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## Info3010, Week 15 notes

by: Rebecca Evans

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# Info3010, Week 15 notes Info3010

Marketplace > Tulane University > Business > Info3010 > Info3010 Week 15 notes
Rebecca Evans
Tulane
GPA 4.0

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Notes on linear regression, point prediction, and interval prediction
COURSE
PROF.
Srinivas Krishnamoorthy
TYPE
Class Notes
PAGES
1
WORDS
KARMA
25 ?

## 0

This 1 page Class Notes was uploaded by Rebecca Evans on Wednesday April 20, 2016. The Class Notes belongs to Info3010 at Tulane University taught by Srinivas Krishnamoorthy in Spring 2016. Since its upload, it has received 19 views. For similar materials see Business Modeling in Business at Tulane University.

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Date Created: 04/20/16
Week 15 Notes 4.18 Broadway Regression Model  Use previous week’s revenue (known data) to predict coming week’s revenue (unknown)  New variable: if the upcoming week is a holiday week  Holiday week variable: binary (is or is not holiday) o In table under p-value (column) and holiday? (row)  Should be able to write regression equation given regression outputs  Gross \$=\$11,807 + (0.96)(previous week \$) + (\$88,820)(holiday) Line for holiday week Gros Line for non-holiday s \$ \$88,82 week 0 (slope = 0.96) \$11,80 7 Previous Week’s \$  Previous week \$= \$200,000 and next week is a holiday week  Point prediction: o Gross \$=\$11,807 + (0.96)(200,000) + (\$88,820)(1)=\$293,200  99% Interval prediction: o \$293,200 +/– (3)(\$64,172)=\$485,718 to \$100,684  High error because high risk industry o Point prediction+/- (3)(standard error) o Standard error found under “regression statistics” table o 99% interval3 standard deviations o 95% interval2 standard deviations Course Wrap Up & Overview  Foundations of modeling o Modularity: keep data, calculations, and output separate o Parameterizations: use cell references o Sensitivity: use data tables for What-if analysis  Deterministic modeling o Optimization using solver  Probabilistic modeling o Normal distribution o Decision tress and value of info o Monte Carlo simulation  Predictive modeling o Linear regression  Advanced modeling

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