Statistical Determination of Genre in Biblical Hebrew - Institute for ...

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666 Steven W. Boydfor a one variable model, but misclassified three passages. On the otherhand, the model using X 1—although it had a slightly lower statisticalsignificance than the X 2model—only misclassified two passages.Because our purpose was to determine the classification accuracyof the model, we chose the model that had the lowest number ofmisclassifications: the model using X 1, the relative frequency ofpreterites. The fact that this model had the fewest misclassificationsconfirmed our analysis up to this point: the ratio of preterites to finiteverbs—as Figures 4–8 indicate—varied the most with genre. It also hadthe advantage of being a simpler model than a two or three independentvariable model; reducing, thereby, the chances of overfitting (explainingnoise instead of signal) and eliminating any possibility of additivity ormulticollinearity in the independent variables. Finally, it made the mostsense in light of Hebrew grammar.The detailed output of the statistics program NCSS can be found inTables C1 and C2 in Appendix C. A summary is presented here.For Model 1—the model with one independent variable X 1(hereaftermodel 1 will be referred to as “our model” and X 1will be referred asX)—the statistics generated take into consideration the number of finiteverbs in each passage. This weighting effectively gave less influence tothe smaller passages in estimating the logistic regression curves.Because our model has only one independent variable, X, the loglikelihood equation has only the intercept, A, and one coefficient, B.The description of our model is as follows: the intercept,A = –5.6562; B = 24.7276. Thus our model for predicting P islog (P/(1 – P)) = –5.6562 + 24.7276 X(4)where X is the relative frequency of preterites. Note that because ourmodel has only one independent variable the right hand side of thisequation is a simple straight line, y = mx + b.Our primary concern in this study was: how good a classifier is ourmodel? Consequently, goodness of fit was an ancillary issue, but theinterested reader can find the calculations in Appendix C, Section C2.
Evidence for an Historical Reading of Genesis 1:1–2:3 6675.5.3 Classification of TextsThe “Percent Correct Predictions” which is a widely acceptedmeasure of the overall performance of the model. Classification of thetexts using the model was done by the usual procedure: if the predictedprobability (P) is greater than or equal to 0.5, then NARRATIVE equals1; if P is less than 0.5, NARRATIVE equals zero.The probability that a text with a given ratio of preterites to finiteverbs is a narrative is its logistic score. Solving for P in equation (4),we obtain the following:P = 1/(1+e (5.6562 – 24.7276X) )(5)where X is the ratio of preterites to finite verbs for each text and e is2.7183, the base of the natural logarithm.The plot of all of these Ps is the logistic regression curve (in blue)seen in Figure 9. The poetry texts are depicted by solid red squares(as in the scatter plots in Figures 6–8) and the narrative texts are solidgreen diamonds (same as scatter plots in Figures 6–8). And for the sakeof comparison, the score of Genesis 1:1–2:3 is plotted as well.1.0Probability of Text being a Narrative0.90.80.70.60.50.40.30.20.1classified as poetryclassified as narrativeLogistic CurveNarrative PassagesPoetry PassagesGenesis 1:12:300 0.1 0.2 0.3 0.4 0.5 0.6 0.7Ratio of Preterites to Total Finite VerbsFigure 9. Logistic regression curve showing the probability a passage is anarrative based on the ratio of preterites to finite verbs.Figure 9
Page 1 and 2: Chapter 9Statistical Determination
Page 3 and 4: Evidence for an Historical Reading
Page 6 and 7: 636 Steven W. Boydan historical rea
Page 8 and 9: 638 Steven W. Boydapproaches this t
Page 10 and 11: 640 Steven W. BoydThe coherence pri
Page 12 and 13: 642 Steven W. Boyd3. Problems of a
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Page 16 and 17: 646 Steven W. Boydis too subjective
Page 18 and 19: 648 Steven W. Boydthey are highly s
Page 20 and 21: 650 Steven W. Boydof events is impo
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Page 32 and 33: 662 Steven W. BoydAs in Figures 6 a
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Page 40 and 41: 670 Steven W. Boydpoetry.With this
Page 42 and 43: 672 Steven W. Boydentire population
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Page 46 and 47: 676 Steven W. Boydnot poetry, with
Page 48 and 49: 678 Steven W. BoydAbraham is noted
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Page 54 and 55: 684 Steven W. Boydto Ashdod, they
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Page 58 and 59: 688 Steven W. BoydBalaam tried to c
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Page 64 and 65: 694 Steven W. Boyd(3) the Samson pe
Page 66 and 67: 696 Steven W. BoydImperfects/Finite
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Page 72 and 73: 702 Steven W. BoydTable B8. Finite
Page 74 and 75: 704 Steven W. Boydcoefficients mode
Page 76 and 77: 706 Steven W. BoydaThis story does
Page 78 and 79: 708 Steven W. BoydTable D5. Chronol
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Page 84 and 85: 714 Steven W. Boydseventh day, and
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718 Steven W. Boydpoetic sensibilit
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732 Steven W. BoydUniversity Press,
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734 Steven W. BoydWeil, G. E., Bibl
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