure is almost certainly wrong when it comes to the precise number. Other economists argue it's much too high. Jason Faberman, a senior economist at the Chicago Fed, tells me it is "based on extreme assumptions" that everyone at risk of layoffs gets laid off. But there are strong reasons to believe Americans are in for unemployment levels higher than any seen in most of their lifetimes.

Reaching that number is not inevitable. We can do much to arrest it. But it is going to take serious and sustained work from policymakers, who still might not grasp just how big the economic catastrophe is.

How the St. Louis Fed arrived at its estimate

Bank economist Miguel Faria-e-Castro wrote a blog post laying out the logic behind the "back-of-the-envelope" estimate.

First, he cited research by his St. Louis Fed colleague Charles Gascon, who classified 808 occupations tracked by the Bureau of Labor Statistics by how likely they are to face layoffs due to social distancing rules. Gascon's estimate was that 66.8 million people in sales, production, food preparation, and other sectors were at high risk of layoffs.

Three other economists at the St. Louis Fed - Fernando Leibovici, Ana Maria Santacreu, and Matthew Famiglietti - relied on survey data asking Americans how close, physically, they work to other people to classify occupations. They estimated that 27.3 million workers are in occupations with "high contact intensity."

So Faria-e-Castro simply averaged these two estimates - 66.8 million people, and 27.3 million people - together to estimate the likely number of unemployed people during coronavirus lockdown. They average to 47.05 million.

Now, there were 5.76 million unemployed people in February 2020, before social distancing began en masse; and the US civilian labor force had 164.5 million people in it in February. Add up the unemployed numbers and divided by 164.5 million, and you get 32.1 percent.

This is a very, very rough estimate, to a level where calling it "back of the envelope" feels like an insult to envelope math. Faria-e-Castro writes that the real number could be as low as 10.5 percent (if you take the estimate of "high contact" occupations and exclude education and health care workers who are less likely to be laid off) and 40.6 percent (if you use the "high risk of layoff" number only). There's a whole universe of potential outcomes in that range.

But Faria-e-Castro's estimate is worth taking seriously, even if it winds up being much too high.

Reasons to think extremely high unemployment is plausible

Let's start with one very solid number: 3.283 million.

That's how many people filed new initial unemployment claims the week ending March 21. It was likely an undercount of the total number of layoffs occurring that week, given how backed up the phone and online systems for accessing unemployment insurance have been.

Due both to those deferred claims and to the increasing number of employers shutting down the following week, claims for the week ending March 28 may be higher. Yale economist Paul Goldsmith-Pinkham and University of Minnesota economist Aaron Sojourner estimate that there will be 5.6 million new claim s. Preliminary news reports suggest the number could be even higher, even double the 3.3 million number from the prior week.

Rutgers economist William Rodgers III (a former chief economist at the Department of Labor) and the Century Foundation's Andrew Stettner built a model using these kinds of unemployment claims numbers to predict the unemployment rate. Plugging in the most recent claims data, they estimate a true unemployment rate in March of 17 percent. In some states, they find the unemployed share is far greater than that: 44.9 percent in Louisiana, 44.6 percent in New Hampshire, 34.6 percent in Pennsylvania.

That 17 percent figure might be too high, Faberman of the Chicago Fed says, because it doesn't account for the fact that the "take-up rate" of unemployment insurance claims rises during downturns. That means there are fewer unemployed people not getting unemployment relief who you have to add to the unemployment rate estimate, meaning the unemployment number could be an overestimate.

But he concedes 17 to 20 percent might be a good estimate of where the job market is headed, if not in March, then in a couple of months. Even now additional businesses are closing down as stay-at-home orders spread to new cities and states, suggesting we could be in for several weeks of high claims numbers.

Other ways of estimating how many people will ultimately be laid off suggest a much higher upper bound.

UChicago economists Jonathan Dingel and Brent Neiman recently put out a paper titled simply, "How Many Jobs Can be Done at Home?" Their answer, from evaluating survey responses to questions about how often workers need to work outdoors or use heavy machinery/vehicles, is that about 34 percent of jobs can be done from home. If you add the 12 percent of jobs that are part of the health care industry to that total (which is an overestimate, since at least some health care jobs can be done from home), you get 46 percent.

