Proportions Problems Worksheet - Practical Questions

Extents Problems Worksheet - Practical Questions An extent is a lot of 2 portions that equivalent one another. This article centers around how to utilize extents to tackle genuine issues. Certifiable Uses of Proportions Altering a financial plan for an eatery network that is growing from 3 areas to 20 locationsCreating a high rise from blueprintsCalculating tips, commissions, and deals charge Altering a Recipe On Monday, you are cooking enough white rice to serve precisely 3 individuals. The formula calls for 2 cups of water and 1 cup of dry rice. On Sunday, you are going to serve rice to 12 individuals. How might the formula change? In the event that you’ve ever constructed rice, you realize that this proportion - 1 section dry rice and 2 sections waterâ -is significant. Wreckage it up, and you’ll be scooping a sticky, chaotic situation on your visitors crayfish à ©touffã ©e. Since you are quadrupling your list of attendees (3 individuals * 4 12 individuals), you should fourfold your formula. Cook 8 cups of water and 4 cups of dry rice. These movements in a formula exhibit the core of extents: utilize a proportion to suit lifes more prominent and littler changes. Variable based math and Proportions 1 Indeed, with the correct numbers, you can renounce setting up a mathematical condition to decide the measures of dry rice and water. What happens when the numbers are not all that agreeable? On Thanksgiving, youll be serving rice to 25 individuals. How much waterâ do you need?Because the proportion of 2 sections water and 1 section dry rice applies to cooking 25 servings of rice, utilize an extent to decide the amount of fixings. Note: Translating a word issue into a condition is overly significant. Indeed, you can comprehend an inaccurately set up condition and discover an answer. You can likewise combine rice and water to make food to serve at Thanksgiving. Regardless of whether the appropriate response or food is attractive relies upon the condition. Consider what you know: 3 servings of cooked rice 2 cups of water; 1 cup of dry rice25 servings of cooked rice ? cups of water; ? cup of dry rice3 servings of cooked rice/25 servings of cooked rice 2 cups of water/x cups of water3/25 2/x Cross increase. Clue: Write these parts vertically to get the full comprehension of cross increasing. To cross increase, take the primary divisions numerator and duplicate it continuously parts denominator. At that point take the second portions numerator and increase it by the primary divisions denominator.3 * x 2 * 253x 50Divide the two sides of the condition by 3 to unravel for x.3x/3 50/3x 16.6667 cups of waterFreeze-confirm that the appropriate response is correct.Is 3/25 2/16.6667?3/25 .122/16.6667 .12Whoo hoo! The main extent is right.â Variable based math and Proportions 2 Recall that x won't generally be in the numerator. In some cases the variable is in the denominator, however the procedure is the equivalent. Comprehend the accompanying for x. 36/x 108/12 Cross multiply:36 * 12 108 * x432 108xDivide the two sides by 108 to comprehend for x.432/108 108x/1084 xCheck and ensure the appropriate response is correct. Keep in mind, an extent is characterized as 2 proportional fractions:Does 36/4 108/12?36/4 9108/12 9It’s right! Practice Exercises Directions: For each activity, set up an extent and unravel. Check each answer.1. Damian is making brownies to serve at the family outing. In the event that the formula calls for 2  ½ cups of cocoa to serve 4 individuals, what number of cups will he need if there will be 60 individuals at the outing? 2. A piglet can increase 3 pounds in 36 hours. In the event that this rate proceeds, the pig will arrive at 18 pounds in _________ hours. 3. Denise’s hare can eat 70 pounds of food in 80 days. To what extent will it take the bunny to eat 87.5 pounds? 4. Jessica travels 130 miles at regular intervals. In the event that this rate proceeds, to what extent will it take her to travel 1,000 miles?

