101C_W4_L1_DZ.pdf - 101C ADMIN STUFF 101C IMPORTANT STUFF ▸ Read the syllabus(on CCLE ▸ Office Hours Thursday 5:00-6:00pm ▸ Midterm in class

101C_W4_L1_DZ.pdf - 101C ADMIN STUFF 101C IMPORTANT STUFF...

This preview shows page 1 - 10 out of 47 pages.

ADMIN STUFF 101C
Image of page 1

Subscribe to view the full document.

101C IMPORTANT STUFF Read the syllabus (on CCLE) Office Hours: Thursday 5:00-6:00pm Midterm in class: November 12 Final: Private Kaggle Data Challenge Send all academic questions to Piazza, not email. Feel free to answer/participate in discussions.
Image of page 2
4 101C
Image of page 3

Subscribe to view the full document.

101C WHAT IS THE POINT? INFERENCE (USUALLY) Sample A Data Set Some Population The Actual Inferential Population Some mechanism of selection or subsetting results in … Informs us about …
Image of page 4
101C WHAT IS THE POINT? INFERENCE (USUALLY) Considering the mechanism that was used to subset the sample, or data set from the larger population . . . A purely random method of sampling is usually regarded as implying that the larger population from which the sample was obtained is equivalent — or at least a proxy for — the actual inferential population.
Image of page 5

Subscribe to view the full document.

101C WHAT IS THE POINT? INFERENCE (USUALLY) Sample A Data Set Some Population The Actual Inferential Population Some mechanism of selection or subsetting results in … Informs us about …
Image of page 6
101C WHAT IS THE POINT? INFERENCE (USUALLY) In probability theory, this concept is sometimes talked about in terms of “exchangeability”. In stats circles, we talk in terms of “representativeness”.
Image of page 7

Subscribe to view the full document.

101C WHAT IS THE POINT? INFERENCE (USUALLY) Sample A Data Set Some Population The Actual Inferential Population Some mechanism of selection or subsetting results in … Informs us about …
Image of page 8
101C WHAT IS THE POINT? INFERENCE (USUALLY) Perhaps the most common response when the data are not generated by random sampling is to proceed as if the data on hand were a random sample from an imaginary population. . . . such populations are sometimes called “superpopulations” (because the data can be considered a population). A superpopulation is defined in a somewhat circular way as the population from which the data would have come if the data were a random sample.
Image of page 9

Subscribe to view the full document.

Image of page 10
  • Spring '15
  • vivianlew

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern

Ask Expert Tutors You can ask You can ask ( soon) You can ask (will expire )
Answers in as fast as 15 minutes