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Logistic Regression using Excel OLS with Nudge

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1 Logistic Regression using Excel OLS with Nudge
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Logistic Regression using Excel OLS with Nudge Milo Schield, Augsburg College Elected Member: International Statistical Institute US Rep: International Statistical Literacy Project VP. National Numeracy Network JSM Philadelphia July 31, 2017 2008SchieldNNN6up.pdf 2013Schield-MBAA 1 1 1

2 Logistic Regression (LR) is Common and Important
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Logistic Regression (LR) is Common and Important Yes/No decisions (binary outcomes) are common in Marketing: Predicting whether someone will buy Finance: Deciding whether to grant a loan Medicine: Determining whether one has a condition Epidemiology: Identifying related factors to an outcome Logistic regression is the most common way of modelling binary outcomes. It is one of the main topics in Stat 200. It is almost never taught in Stat 100. But it should be!!! 2008SchieldNNN6up.pdf 2013Schield-MBAA 2 2 2

3 Why Isn’t Logistic Regression Taught in Intro Course?
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Why Isn’t Logistic Regression Taught in Intro Course? LR isn’t taught in Stat 100 for several reasons: Complexity: Maximum likelihood estimation is complex as are odds, log-odds and quality measures. Availability: Not available in Excel or on calculators. Infinity: |Log(Odds)| goes to infinity when p=0 or p=1 Non-analytic: Requires trial & error to find best solution. Time: No extra time for extra topics in Intro Statistics. 2008SchieldNNN6up.pdf 2013Schield-MBAA 3 3 3

4 The Data: Height and Gender
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March The Data: Height and Gender . 2008SchieldNNN6up.pdf 2013Schield-MBAA 4 4 4

5 Simple Model #1: Connect the Mean Heights
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Simple Model #1: Connect the Mean Heights . 2008SchieldNNN6up.pdf 2013Schield-MBAA 5 5 5

6 Statistical Literacy for ManagersStatLit for Managers
Analyzing Numbers in the News 15 May 2008 2013 1 March Simple Model #2: Linear . 2008SchieldNNN6up.pdf 2013Schield-MBAA 6 6 6

7 Simple Model #3: Logistic Curve
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Simple Model #3: Logistic Curve . 2008SchieldNNN6up.pdf 2013Schield-MBAA 7 7 7

8 Statistical Literacy for ManagersStatLit for Managers
Analyzing Numbers in the News 15 May 2008 2013 1 March Simple Solution #4 This simple solution involves two shortcuts: Use the logistic function, but nudge the zero-one data to be epsilon and one minus epsilon. This ‘nudge’ eliminates the infinities in Ln[Odds(p)]. Use the Ordinary Least Squares (OLS) in place of Maximum Likelihood Estimation (MLE). This eliminates the need for industrial-strength software. Benefits: This allows more attention to the results and to subsequent topics such as confounding and classification. 2008SchieldNNN6up.pdf 2013Schield-MBAA 8 8 8

9 Ln[Odds(Nudged Prob)]
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Ln[Odds(Nudged Prob)] . 2008SchieldNNN6up.pdf 2013Schield-MBAA 9 9 9

10 OLS Results: Regress Gender on Height
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March OLS Results: Regress Gender on Height . 2008SchieldNNN6up.pdf 2013Schield-MBAA 10 10 10

11 Translate back to P-space; Plot Probability vs. Height
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Translate back to P-space; Plot Probability vs. Height . 2008SchieldNNN6up.pdf 2013Schield-MBAA 11 11 11

12 How close is OLS to MLE? Height: Fairly close…
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March How close is OLS to MLE? Height: Fairly close… . 2008SchieldNNN6up.pdf 2013Schield-MBAA 12 12 12

13 MLE vs. OLS+Nudge: Significant Difference? No!
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March MLE vs. OLS+Nudge: Significant Difference? No! . 2008SchieldNNN6up.pdf 2013Schield-MBAA 13 13 13

14 How close is OLS to MLE? Weight: Fairly close…
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March How close is OLS to MLE? Weight: Fairly close… . 2008SchieldNNN6up.pdf 2013Schield-MBAA 14 14 14