Comprehensive Geriatric Assessment Essay

The geriatric appraisal is a multidimensional, multidisciplinary symptomatic instrument intended to gather information on the clinical, psychosocial and practical capacities and restrictions of old patients. Different geriatric experts utilize the data produced to create treatment and long haul follow-up plans, orchestrate essential consideration and rehabilitative administrations, compose and encourage the many-sided procedure of case the board, decide long haul care prerequisites and ideal arrangement, and utilize human services assets. The geriatric appraisal contrasts from a standard clinical assessment in three general manners: (1) it centers around old people with complex issues, (2) it accentuates practical status and personal satisfaction, and (3) it as often as possible exploits an interdisciplinary group of suppliers. While the standard clinical assessment works sensibly well in most different populaces, it will in general miss the absolute most predominant issues looked by the senior patient. These difficulties, frequently alluded to as the â€Å"Five I’s of Geriatrics†, incorporate scholarly impedance, fixed status, unsteadiness, incontinence and iatrogenic issue. The geriatric evaluation viably addresses these and numerous different zones of geriatric consideration that are critical to the effective treatment and anticipation of ailment and incapacity in more seasoned individuals. Playing out a far reaching evaluation is a yearning undertaking. The following is a rundown of the territories g eriatric suppliers may decide to evaluate: †¢ Current side effects and ailments and their utilitarian effect. †¢ Current meds, their signs and impacts. †¢ Relevant past ailments. †¢ Recent and approaching life changes. †¢ Objective proportion of by and large close to home and social usefulness. †¢ Current and future living condition and its suitability to capacity and visualization. †¢ Family circumstance and accessibility. †¢ Current parental figure organize including its insufficiencies and potential. †¢ Objective proportion of intellectual status. †¢ Objective evaluation of versatility and equalization. †¢ Rehabilitative status and anticipation assuming sick or debilitated. †¢ Current enthusiastic wellbeing and substance misuse. †¢ Nutritional status and necessities. †¢ Disease chance variables, screening status, and wellbeing advancement exercises. †¢ Services required and got. The essential consideration doctor or network wellbeing laborer for the most part starts an evaluation when the person distinguishes a potential issue. Like any successful clinical assessment, the geriatric appraisal should be adequately adaptable in scope and versatile in substance to serve a wide scope of patients. A total geriatric evaluation, performed by numerous staff over numerous experiences, is most appropriate for older folks with different clinical issues and huge utilitarian impediments. In a perfect world, under these conditions, an interdisciplinary group †speaking to medication, psychiatry, social work, nourishment, physical and word related treatment and others †plays out a point by point evaluation, examines the data, devises an intercession procedure, starts treatment, and follows-up on the patient’s progress. Because of the multifaceted idea of complete evaluations, numerous groups assign a caseworker or case manager to facilitate the whole exertion. Most appraisals happen in clinical workplaces and inpatient units over numerous visits. Assuming there is any chance of this happening, be that as it may, in any event one individual from the group (once in a while the doctor) will endeavor to visit the patient at home. Regardless of the issue of low or no repayment, the normally high return of data from even a solitary home visit makes it a very effective utilization of assets. Most geriatric evaluations, performed under the imperatives of time and cash, will in general be not so much thorough but rather more coordinated. Albeit such adjustments are most appropriate to moderately advanced seniors living in the network, numerous specialists discover some rendition of a guided geriatric appraisal to be a progressively reasonable instrument in a bustling practice. Persistent driven evaluation instruments are likewise well known among geriatricians. Requesting that patients complete surveys and perform explicit errands notâ only spares time, yet in addition it gives helpful understanding into their inspiration and psychological capacity. To the degree that patients can't finish the evaluation themselves, specialists resort to conventional patient meeting procedures that as often as possible include contribution from a relative or other guardian. During your up and coming site visits, you will play out a coordinated geriatric appraisal (DGA), in a perfect world with a similar patient, more than two meetings. In light of a legitimate concern for training, the vast majority of your DGA instruments are understudy driven, as opposed to understanding driven, and require generally little data from parental figures who could conceivably be accessible at the hour of your visit. We have separated the DGA in two sections, each with three subsections. In Part I, you will play out an extended clinical meeting covering the clinical history, healthful appraisal and a social assessment. In Part II, you will perform neuropsychiatric, physical and utilitarian assessments. What follows is a multiplication of the History and Physical (H&P) design that you will use in your Physical Diagnosis II course next semester. Albeit every geriatric professional don't utilize a standard evaluation group (thorough or something else), most concede to fundamental substance. The far reaching geriatric evaluation (history and assessment) following the Physical Diagnosis plot covers the most critical substance regions of a prototypical geriatric appraisal. As should be obvious, it moves well past the standard H&P, which is correctly the point. We have structured it to relate as intently as conceivable with the history and physical you will learn in the not so distant future. It is furthering your impressive potential benefit to audit this data before meeting your patients up close and personal on the site visits. The DGA instrument you will use during your experience quickly follows this segment.

