{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Lect23 - Leveraged Outliers Dependent Errors and Time...

Info icon This preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
Leveraged Outliers Dependent Errors and Time Series Outline Leveraged Outliers Dependent Errors and Time Series 1 / 15 ISOM 2500 Lect 23: Leveraged Outliers; Dependent Errors and Time Series
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Leveraged Outliers Dependent Errors and Time Series How Much to Bid? A contractor is bidding on a project to construct an 875 square-foot addition to a home. If he bids too low, he loses money on the project. If he bids too high, he does not get the job. The contractor has kept data that record the costs for 30 similar project: All but one of his previous projects are smaller than 875 square feet. His one project at 900 square feet is an outlier. 2 / 15 ISOM 2500 Lect 23: Leveraged Outliers; Dependent Errors and Time Series
Image of page 2
Leveraged Outliers Dependent Errors and Time Series Keep or Remove the Outlier? If keep the outlier, then the fitted equation is Estimated Cost ($) = 5887 . 74 + 27 . 44 * Size (sq.ft.) If remove the outlier, then the fitted equation becomes Estimated Cost ($) = 1558 . 17 + 44 . 74 * Size (sq.ft.) 3 / 15 ISOM 2500 Lect 23: Leveraged Outliers; Dependent Errors and Time Series
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Leveraged Outliers Dependent Errors and Time Series Leveraged Observations Say that an observation is leveraged in regression if it has a small or large value of the explanatory variable. The 900 square feet outlier is a leveraged observation as it has a large value of the explanatory variable (actually much larger than the remaining) A leveraged observation has the ability to pull the regression line in its direction, like in the bid case 4 / 15 ISOM 2500 Lect 23: Leveraged Outliers; Dependent Errors and Time Series
Image of page 4
Leveraged Outliers
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

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