The Way To Equal Distribution Questions And Answers
Sandra Grady
i am working on an application in which a lot of newspaper clippings should be "thrown" randomly on y table.however if using true random there is always the possibility that all clipings appear on one place. the client would prefer a more "equal" random distribution.
What you are looking for is known as a quasi-random sequence (or a low-discrepancy sequence). There are several well-known sequences like that, here's the Wikipedia entry. Depending on your language of choice, ready-to-use libraries might be available (a couple of examples are mentioned in this question: Recommendations for Low Discrepancy (e.g. Sobol) quasi-random sequences in Python/SciPy?).
I would divide the table in 4 parts (equal in surface) and and one more (overlapping) part that represents the centre of the table. you can always play with the number of 4 and make it 6 or 8 but i would not go as high as 20.
Perhaps the easiest way of solving this problem is to flatten it to a linear object. A grid of 4 x 5 can become a list of 20. Just give each 'slot' a number (0 - 19) and use the following algorithm. Hope you don't mind Java.
Need your help resolving algorithm task -There are 3 baskets, basket 1 has 10 balls and a possible max capacity of 100, basket 2 has 50 balls and a possible max capacity of 200, and basket 3 has 100 balls and a possible max capacity of 300.Please help me to write an algorithm or code that split another 100 balls between the 3 baskets for the best possible equal distribution between the baskets.Not possible to move balls between the baskets.
Your suggested algorithm should of course work on any number of baskets with different max capacities and any onHand value, for example, 1 ball that I want to add or the maximum capacity value that should fill all baskets to 100% fill.
I want to find out what range I should set to say have 3 groups with a equal count in each group (in this case 7 records in each group). Is there a certain tool that would be able to auto-calculate that?
Thanks for your response, I think the Equal Records clusters it randomly. So in my instance, I want an algorithmic way to group the "Items Sold" in 3 buckets determined by the distribution of the "Items Sold". If say there are 7 records for 1,2, and 3 # of "Items Sold" then I would construct a bucket of "1-3 Items Sold". Then the next bucket could "4-5 Items Sold" and last "6-10 Items" Sold.
anyone knows how do I set up randomisation of treatments based on answers to a scale question? I thought I would first do embedded data where scores are weighted based on normal and equal distribution of participants to 5 treatment scenarios with the condition that each scenario will have participants with a varied levels of trust (scale with 4 likely scale statements). anyone knows how to set this up in Qualtrics?
Health equity generally refers to individuals achieving their highest level of health through the elimination of disparities in health and health care. Healthy People 2030 defines health equity as the attainment of the highest level of health for all people and notes that it requires valuing everyone equally with focused and ongoing societal efforts to address avoidable inequalities, historical and contemporary injustices, and health and health care disparities. The CDC describes health equity as when everyone has the opportunity to be as healthy as possible.
It is increasingly important to address health disparities as the population becomes more diverse and income inequality continues to grow. It is projected that people of color will account for over half (52%) of the population in 2050, with the largest growth occurring among people who identify as Asian or Hispanic (Figure 2). Over time, the population has become increasingly racially diverse, reflecting shifting immigration patterns, a growing multiracial population, as well as adjustments to how the federal Census Bureau measures race and ethnicity. Over time, income inequality within the U.S. has also widened. As of 2021, the richest 20% of households accounted for over half of the aggregate household income and had an income of $149,132 or higher compared to the bottom 20% of households who accounted for less than 3% of the aggregate household income and had incomes of $28,007 or less. The top 5 percent of households in the income distribution had incomes of $286,305 or more. Research suggests that the disparate negative effects of the COVID-19 pandemic on lower-paid occupations may have lingering effects that contribute to further widening income inequality over the long term.
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Despite large gains in coverage since implementation of the Patient Protection and Affordable Care Act (ACA) in 2014, people of color and other marginalized and underserved groups remain more likely to be uninsured. Racial disparities in coverage persisted as of 2021, with higher uninsured rates for nonelderly American Indian or Alaska Native (AIAN), Hispanic, Black, and Native Hawaiian or Pacific Islander (NHOPI) people compared to their White counterparts (Figure 3). Other groups also remained at increased risk of being uninsured, including immigrants and people in lower-income families. Many people who are uninsured are eligible for coverage through Medicaid, CHIP, or the ACA Marketplaces but face barriers to enrollment including confusion about eligibility policies, difficulty navigating enrollment processes, and language and literacy problems. Some immigrant families also have immigration-related fears about enrolling themselves or their children in Medicaid or CHIP even if they are eligible. Others remain ineligible because their state did not expand Medicaid, due to their immigration status, or because they have access to an affordable Marketplace plan or offer of employer coverage.
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Beyond coverage, people of color and other marginalized and underserved groups continue to experience many disparities in accessing and receiving care. For example, people in rural areas face barriers to accessing care due to low density of providers and longer travel times to care, as well as more limited access to health coverage. There also are inequities in experiences receiving health care across groups. For example, the KFF/The Undefeated 2020 Survey on Race and Health, found that one in five Black adults and one in five Hispanic adults report being treated unfairly treatment due to their race or ethnicity while getting health care for themselves or a family member in the past year. Nearly one-quarter (24%) of Hispanic adults and over one in three (34%) potentially undocumented Hispanic adults reported that it was very or somewhat difficult to find a doctor who explains this in a way that is easy to understand in a 2021 KFF survey. Other KFF survey data from 2022 found that nearly one in ten (9%) of nonelderly adult women who visited a health care provider in the past two years said they experienced discrimination because of their age, gender, race, sexual orientation, religion, or some other personal characteristic during a health care visit. KFF data also showed that LGBT+ people were more likely than their non-LGBT+ counterparts to report certain negative experiences while getting health care, including a doctor not believing they were telling the truth, suggesting they are personally to blame for a health problem, assuming something about them without asking, and/or dismissing their concerns. The 2023 KFF/The Washington Post Trans Survey found that trans adults were more likely to report having difficulty finding affordable health care or a provider who treated them with dignity and respect compared to cisgender adults.
People of color and other underserved groups face ongoing disparities in health. For example, at birth, AIAN and Black people had shorter life expectancies compared to White people as of 2021, and AIAN, Hispanic, and Black people experienced larger declines in life expectancy than White people between 2019 and 2021, reflecting the impacts of COVID-19 (Figure 4). Black infants were more than two times as likely to die as White infants and AIAN infants were nearly twice as likely to die as White infants as of 2021. Black and AIAN women also had the highest rates of pregnancy-related mortality across groups. Rates of chronic disease and cancer also vary by race and ethnicity. Although Black people did not have higher cancer incidence rates than White people overall and across most types of cancer, they were more likely to die from cancer in 2019. There are also stark disparities in health by income. Research shows that people living in areas with high concentrations of poverty are at increased risk of poorer health outcomes over the course of their lives. KFF analysis also found that LGBT+ people were more likely to report being in fair or poor health and having an ongoing health condition that requires ongoing monitoring, medical care, or medication compared to non-LGBT+ people despite being a younger population.
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The COVID-19 pandemic has taken a disproportionate toll on the health and well-being of people of color and other underserved groups. Cumulative age-adjusted data showed that AIAN and Hispanic people have had a higher risk for COVID-19 infection and AIAN, Hispanic, and Black people have had a higher risk for hospitalization and death due to COVID-19. Beyond these direct health impacts, the pandemic has negatively impacted the mental health, well-being, and social and economic factors that drive health for people of color and other underserved groups, including LGBT+ people. As such, the pandemic may contribute to worsening health disparities going forward.
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