An MIT Underwear Exposé (and Sorting Hat)

An MIT Underwear Exposé (and Sorting Hat) A lot of socializing at MIT happens on the dorm mailing lists. One of my favorite mailing lists is Burton-Conner’s, not because of the content of the mailing list (I’ve never been on it), but because of the excellent barrier to emailing it: it is tradition, a very important rule, and a sign of respect to sign emails to the Burton-Conner dormwide social mailing list with the color of the underwear you are wearing.  (For a more detailed explanation, see Snively ‘11’s post from 2009.) This rule is a huge boon to those of us who are data-curious and kind of creepy. All MIT undergraduates, even those who have never lived in Burton-Conner, have a wealth of data on the self-reported underwear colors of people who have emailed the entire undergraduate population, which includes Burton-Conner. Reasons for emailing all undergraduates include event announcements for student groups and departments, flame wars, and occasionally lost items. In contrast, the kinds of emails sent within a dorm mailing list include, at the top of my inbox right now, parties, house meetings, and foodmobs to restaurants in Boston; decisions about when to turn off the heating for spring, invitations to test food experiments, and a memo to the person who left their clothes in the middle washer; and requests for empty gallon jugs, superglue, cooking scales, male-to-male audio cables, MIDI cables, 120V twist lock connectors, funnels, and hairdryers. At the end of one IAP, from BMF and Destiny kitchens, my room, Cory’s room, and Random Hall desk, I downloaded and parsed all the emails that had been sent to my MIT email address. I extracted the underwear colors from the emails and I retrieved data (this part by hand, not with a script) on the people who had sent them from the MIT people directory. 417 days later I had a very bad headache, so I made pie charts from the parsed data and traced and colored them in BMF kitchen. The data are squirmy, like most data: We can’t know what proportion of the self-reported underwear colors is real and what proportion is made up. My parsing might be imperfect, especially if anyone made a typo. Class years are wrong for anyone who took a gap year between when they sent an email and when I retrieved their data, or for people who were superseniors when I retrieved their data, since superseniors are grouped with seniors in the MIT people directory. Similarly, I can’t know if a person switched living groups or departments after sending their email; they are grouped with the living group and department they belonged to when I retrieved their data, not the living group and department they belonged to when they sent their email. Finally, to protect the privacy of the people wearing the underwear, I am not going to tell you what years these are from. Figure 1a: Underwear Color by Undergraduate Dorm I dearly enjoy these pie charts. They are not a perfect picture but they are a kind of picture of our very varied homes. I think they capture a bit of the self-presentation of the dorm cultures, from one particular perspective and in one particular slice of time.             A few other living groups also contributed their data, though you are officially not allowed to live in these places until your second year at MIT. Figure 1b: Underwear Color by Non-Dorm Living Group       I just checked out Harry Potter and the Sorcerer’s Stone to reread it, after reading a particularly engaging/addicting/help fanfic with Harry Potter as a squib. There are lots of parallels between MIT and Hogwarts. Both are magical and occasionally terrifying. Both have weird rooms and passageways to explore and discover, staircases that lead to different places depending on when you take them, and unique and varied houses with beloved authority figures. There are also cupboards, some of them under stairs, where people have been rumored to live. But we don’t have a sorting hat, so I made a sorting hat, using the most comprehensive, unbiased data available to me (which is unfortunately neither comprehensive nor unbiased). We are going to use Bayes’ Theorem, which I think, based on my 5.59 years of experience, is the very favorite theorem of the computer science part of the course 6 (electrical engineering and computer science) department and possibly also course 7 (biology). Bayes’ Theorem allows us to calculate what we don’t know from what we do. Formally, for an event or truth A and an