That makes 54 percent of all jobs not performable from home, or not part of the health care sector and thus more resistant to layoffs due to massive demand. This is higher, Dingel and Neiman are quick to point out, than other estimates of work from home. Princeton's Alexandre Mas and Harvard's Amanda Pallais, for instance, reported in 2019 that only 26 percent of Americans regularly worked from home; most of them were not in formal work-from-home arrangements, and very few were working exclusively from home.

54 percent unemployment is likely an extreme overestimate. Not every person who can't work from home will be laid off. Some employers will keep them on payroll, perhaps using forgivable loans authorized as part of the congressional stimulus package. There are essential jobs other than health care that will keep people employed and not working from home during the pandemic, especially in grocery stores and takeout restaurants.

But 54 percent is nonetheless an upper bound. This shocks a lot of people I know, many of whom have jobs they can do from home. As a journalist I fall in that boat; I actually worked from home two to three days a week before the pandemic hit. My life hasn't changed that much. But I have a very, very unusual job. Most work in the US cannot be performed from home, and it is reasonable to be worried that the vast majority of employers in such cases will not keep people on their payrolls.

Reasons for caution

There are, of course, other factors that could make unemployment in the 30 to 50 percent range less likely. The first and most obvious is the availability of government stimulus measures. Here, $1,200 checks are likely to be less important than the business loan provisions of the stimulus package, which enable businesses to take out large loans to cover their payroll on the understanding that the government will forgive the vast majority of these loans when the crisis is over.

Another factor, which Miguel Faria-e-Castro of the St. Louis Fed notes, is that the size of the labor force is not constant. During the last recession many people stopped looking for work and were not counted as unemployed for the purpose of the unemployment numbers put out by the Bureau of Labor Statistics. We should expect some of that to happen again this time.

Ultimately we just won't have much clarity on the unemployment rate until we see numbers for April. The unemployment rate for March will include data both from early in the month, before mass layoffs began, and later in the month. It's not going to be a useful number. April, however, will be the cruelest month, capturing the bulk of the layoffs and furloughs undertaken due to coronavirus. The April numbers will not be available until the first week of May, and even those numbers will likely be significantly revised in subsequent months.

But you should not be shocked if the April number is in the double digits, at the very least. This is not inevitable, but it is quite plausible. These are truly unprecedented times. Expect unprecedented numbers.
Sign up for the Future Perfect newsletter Future Perfect is funded in part by individual contributions, grants, and sponsorships. Learn more . and we'll send you a roundup of ideas and solutions for tackling the world's biggest challenges - and how to get better at doing good.

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Coronavirus could lead to the highest unemployment levels since the Great Depression

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Finding the best ways to do good. On March 24, 2020, the Federal Reserve Bank of St. Louis, to not much fanfare, predicted that unemployment in the United States was about to skyrocket to a point higher than that reached at the peak of the Great Depression: 32 percent.

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Topic: The amount of people leaving home during Covid-19
Question: What is the difference between the amount of people that were going out before the virus and during it?
Resource Link: https://laist.com/2020/04/28/coronavirus-covid19-los-angeles-guide-rules-closures-tips-advice.php

From Mrs. Y - Nice but your question should be about shopping not just going out.  This article is about revenue    Group P

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High School Students' Sleep Schedules - Christian Miraflores Jr. and  Sanzid Shamim - Period 2

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High School Students' Sleep Schedules - Christian Miraflores Jr. and  Sanzid Shamim - Period 2

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Question: How is the current pandemic affecting students' sleep schedule during distance learning?
https://www.uchicagomedicine.org/forefront/coronavirus-disease-covid-19/advice-for-sleeping-well-during-the-covid-19-outbreak

From Mrs. Y - Your article gives no data so you  have have nothing to compare unless you are doing a paired t test  which would be pre covid vs post covid sleep
Group Q

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Julia Peters (P·1)
Topic : Mental health during pandemic
Is there an association between mental illness and the covid-19 pandemic?
https://abcnews.go.com/Health/anxiety-depression-spike-americans-coronavirus-pandemic-spreads/story?id=69749677

From Mrs. Y  - NICE!  I was hoping someone would do this?  You need to find national or state wide %.  Can not tell you what test you would use until then.  You need numbers  Group R