15 MLE vs. OLS+Nudge: No Significant Difference
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March MLE vs. OLS+Nudge: No Significant Difference . 2008SchieldNNN6up.pdf 2013Schield-MBAA 15 15 15

16 Statistical Literacy for ManagersStatLit for Managers
Analyzing Numbers in the News 15 May 2008 2013 1 March Quotes “the maximized log likelihood method has always impressed me as an exercise in excessive fine-tuning, reminiscent on some occasions of what Alfred North Whitehead identified as the fallacy of misplaced concreteness, and on others of what Freud described as the narcissism of small differences.” Comparing exact MLE with OLS regression of Ln[Odds(p)] where p is for grouped data: “The second reason is that in most real-world cases there is little if any practical difference between the results of the two methods.” Richard Lowry, Vassar. 2008SchieldNNN6up.pdf 2013Schield-MBAA 16 16 16

17 Statistical Literacy for ManagersStatLit for Managers
Analyzing Numbers in the News StatLit for Managers Statistical Literacy for ManagersStatLit for Managers 15 May 2008 2013 1 March Recommendation Those teaching intro statistics needs to think broadly. Going deeper is good for those who plan to continue on. But almost none of those taking Stat 101 will take Stat 201. Introducing logistic regression using OLS is simple. The difference between MLE and OLS may not be significant . Introducing logistic regression in STAT 101 opens the door for other multivariate items such as confounding, classification analysis and discriminant analysis. 2008SchieldNNN6up.pdf 2013Schield-MBAA 17 17 17

18 So Why Won’t It Be Taught?
Analyzing Numbers in the News StatLit for Managers Statistical Literacy for ManagersStatLit for Managers 15 May 2008 2013 1 March So Why Won’t It Be Taught? OLS is not right in this case. We don’t want to teach our students bad methods. This OLS+nudge shortcut has a serious lack of rigor. This is unprofessional; we shouldn’t allow it. Reply: Lack of rigor vs. rigor mortis? Can the perfect be the enemy of the good? What is our goal? For students to understand some important ideas or be taught correctly even if they don’t understand? 2008SchieldNNN6up.pdf 2013Schield-MBAA 18 18 18

19 Statistical Literacy for ManagersStatLit for Managers
Analyzing Numbers in the News 15 May 2008 2013 1 March Conclusion Focus on GAISE 2017 goals. Multivariate thinking More focus on confounding See Schield (2016) Offering Stat 102: Social Statistics for Decision Makers. 2008SchieldNNN6up.pdf 2013Schield-MBAA 19 19 19

20 Much More Important Issues Un-Scientific American (2017)
Statistical Literacy for ManagersStatLit for Managers StatLit for Managers Analyzing Numbers in the News 15 May 2008 2013 1 March Much More Important Issues Un-Scientific American (2017) . 2008SchieldNNN6up.pdf 2013Schield-MBAA 20 20 20

21 Much More Important Issues Un-Scientific American
Analyzing Numbers in the News StatLit for Managers Statistical Literacy for ManagersStatLit for Managers 15 May 2008 2013 1 March Much More Important Issues Un-Scientific American Three strikes and you are out! Association is not statistically significant Association is not materially significant Author knows that both of these are true, yet puts the association in the headline to the story Moral: Statistical educators need to put more attention on misuses of statistics in the everyday media. To do less is professional negligence. 2008SchieldNNN6up.pdf 2013Schield-MBAA 21 21 21

22 Statistical Literacy for ManagersStatLit for Managers
Analyzing Numbers in the News 15 May 2008 2013 1 March Bibliography Carlberg, Conrad (2012). Decision Analytics: Microsoft Excel. Que Publishing. Lowry, R. (2017). Moore, David (2001). Statistical Literacy and Statistical Competence in the New Century. IASE Proceedings. Schield, Milo (2017). Tools at Schield, Milo (2016). Logistic Regression using Minitab and Pulse dataset. Minitab-MLE1-Test1.pdf 2008SchieldNNN6up.pdf 2013Schield-MBAA 22 22 22

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