event or truth B, Bayes’ Theorem is as follows: Pr(A|B)  =   Pr(B|A) Pr(A) Pr(B) In other words, the probability of A given that B has happened or is true is equal to the probability of B given A, multiplied by the overall probability of A and divided by the overall probability of B. In our case, armed with the information we collected from my inbox, we can use Bayes’ Theorem to calculate the probability of you living in a dorm given your self-reported underwear color. Pr(dorm|underwear color)  =   Pr(underwear color|dorm) Pr(dorm) Pr(underwear color) If you were wearing purple underwear, for example, we could calculate the probability of you living in Simmons. Pr(Simmons|purple)  =   Pr(purple|Simmons) Pr(Simmons) Pr(purple) Simmons accounts for 10.49% of the undergraduate population living in dorms, and of emails coming from Simmons residents and signed with an underwear color, 11.11% were purple. Finally, we can calculate the denominator, Pr(purple), by adding up the probability of wearing purple underwear in each dorm where people wear purple underwear multiplied by the probability of living in that dorm in the first place. (In other words, the denominator is the sum of all possible numerators.)             Pr(purple) = Pr(purple|Next) Pr(Next) + Pr(purple|East Campus) Pr(East Campus) + Pr(purple|McCormick) Pr(McCormick) + Pr(purple|New House) Pr(New House) + Pr(purple|Simmons) Pr(Simmons) + Pr(purple|Random) Pr(Random) + Pr(purple|MacGregor) Pr(MacGregor) = (10.26%)(10.59%) + (3.23%)(10.89%) + (7.58%)(7.11%) + (6.67%)(8.76%) + (11.11%)(10.49%) + (5.41%)(2.84%) + (18.18%)(9.70%) = 5.64% We can therefore say, if you are wearing purple underwear, that the probability of you living in Simmons is 20.67%. Pr(Simmons|purple)  =   Pr(purple|Simmons) Pr(Simmons)  =   (11.11%)(10.49%)  =   20.67% Pr(purple) (5.64%) We can similarly calculate Pr(Baker|purple), Pr(Maseeh|purple), and so on. (See the supplemental tables at the end of this blog post if you would like to perform these calculations by hand with your own underwear.) From this, we can code up a sorting hat. It won’t be an exact sorting hatâ€"the fact that you are wearing purple underwear only gives you a probability distribution, not a guarantee. But there’s an element of chance to everything, right? So here we are: a probabilistic underwear sorting hat. I may not be practical, But don’t judge on what you see. I’ll eat your clothes if you can find A smarter hat than me. You can dye your boxer briefs, Your bras and panties all; I’m the Probabilistic Underwear Sorting Hat And I can sort them all. There’s no color underpants The Sorting Hat can’t see, So try me on and I will tell you Where they ought to be. My underwear is .  Sort me. We can also frame these data a few other ways. Figure 2: Underwear Color by Guesstimated Binary Gender Figure 3: Underwear Color by Day of the Week The radial axis is the percent of underwear that was each color on that day of the week. The right side is a zoomed in version of the left side, to facilitate viewing the rarer, more fun colors.  Roll over the image if you’re on a computer or click if you’re on a tablet to see each color on its own. We can see that blue is a staple throughout the week. Wednesday is not the day for black, but is a peak day for pink. Saturday’s the day people break out the yellow and don’t wear stripes. Sunday is a great day for silly prints. Figure 4: Underwear Color by Class Year As before, roll over the image (on a computer) or click (if you’re on a tablet) to see each color on its own. There is a sad, persistent decrease in multicolored underwear, ending with none by senior year. There is also a persistent decrease in purple and green and a sharp drop in pink underwear after sophomore year. Meanwhile, blue and grey underwear increase throughout a student’s academic career. Red and white move around but end up where they started: they seem to be occasional staples students come in and leave with. Black sees a sharp increase after freshman year and doesn’t grow after that. Figure 5: Underwear Color by Department Different majors are also nice to look at. I like that course 7 (biology) and course 20 (biological engineering) both have animal prints. I also like that course 16 (aerospace engineering) is colored kind of like airplanes, at least in my mind. Course 6 (electrical engineering and computer science) is the largest department, and has a wide variety of underwear colors.                   