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Topic: Workout times before and after the quarantine
Question: Is there a significant difference between the national average of time that people spend working out before the quarantine with a sample of people who work out during quarantine?
Resource Link: https://us.myprotein.com/thezone/training/american-health-fitness-survey/

What are yout trying to do?  Compare peoples pre Covid vs. current amount of exercise  OR comparing to the national average?  2 sample t or p tes  Group S

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Topic: The housing market before and after the quarantine
Question: How are home sales and the housing market changing before and after this pandemic?
Resource Link: https://www.marketwatch.com/story/fannie-mae-home-sales-will-decline-by-nearly-15-in-2020-due-to-coronavirus-2020-04-15
From Mrs. Y - 5/1 3:46 This is a paired t test BUT you do not have any before and after raw data.  See if you can find housing sales $ for pre Covid vs now  OR you can find the % for before and then now to do a 1 prop z test  Group T

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Student names: Tylyn Davis(p.3) Alejandro Mosqueda

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Student names: Tylyn Davis(p.3) Alejandro Mosqueda

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Topic: The popularity of shoes
Question: Will the popularity of shoes affect the price on the resale market?
Resource link: https://www.nytimes.com/2019/06/26/technology/trading-sneakers-stockx.html
From Mrs. Y - This is a NY Times link and I do not have a subscriptio so find another source 2) How are you going to quantify this? %? retail $ ?  And then compare it to what??   Group U

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Presley B (1)  and Neema M (1)              Group  V

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Presley B (1)  and Neema M (1)              Group  V

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Topic: Parents and work after children
Question: Is there a difference between how men and women continue their careers after having or starting a family?
Link: https://www.sciencemag.org/careers/2019/02/after-baby-28-new-parents-leave-full-time-stem-work
From Mrs. Y - 5/1/3:39pm Nice! You will using a 2 prop z test BUT change your question to
"Is there a significant difference between the % of men and women switch fields, transition to part-time work, or leave the workforce entirely after having or starting a family? "    Group V

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Student names- Mandy Stacey and Timia Mckenzie Period 1-  Group W

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Student names- Mandy Stacey and Timia Mckenzie Period 1-  Group W

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Topic- differences between prescribed and unprescribed opioids, family history of addiction and no history
Question: Have you ever been prescribed opioids? Have you ever taken opioids? Do you have a family history of addiction? Do you have friends/ peers that take them?
Link: https://www.truvida.com/addiction-rehab-resources/youth-and-opioid-use-california/
Mrs. Y 5/1 3:59 It will be a 1 prop z test IF you are using this info "20 percent of teens has abused a prescription pain medication at some point" from your article Group W

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Related to quarantine                           Group X

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Related to quarantine                           Group X

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Student names — Caleb Wolfe(1), Gavin McEwan(2), Aidan Pfister(1)
Topic — Teens In quarantines
Question — how has the time a teenager spends on a screen changed due to the coronavirus outbreak!
Source— https://www.axios.com/kids-screen-time-coronavirus-562073f6-0638-47f2-8ea3-4f8781d6b31b.html
From Mrs. Y 5/1  3:49 This is a 1 sample t test BUT you do not have s so you need to find % of screen time comparison     Group X

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Student name: Alan Codling (P.1)        -  Group Y

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Student name: Alan Codling (P.1)        -  Group Y

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Topic: Temperature and Covid-19
Question: Does temperature affect Covid-19's ability to spread?
Resource link: https://www.washingtonpost.com/weather/2020/03/24/warm-humid-weather-coronavirus/
Mrs. Y 5/1 1:33 ( you are late)                           Group Y
5/1 3:39 Where is your resource link?
5/2 2:16 pm - Where is the quantitative data (% or mean) for you do to a HT?

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Student Names: Remus Alcala (P.3) Brianna Fernandez(P.1)  Group z

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Student Names: Remus Alcala (P.3) Brianna Fernandez(P.1)  Group z

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Topic: USA vs Mexico win percentage
Question: What is the difference in win percentage and can we say USA has a better one.
Resource link: https://www.11v11.com/teams/usa/tab/opposingTeams/opposition/Mexico/
From Mrs. Y  5/2 2:14pm - You will be conducting a 2 prop z test for p1 and p2  Group Z