New User Special Price Expires in

Let's log you in.

Sign in with Facebook


Don't have a StudySoup account? Create one here!


Create a StudySoup account

Be part of our community, it's free to join!

Sign up with Facebook


Create your account
By creating an account you agree to StudySoup's terms and conditions and privacy policy

Already have a StudySoup account? Login here

Statistical Models Theory and Application

by: Floy Kub

Statistical Models Theory and Application STAT 215B

Marketplace > University of California - Berkeley > Statistics > STAT 215B > Statistical Models Theory and Application
Floy Kub

GPA 3.64


Almost Ready


These notes were just uploaded, and will be ready to view shortly.

Purchase these notes here, or revisit this page.

Either way, we'll remind you when they're ready :)

Preview These Notes for FREE

Get a free preview of these Notes, just enter your email below.

Unlock Preview
Unlock Preview

Preview these materials now for free

Why put in your email? Get access to more of this material and other relevant free materials for your school

View Preview

About this Document

Class Notes
25 ?




Popular in Course

Popular in Statistics

This 8 page Class Notes was uploaded by Floy Kub on Thursday October 22, 2015. The Class Notes belongs to STAT 215B at University of California - Berkeley taught by Staff in Fall. Since its upload, it has received 63 views. For similar materials see /class/226728/stat-215b-university-of-california-berkeley in Statistics at University of California - Berkeley.


Reviews for Statistical Models Theory and Application


Report this Material


What is Karma?


Karma is the currency of StudySoup.

You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 10/22/15
Statistics 215b 112003 DR Brillinger Data mining A field in search of a definition a vague concept D Hand H Mannila and P Smyth 2001 Principles of Data Mining MIT Press Cambridge Some definitionsdescriptions Data mining is the analysis of often large observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner Hand et al N data mining refers to extracting or mining39 knowledge from large amounts of data N data mining should have been more appropriately named knowledge mining from data An analytic process designed to explore data usually large amounts of data typically business or market related in search of consistent andor systematic relationships between variables and then to validate the findings by applying the detected patterns to new subsets of data Data mining is the application of a specific algorithm usually within machine learning for extracting patterns from data A simple approach to the theory of data mining is to declare that data mining is statistics perhaps on larger data sets than previously m Data mining is the nontrivial process of identifying valid novel potentially useful and ultimately understandable patterns in data Data mining is the process of extracting previously unknown comprehensible and actionable information from large databases and using it to make crucial business decisions Data mining is a set of methods used in the knowledge discovery process to distinguish previously unknown relationships and patterns within data Data mining is the process of discovering advantageous patterns in data Data mining is a decision support process where we look in large data bases for unknown and unsuspected patterns of information Data mining is the process of seeking interesting or valuable information within large databases Data mining is the exploration and automatic analysis of large data sets by automatic or semiautomatic means with the purpose of discovering meaningful patterns Data mining the science of extracting useful information from large data sets Data mining is a knowledge discovery process of extracting previously unknown actionable information from very large databases Data mining is finding interesting structure patterns statistical models relationships in databases Data mining is a process that uses a variety of tools to discover patterns and relationships in data that may be used to make valid predictions Data mining The process of efficient discovery of nonobvious valuable patterns from a large collection of data Data mining is exploratory data analysis with little or no human interaction using computationally feasible techniques ie the attempt to find interesting structure unknown a priori Data mining is the art and science of teasing meaningful information and patterns out of large quantities of data Data mining is in fact an umbrella term for a variety of analytic techniques Data mining is about digging into data to find subtle patterns and informative relationships amongst the data resources piling up in today s businesses Data mining the extraction of hidden predictive information from large databasesm The knowledge discovery and data mining KDD field draws on the findings from statistics databases and artificial intelligence to construct tools that let users gain insight from massive data sets Data mining Objective explore the data for interesting patterns Approach size is overcome to search for information Criteria data become findings by obtaining questions Focus association is sought through coincidence Stats sampling and design aid start Sequential analysis aids end Process reduce the data with maximum gain Behavior break old rules powered by technology Attitude seek local effects proactively amp persistentlyquot Arnold Goodman Data mining refers to the exaggerated claims of significance and or forecasting precision generated by the selective reporting of results obtained when the structure of the model is determined experimentally by repeated applications of such procedures as regression analysis to the same body of data m synonymous with data scrubbing data fishing Darwinian econometrics survival of the fittest m the term data mining is sometimes used perjoratively to describe such work particularly when an analyst has searched over a large model space without adjusting for such a search or testing the resulting model on new data Data mining fishing grubbing number crunching These are value laden terms we use to disparage each other s empirical work with the linear regression model A less provocative description would be quotspecification searchquot and a catch all definition is the data dependent process of selecting a statistical model mining suggests that the activity may in fact be productive Tukey N data analysis which I take to include among other things procedures for analyzing data techniques for interpreting the results of such procedures ways of planning the gathering of data to make its analysis easier more precise or more accurate and all the machinery and results of mathematical statistics which apply to analyzing data


Buy Material

Are you sure you want to buy this material for

25 Karma

Buy Material

BOOM! Enjoy Your Free Notes!

We've added these Notes to your profile, click here to view them now.


You're already Subscribed!

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

Why people love StudySoup

Steve Martinelli UC Los Angeles

"There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

Allison Fischer University of Alabama

"I signed up to be an Elite Notetaker with 2 of my sorority sisters this semester. We just posted our notes weekly and were each making over $600 per month. I LOVE StudySoup!"

Jim McGreen Ohio University

"Knowing I can count on the Elite Notetaker in my class allows me to focus on what the professor is saying instead of just scribbling notes the whole time and falling behind."

Parker Thompson 500 Startups

"It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

Become an Elite Notetaker and start selling your notes online!

Refund Policy


All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email


StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here:

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to

Satisfaction Guarantee: If you’re not satisfied with your subscription, you can contact us for further help. Contact must be made within 3 business days of your subscription purchase and your refund request will be subject for review.

Please Note: Refunds can never be provided more than 30 days after the initial purchase date regardless of your activity on the site.