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All Educational Materials for ADMS 3300 at York University (YORKU)

YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Test Statistic, Mean Squared Error, Delphi Method

2 Page
6 Feb 2015
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YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Analysis Of Variance, Average Absolute Deviation, Gambling

4 Page
6 Feb 2015
Extremte point (2,5) (5,6) (7,3) . Min 3x 4y + 0s1 + 0s2 + 0s3 s. t. 3x 4y = 12 x = 4, y = 0 x = 8, y = 3. X , y , s1 , s2 , s3 . Optimal solution: x =
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Midterm Guide: Utility, Expected Value Of Perfect Information, Perfect Information

2 Page
6 Feb 2015
Chapter 4: decision analysis: decision alternative (d1, d2 ) and state of nature (s1, s1 ) payoff (consequence $, #). Dm without probability: optimisti
View Document
YORKADMS 3300Shamim AbdullahFall

ADMS 3300 Study Guide - Final Guide: Risk Aversion, Risk Premium, Emv

8 Page
15 Jun 2013
Perfect information: evpi = | evwpi evwopi | If the outcome is less than the certainty or other option, take the certainty or other option. The probabi
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Linear Programming Relaxation, Network Model, Mean Squared Error

5 Page
6 Feb 2015
17 regression line: deterministic: appr. relationship (y = a + bx). Probabilistic: real life; random (err): y=a+bx + . Residuals (error): ei = yi i : d
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YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Tachykinin Receptor 1, Stepwise Regression, Homoscedasticity

3 Page
6 Feb 2015
Sample coefficient of correlation: r= sxy sx sy. Test statistic for the 1 t= b1 1 sb1. Confidence interval estimator of 1 b1 t / 2 sb 1 note: v = n-2.
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YORKADMS 3300Shamim AbdullahFall

ADMS 3300 Study Guide - Utility

1 Page
15 Jun 2013
Problems t - m m e n t : a company has just received some "state of the art" electronic equipment from an overseas supplier. The packaging has been dam
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YORKADMS 3300Shamim AbdullahWinter

ADMS 3300 Study Guide - Final Guide: Net Present Value, Emv, Cardinal Utility

20 Page
9 Apr 2014
Order of questions does not follow the order of chapters in the text book: you must hand in this exam before leaving the examination room. Failure to d
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YORKFall

ADMS 3530 Final: 3530_Final_Tutorial Solutions.SU19 (1)

7 Page
29 Oct 2019
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YORKFall

ADMS 3530 Final: 3530-Final Exam-Type X Solutions.F16.post

18 Page
29 Oct 2019
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YORKFall

ADMS 3530 Lecture 1: ADMS 3530 Lecture : (3) Help Session & Tutorial Schedule

2 Page
29 Oct 2019
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YORKFall

ADMS 3530 Chapter 2: (1) ADMS 3530 Course Outline

4 Page
29 Oct 2019
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YORKFall

ADMS 3530 Chapter 3: (2) Detailed Course Schedule

2 Page
29 Oct 2019
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YORKFall

ADMS 3530 Final: 3530 Final Tutorial Questions.SU19 (1)

15 Page
29 Oct 2019
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YORKFall

ADMS 3530 Lecture Notes - Lecture 9: Effective Interest Rate, Savings Account, Callable Bond

7 Page
29 Oct 2019
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YORKFall

ADMS 3530 Final: 3530 Final Exam Formula Sheet.F18

6 Page
29 Oct 2019
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YORKFall

ADMS 3530 Lecture 2: ADMS 3530-SU 19- Final Exam Information (R) (3)

1 Page
29 Oct 2019
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YORKFall

ADMS 3530 Lecture Notes - Lecture 6: Financial Statement, Cash Flow, Current Yield

17 Page
29 Oct 2019
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YORKADMS 3300Mark ThomasFall

ADMS 3300 Lecture 2: ADMS 3300 Final Summary

9 Page
24 Jan 2016
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YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 9: Exponential Function, Risk Aversion, Exponential Utility

9 Page
17 Aug 2016
Risk-aversion can be understood as a utility function, which can be presented as an upward sloping, concave curve (opening downward). On the graph, . 7
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 11: Cardinal Utility, Ordinal Utility, Regional Policy Of The European Union

11 Page
17 Aug 2016
Chapter 16: conflicting objectives fundamental objectives and the additive utility function. Criteria for fundamental objectives and their attributes.
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YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 12: Fixed Cost, Multilinear Map, Risk Premium

7 Page
17 Aug 2016
Chapter 17 conflicting objectives ii - multi-attribute utility models with interactions. Methods to deal with multiple conflicting objectives. Decide h
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YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 7: Conditional Probability, Perfect Information, Expected Value Of Perfect Information

6 Page
17 Aug 2016
To answer these questions, we will have to determine the value of perfect information and imperfect information. Let a = do(cid:449) jo(cid:374)es i(ci
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture 6: Week 6 Ch 8

17 Page
7 Jul 2016
We will learn the principles behind information valuation. Chronic health evaluation). (cid:61607) apache iii evaluates the patient(cid:8217)s risk as
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YORKADMS 3300Shamim AbdullahWinter

