Dataset bwght.data free download

FiveThirtyEight makes the data sets used in its articles available online on Github. Socrata OpenData is a portal that contains multiple clean data sets that can be explored in the browser or downloaded to visualize. A significant portion of the data is from US government sources, and many are outdated.

You can explore and download data from OpenData without registering. You can also use visualization and exploration tools to explore the data in the browser. Sometimes you just want to work with a large data set. You might use tools like Spark or Hadoop to distribute the processing across multiple nodes.

Things to keep in mind when looking for a good data processing data set:. A good place to find large public data sets are cloud hosting providers like Amazon and Google. They have an incentive to host the data sets, because they make you analyze them using their infrastructure and pay them. Amazon makes large data sets available on its Amazon Web Services platform.

You can download the data and work with it on your own computer, or analyze the data in the cloud using EC2 and Hadoop via EMR. You can read more about how the program works here. Amazon has a page that lists all of the data sets for you to browse. Google lists all of the data sets on a page.

Wikipedia is a free, online, community-edited encyclopedia. Wikipedia contains an astonishing breadth of knowledge, containing pages on everything from the Ottoman-Habsburg Wars to Leonard Nimoy. Additionally, Wikipedia offers edit history and activity, so you can track how a page on a topic evolves over time, and who contributes to it. You can find the various ways to download the data on the Wikipedia site.

In order to be able to do this, we need to make sure that:. There are a few online repositories of data sets that are specifically for machine learning. These data sets are typically cleaned up beforehand, and allow for testing of algorithms very quickly. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact:.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form. Bookstore Stata Journal Stata News. Contact us Hours of operation. Advanced search. Checkout Continue shopping. Stata: Data Analysis and Statistical Software. Go Stata. Purchase Products Training Support Company. Stata New in Stata Why Stata? Order Stata. Company Contact us Customer service Announcements Search. Cross-sectional data on eligibility for and participation in k plans along with income and demographic information.

Data on quantity of ecologically friendly apples desired by a survey of individuals. I answered this in the previous part — the fact that the coefficient on log expend got larger when we excluded lnchprg indicates, of course, that the inclusion of lnchprg suppressed the coefficient on log expend. What would lead this to happen? To see, combine two facts: 1 when lnchprg increases, math10 decreases; 2 when lexpend increases, lnchprg decreases. Therefore, when lexpend increases, what happens, in total?

When lexpend increases, lnchprg decreases, which causes math10 to go. These are ZIP code-level data on prices for various items at fast-food restaurants, along with characteristics of the zip code population, in New Jersey and Pennsylvania. The idea is to see whether fast-food restaurants charge higher prices in areas with a larger concentration of blacks. What are the units of measurement of prpblck and income?

Once again, this can be done with the sum function in Stata. Result: variable mean std. Do not use scientific notation when reporting the estimates. Interpret the coefficient on prpblck.

Do you think it is economically large? The coefficient on prpblck is 0. The literal interpretation would be: when prpblck increases by 1, the price of a medium soda increases by 11 cents. The only problem is, the notion of increasing prpblck by 1 is not very meaningful. That is, the only zip code that can increase by 1 is a zip code that starts out with no individuals who are black, and then becomes a zip code that is made up only of individuals who are black.

This is not a very useful marginal effect. In order to interpret the marginal effect more usefully, look at smaller more realistically-sized changes. For instance, an increase of 0. An increase of 0.

Is this economically large? I do not think so, on its face. If, however, there are many medium sodas purchased, such an effect might be better expressed in terms of total expenditure, which might be large. A penny, multiplied by many tens of thousands of sodas in a particular zip code, starts to run into real money. Is the discrimination effect larger or smaller when you control for income?

The discrimination effect is estimated to be significantly smaller when income is excluded from the regression. Notice that we do not need to create a log prpblck variable. If prpblck increases by 0. The coefficient on prpblck goes from 0. Is it roughly what you expected? Given the results of the above regressions and intuition , I expected a negative correla- tion.