Index Of Tally Erp 9

[Niobe Hennigan]

The California Hard-to-Count (CA-HTC) Index is based on multiple demographic, housing and socioeconomic variables correlated with an area being difficult to enumerate. Census tracts with higher CA-HTC indexes are likely to be places that will pose significant challenges to enumerate in 2020, while tracts with lower indexes should be easier to count.

Percent of households without broadband subscriptions (California Public Utilities Commission): More than 10 million California households will be asked to complete the census online. Some outreach efforts will be online, as well. A household without a broadband subscription is less likely to know about the census and more likely to fail to self-respond.

Percent of households that are non-family (Table B11001, U.S. Census Bureau 2013-2017 American Community Survey [ACS]). Nonfamily households generally involve multiple roommates. The household member completing the census form might forget to include some of these people.

Percent of occupied housing units that are renter-occupied (Table B25003, ACS). The percentage of renter households in a tract or block group is among the strongest hard-to-count indicators. Renters move more often and have a greater chance of being missed during the census-taking process.

Percent of total housing units that are vacant (Table B25002, ACS): Vacant housing units change status quickly. Housing units considered vacant by census takers in reality could be occupied April 1, 2020.

Percent crowded (the percent of occupied housing units with more than 1.5 persons per room. Table B25014, ACS): As with nonfamily households, occupants in crowded households are more likely to be left off census forms. Also, the person completing the form may omit occupants if the household exceeds landlord or government limits.

Percent of population that is foreign-born (Table B05001, ACS): People who are born in other countries are less likely to be familiar with the census. Some also are not citizens and may fear the consequences of revealing their presence and legal status to the government.

Percent of adults (25 or older) who are not high-school graduates (Table S1501, ACS): Non-high school graduates are less likely to be engaged in civic affairs and more likely to be working multiple low-wage jobs that leave little spare time for completing census forms.

Percent of population with income below 150 percent of poverty level (Table C17002, ACS): Multiple issues increase the odds of an undercount among the poor. They tend to be renters. Administrative records to supplement the census, such as tax returns, may be incomplete for this group. They also are less likely to have internet access.

Percent of households receiving public assistance income (Table B19057, ACS): People may be reluctant to share their true household size because the information may contradict government assistance records. They are likely living near or below the poverty line.

Percent of persons who moved from outside county in past year (Table B07003, ACS): Recent arrivals likely have little connection to local civic affairs. Proxy information and administrative records about this population will be more difficult to come by.

Percent of population under 5 (Table S0101, ACS): More children are living in complex family situations, such as shared parental custody or with a grandparent, increasing the chances they will be left off the census form. Some new parents mistakenly believe the census incorporates birth records.

which takes about 435ms. Is there any way to change my query to improve performance? I've tried grouping and doing a regular count, as well as putting an index on x; both have the same 7.5s execution time.

If your count(distinct(x)) is significantly slower than count(x) then you can speed up this query by maintaining x value counts in different table, for example table_name_x_counts (x integer not null, x_count int not null), using triggers. But your write performance will suffer and if you update multiple x values in single transaction then you'd need to do this in some explicit order to avoid possible deadlock.

*Note: These percentages reflect estimates of the LGBTQ adult population living in the 50 states and the District of Columbia. Estimates of the LGBTQ adult population in the five inhabited U.S. territories are not available, and so cannot be reflected here.

February 2020 - This report offers a fresh perspective on the current legal status of LGBTQ people and tallies nearly 40 LGBTQ-related laws and policies across all 50 states, the District of Columbia, and the five U.S. territories.

February 2017 - To help make sense of the current policy landscape in the states, this report looks at legal equality for transgender people across the country. The gender identity tally is comprised of 25 state laws and policies in five key categories: Non-Discrimination, LGBT Youth, Health and Safety, Ability to Correct the Name and Gender Marker on Identity Documents, and Adoption and Parenting.

May 2015 - Mapping LGBT Equality in America sets out to identify and explain the key gaps in legal equality for LGBT Americans by introducing the major state and local laws and policies that protect or harm LGBT people, providing a breakdown of those laws and policies by state, and showing how protections for LGBT Americans vary based on sexual orientation and gender identity and expression.

Policy Tallies provide an overview of laws and polices that exist in each state. The major categories of laws covered by the policy tally include: Marriage and Relationship Recognition, Adoption and Parenting, Non-Discrimination, Safe Schools, Health and Safety, and the Ability for Transgender People to Correct the Gender Marker on Identity Documents.

Founded in 2006, the Movement Advancement Project (MAP) is an independent, nonprofit think tank that provides rigorous research, insight and communications that help speed equality and opportunity for all.

MAP works to ensure that all people have a fair chance to pursue health and happiness, earn a living, take care of the ones they love, be safe in their communities, and participate in civic life. MAP is a nonprofit 501(c)(3) organization and donations to MAP are 100% tax-deductible. You can read more about MAP and the work we do on our About page.

is very slow for large tables. It seems like the database it actually going through every row and incrementing a counter one at a time. I would think that there would be a counter somewhere in the table how many rows that table has.

Think about it: the database really has to go to every row to do that. In a multi-user environment my COUNT(*) could be different from your COUNT(*). It would be impractical to have a different counter for each and every session so you have literally to count the rows. Most of the time anyway you would have a WHERE clause or a JOIN in your query so your hypothetical counter would be of litte practical value.

There are ways to speed up things however: if you have an INDEX on a NOT NULL column Oracle will count the rows of the index instead of the table. In a proper relational model all tables have a primary key so the COUNT(*) will use the index of the primary key.

I'll admit I wouldn't be happy with 41 seconds but really WHY do you think it should be faster? If you tell us the table has 18 billion rows and is running on the laptop you bought from a garage sale in 2001, 41 seconds is probably not that far outside "good as it will get" unless you get better hardware. However if you say you are on Oracle 9 and you ran statistics last summer well you'll probably get a different suggestions.

This is the first of what will be a regular analysis of bonds in the Morningstar US High-Yield Bond Index that are trading at least 1,000 basis points above the 10-year U.S. Treasury yield, a level commonly considered distressed.

The number of high-yield issuers and issues in the Morningstar US High-Yield Bond Index trading at distressed levels (defined as 1,000 basis points or more above Treasuries) on Aug. 17 was down markedly from the total at the end of the second quarter, clearly reflecting the high-yield market rally that began July 1.

Specifically, 121 bonds belonging to 85 issuers in the Morningstar index were trading at distressed yields, versus 203 bonds sold by 139 issuers on June 30. The 39% decline in issuers and 40% drop in issues come as the option-adjusted spread of the index tightened to 418 basis points off the curve, from 570 bps at the end of the June quarter.

The Aug. 17 average price of the distressed subset is still higher than the 61.27 average price on March 31. The weighted average yield to worst of the distressed subset on Aug. 17 was 23.96%, 521 bps below the March 31 level.

The healthcare sector dominates the distressed subset of the Morningstar US High-Yield Bond Index, accounting for 22% of the distressed bonds as of Aug. 17. Most of those bonds were issued by pharma companies. Energy issues were second, at 12.4%. Retail and basic industry issues were tied for third place, both at 11.6%.

This method constructs a new tally to encapsulate the average of thedata represented by the average of the data in this tally. The tallydata average is determined by the scores, filter bins and nuclidesspecified in the input parameters.

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