ADMS 3300 Study Guide - Final Guide: Net Present Value, Emv, Cardinal Utility

20 Page
9 Apr 2014
Order of questions does not follow the order of chapters in the text book: you must hand in this exam before leaving the examination room. Failure to d
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Midterm Guide: Utility, Expected Value Of Perfect Information, Perfect Information

2 Page
6 Feb 2015
Chapter 4: decision analysis: decision alternative (d1, d2 ) and state of nature (s1, s1 ) payoff (consequence $, #). Dm without probability: optimisti
View Document
YORKADMS 3300Shamim AbdullahFall

ADMS 3300 Study Guide - Final Guide: Risk Aversion, Risk Premium, Emv

8 Page
15 Jun 2013
Perfect information: evpi = | evwpi evwopi | If the outcome is less than the certainty or other option, take the certainty or other option. The probabi
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Test Statistic, Mean Squared Error, Delphi Method

2 Page
6 Feb 2015
View Document
YORKADMS 3300Shamim AbdullahFall

ADMS 3300 Study Guide - Utility

1 Page
15 Jun 2013
Problems t - m m e n t : a company has just received some "state of the art" electronic equipment from an overseas supplier. The packaging has been dam
View Document
YORKADMS 3300Mark ThomasFall

ADMS 3300 Lecture 2: ADMS 3300 Final Summary

9 Page
24 Jan 2016
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 9: Exponential Function, Risk Aversion, Exponential Utility

9 Page
17 Aug 2016
Risk-aversion can be understood as a utility function, which can be presented as an upward sloping, concave curve (opening downward). On the graph, . 7
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Linear Programming Relaxation, Network Model, Mean Squared Error

5 Page
6 Feb 2015
17 regression line: deterministic: appr. relationship (y = a + bx). Probabilistic: real life; random (err): y=a+bx + . Residuals (error): ei = yi i : d
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Analysis Of Variance, Average Absolute Deviation, Gambling

4 Page
6 Feb 2015
Extremte point (2,5) (5,6) (7,3) . Min 3x 4y + 0s1 + 0s2 + 0s3 s. t. 3x 4y = 12 x = 4, y = 0 x = 8, y = 3. X , y , s1 , s2 , s3 . Optimal solution: x =
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 11: Cardinal Utility, Ordinal Utility, Regional Policy Of The European Union

11 Page
17 Aug 2016
Chapter 16: conflicting objectives fundamental objectives and the additive utility function. Criteria for fundamental objectives and their attributes.
View Document

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YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 7: Conditional Probability, Perfect Information, Expected Value Of Perfect Information

6 Page
17 Aug 2016
To answer these questions, we will have to determine the value of perfect information and imperfect information. Let a = do(cid:449) jo(cid:374)es i(ci
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 9: Exponential Function, Risk Aversion, Exponential Utility

9 Page
17 Aug 2016
Risk-aversion can be understood as a utility function, which can be presented as an upward sloping, concave curve (opening downward). On the graph, . 7
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 11: Cardinal Utility, Ordinal Utility, Regional Policy Of The European Union

11 Page
17 Aug 2016
Chapter 16: conflicting objectives fundamental objectives and the additive utility function. Criteria for fundamental objectives and their attributes.
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture Notes - Lecture 12: Fixed Cost, Multilinear Map, Risk Premium

7 Page
17 Aug 2016
Chapter 17 conflicting objectives ii - multi-attribute utility models with interactions. Methods to deal with multiple conflicting objectives. Decide h
View Document
YORKADMS 3300Shamim AbdullahSummer

ADMS 3300 Lecture 6: Week 6 Ch 8

17 Page
7 Jul 2016
We will learn the principles behind information valuation. Chronic health evaluation). (cid:61607) apache iii evaluates the patient(cid:8217)s risk as
View Document
YORKADMS 3300Mark ThomasFall

ADMS 3300 Lecture 2: ADMS 3300 Final Summary

9 Page
24 Jan 2016
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Midterm Guide: Utility, Expected Value Of Perfect Information, Perfect Information

2 Page
6 Feb 2015
Chapter 4: decision analysis: decision alternative (d1, d2 ) and state of nature (s1, s1 ) payoff (consequence $, #). Dm without probability: optimisti
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Test Statistic, Mean Squared Error, Delphi Method

2 Page
6 Feb 2015
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Analysis Of Variance, Average Absolute Deviation, Gambling

4 Page
6 Feb 2015
Extremte point (2,5) (5,6) (7,3) . Min 3x 4y + 0s1 + 0s2 + 0s3 s. t. 3x 4y = 12 x = 4, y = 0 x = 8, y = 3. X , y , s1 , s2 , s3 . Optimal solution: x =
View Document
YORKADMS 3300Malcolm Mac TaggartWinter

ADMS 3300 Study Guide - Final Guide: Linear Programming Relaxation, Network Model, Mean Squared Error

5 Page
6 Feb 2015
17 regression line: deterministic: appr. relationship (y = a + bx). Probabilistic: real life; random (err): y=a+bx + . Residuals (error): ei = yi i : d
View Document

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