My field, computational biology, runs largely on perl, so writing a pattern matching script to parse the contents of my inbox once I had downloaded them was something I was well trained to do. It was surprisingly difficult, however, to collect my data from Google, where all my email addresses supersecretly lead. I did it a while ago, back when you could look up MIT students’ addresses in addition to their years and departments. Things might have changed (I hope they have changed). Here is how I went about obtaining my data: I deleted spam and emptied the trash. In Gmail, there is a gear button in top right corner. Click: Settings, then Accounts, then Other Google account settings, then Data tools, then Download data: Select data to download. Create an archive. Select Mail under Home and Office (if you want, select the labels you want to download) and press the red CREATE ARCHIVE button. Wait (hours or possibly days) for an email.  In my case, the collection happened from 11:15 am to 2:22 am, or 15 hours and 7 minutes to collect 6.78 GB of emails. I tried again with the contents of my MIT label only. This took from 1:30 am to 9:52 am, or 8 hours and 22 minutes to collect 5.77 GB. I followed the link sent to my email. If the file were small, I could have just downloaded it. Unfortunately the file was not smallâ€"decidedly not small. This for some reason necessitated Internet Explorer: other browsers wouldn’t let me download such a large file, and I couldn’t figure out if or how I could curl it. The most frustrating browser was Google Chrome, which pretended to successfully download the file until the very very end, when it gave me a network error and gave up. Safari was kind of fun: it showed the download date as 3 am Jan 24, 1984.The only browser fit for the task, Internet Explorer, is unfortunately (?) no longer available for OS X. I ended up downloading the contents of my inbox onto a computer with Internet Explorer (which may or may not have been a pretty slow computer that belongs to Housing and lives at Random Hall desk, where I might have had very long and, since everyone was asleep, very private Sunday morning desk shifts (I admit nothing)), and th en using an external harddrive to transfer the file to my personal laptop. Stripped of non-text attachments, the 5.77 GB of my inbox was only 137.5 MB. A decent chunk of those 137.5 MB is the Wikipedia article about Vlad the Impaler, which appears in its entirety ten times. The phrase “Vlad the Impaler” appears 535 times. The Bible, from the Book of Genesis through the Book of Revelation, appears twice, and The Communist Manifesto by Karl Marx and Friedrich Engels appears 17 times. Without Vlad et al., the emails I cared about, which were those that contained underwear colors, were only 907 KB. This was, by the way, a number of years ago. I’ve hit my 15 GB Gmail limit a few times since then (I’m currently back to 95%). Supplemental Table 1: The Proportion of Dorm-Living Undergraduates Living in Each Dorm (at Capacity) Baker 317 students 9.67% McCormick 233  students 7.11% Bexley (RIP) 116  students 0% New House 287  students 8.76% Burton Conner 346  students 10.56% Next House 347  students 10.59% East Campus 357 students 10.89% Random 93  students 2.84% MacGregor 318  students 9.70% Senior Haus 146  students 4.45% Maseeh 490  students 14.95% Simmons 344  students 10.49% Supplemental Table 2: The Proportion of Emails with Each Underwear Color by Dorm Next House   Burton Conner East Campus McCormick New House black 21.37% 20.93% 6.45% 6.06% 31.67 white 7.69% 4.65% 8.06% 3.03% 10.00% blue 15.38% 37.21% 22.58% 27.27% 20.00% pink 19.66% 4.65% 4.84% 15.15% 15.00% grey 12.82% 2.33% 30.65% 7.58% 3.33% purple 10.26% 3.23% 7.58% 6.67% red 0.85% 11.63% 4.84% 13.64% green 5.13% 2.33% 8.06% 6.06% 3.33% blue-green 0.85% 4.65% 3.03% 1.67% yellow 1.71% 1.61% 4.55% 1.67% orange 1.67% brown 0.85% striped 0.85% 4.65% 1.61% 1.52% polka dotted 2.33% 1.61% animals 0.85% 1.52% 3.33% multicolored 0.85% 4.65% 4.84% 3.03% commando 0.85% 1.61% 1.67% total 100% 100% 100% 100% 100% Baker       MacGregor Maseeh     Senior Haus Simmons   Random black 39.29% 54.55% 33.33% 13.51% white 10.71% 18.18% 84.62% 11.11% 8.11% blue 17.86% 45.45% 3.85% 9.09% 11.11% 27.03% pink 21.43% 18.18% 7.69% 4.55% 11.11% 2.70% grey 3.57% 3.85% 13.51% purple 18.18% 11.11% 5.41% red 9.09% 8.11% green 3.57% 4.55% 5.41% blue-green 8.11% yellow 3.57% 2.70% orange 11.11% striped 5.41% multicolored 4.55% 11.11% commando 13.64% total 100% 100% 100% 100% 100% 100%