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HP_Prime_MATH: Manual

Schrausser, Dietmar Gerald

Abstract

Mathematical and statistical applications for HP Prime (s. HP Inc., 2017; Schrausser, 2025). Algorithms are presented in context with the corresponding scope of application (s. Functions). CAS programs (1), HP Prime User functions (2) and functions for HP Prime Applications (3) are listed in alphabetical order (s. Source Codes), for a comparison to corresponding SCHRAUSSER-MAT functions (Schrausser, 2022) see Table 2. In addition to the source codes of the functions, raw data sets are provided for correlation- as well as resampling-methods (s. Data).

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Manual HP_Prime_MATH Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M i HP_Prime_MATH: Manual Open Library ID: OL60452436M Copyright ÂĐ 2025 Dietmar Gerald Schrausser, Creative Commons Attribution 4.0 International. Edition 1. Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M ii Contents Introduction 1 Functions Correlation 1 Exposure functions 7 Functions of integration 9 Distribution functions 11 Probability 20 Combinatorics 23 Resampling 24 Complex plane 27 Source codes CAS functions 31 User functions 60 Application functions 69 Graph 3D 72 Solve 73 Data Functions 75 Lists 76 Matrices 77 References 99 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 1 HP_Prime_MATH: Manual Dietmar G. Schrausser Karl-Franzens University, Graz, Austria Introduction Mathematical and statistical applications for HP Prime (s. HP Inc., 2017; Schrausser, 2025a, b). Algorithms are presented in context with the corresponding scope of application (s. Functions). CAS programs (i), HP Prime User functions (ii) and functions for HP Prime Applications (iii) are listed in alphabetical order (s. Source Codes), for a comparison to corresponding SCHRAUSSER-MAT functions (Schrausser, 2022a) see Table 2. In addition to the source codes of the functions, raw data sets are provided for correlationas well as resampling-methods (s. Data). On mathematical statistical methods in general see e.g. Cox and Hinkley (1974), Bortz and Weber (2005), Lehmann and Romano (2008) or Bortz and Schuster (2010), Schrausser (2024a) provides a comprehensive overview of the most important distribution functions and corresponding algorithms. Introducing works on resampling methods are given by e.g. Good (2006) or Beasley and Rodgers (2009), for calculus and theory of functions see e.g. Meyberg and Vachenauer (2001a, b) or Remmert and Schumacher (2002), on complex numbers in the complex plane see e.g. Burckel (2021) and Vince (2021). For the history of statistical inference in general see e.g. Stigler (1986) and Hald (1990, 1998, 2003, 2007), historical foundations of mathematics are thematized and discussed in e.g. Suter (1887), Heath (1921a, b), Boyer (1968), Neugebauer (1969), Ewald (1996a, b), Katz (2009) or Merzbach and Boyer (2011). Functions Correlation To measure the degree of a linear relation between variables, Karl Pearson (1904) was developing statistical procedures for biometry including the correlation and regression coefficients based on the works of Bravais (1844) and Galton (1877) who introduced the symbol 𝑟, on the then designation of the term reversion. Table 1. Appropriate correlation coefficients; product-moment or Pearson correlation 𝑟ð‘Ĩð‘Ķ, Spearman’s rank correlation coefficient rho 𝜌, biserial (or biseral) coefficients 𝑟𝑏𝑖𝑠, 𝑟𝑝𝑏, 𝑟𝑏𝑖𝑠𝑅 and phi coefficient 𝛷 at the corresponding scale levels, interval i, ordinal o, and nominal n. i o n i 𝑟ð‘Ĩð‘Ķ o 𝜌 Âđ n 𝑟𝑏𝑖𝑠, 𝑟𝑝𝑏 𝑟𝑏𝑖𝑠𝑅 𝛷 Âē Âđ) also Kendall’s tau 𝜏 (1938) or Somers’ 𝐷 (1962). Âē) also tetrachoric correlation ð‘Ÿð‘Ąð‘’ð‘Ą. Creative Commons Attribution 4.0 International Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 2 The methodological apparatus of factor analysis as a further and broader concept, based on multiple regression and matrix calculation was first discussed by Charles Edward Spearman (1904), later the initial developed took place by Louis Leon Thurstone (1931, 1934, 1935; s. also Cattell, 1966). [KOR|IC_M] [rxy|RED|tr|TRW|pRW|pRWx] [E01] Pearson product-moment correlation coefficient 𝑟ð‘Ĩð‘Ķ Bravais (1844), Galton (1877), Pearson (1904, 1905). 𝑟ð‘Ĩð‘Ķ=𝜎ð‘Ĩð‘Ķ 2 𝜎ð‘Ĩ⋅𝜎ð‘Ķ, 𝜎ð‘Ĩð‘Ķ 2=∑ (ð‘Ĩ𝑖−ð‘Ĩ)⋅(ð‘Ķ𝑖−ð‘Ķ) 𝑛𝑖=1 𝑛 with ð‘Ą(𝑑𝑓)=𝑟⋅√𝑛−2 √1−𝑟2 where 𝑟2 = coefficient of determination, redundancy ð‘‘ð‘’ð‘Ą%=𝑟2⋅100 𝜎ð‘Ĩð‘Ķ 2 = covariance of ð‘Ĩ and ð‘Ķ 𝑑𝑓 = 𝑛−2 [RHO] Spearman’s 𝜌 Equivalent to the product moment correlation when rank values are present (s. Spearman, 1904). 𝑟𝑠=𝜌=1−6⋅∑𝑑𝑖2𝑛𝑖=1 𝑛⋅(𝑛2−2) with ð‘Ą(𝑑𝑓)=𝜌⋅√𝑛−2 √1−𝜌2;𝑛â‰Ĩ30 where 𝑑𝑖 = rank difference of ð‘Ĩ𝑖 and ð‘Ķ𝑖 𝑑𝑓 = 𝑛−2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 3 [TAU] Kendall’s tau 𝜏𝑎 Without adjustment for ties (s. Kendall, 1938). 𝜏𝑎=1− 2⋅𝑛𝑑 0.5⋅𝑛⋅(𝑛−1), with 𝑧=3⋅𝜏𝑎⋅√𝑛⋅(𝑛−1) √2⋅(2⋅𝑛+5);𝑛>10 alternatively 𝑧= 𝑛𝑐−𝑛𝑑 √1 18⋅𝑛⋅(𝑛−1)⋅(2⋅𝑛+5) where 𝑛 = total number of pairs 𝑛𝑑 = number of discordant pairs 𝑛𝑐 = number of concordant pairs, with 𝑛𝑐=(𝑛2)−𝑛𝑑 [DELTA2] Somers’ 𝐷 For binary data [0,1] (s. Somers, 1962). 𝐷𝑌𝑋=𝑛1,1 𝑛−𝑛1,0 𝑛 where 𝑛 = total number of pairs 𝑛1,1 = number of pairs with 𝑌=1,𝑋=1 𝑛1,0 = number of pairs with 𝑌=1,𝑋=0 [rpbis] Point biserial correlation coefficient 𝑟𝑝𝑏 Also point biseral. 𝑟𝑝𝑏=ð‘Ĩ1−ð‘Ĩ0 𝜎ð‘Ĩ⋅√𝑛1⋅𝑛2 𝑛2 with ð‘Ą(𝑑𝑓)=𝑟𝑝𝑏⋅√𝑛−2 √1−𝑟𝑝𝑏 2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 4 where 𝑑𝑓 = 𝑛−2 [rbis|srbis|zrbis|prbis] Biserial correlation coefficient 𝑟𝑏𝑖𝑠 Pearson (1909), see e.g. Tate (1955), also called biseral. 𝑟𝑏𝑖𝑠=ð‘Ĩ1−ð‘Ĩ0 𝜎ð‘Ĩ⋅𝑛1⋅𝑛2 𝜗⋅𝑛2 with 𝑧=𝑟𝑏𝑖𝑠 𝜎𝑟𝑏𝑖𝑠, 𝜎𝑟𝑏𝑖𝑠=√𝑛1⋅𝑛2 𝜗⋅𝑛⋅√𝑛 where 𝜗= 1 √2⋅π⋅𝑒−ðđ(𝑝=𝑛0 𝑛)2 2 [rbisR|U_1|U_2|zrbisR|prbisR] Rank biserial correlation coefficient 𝑟𝑏𝑖𝑠𝑅 Also rank biseral correlation, corresponds to the effect size for the Mann–Whitney 𝑈 test (Mann & Whitney, 1947). 𝑟𝑏𝑖𝑠𝑅=2𝑛⋅(𝑖1−𝑖2) with 𝑧= 𝑈−𝑛1⋅𝑛2 2 √𝑛1⋅𝑛2⋅(𝑛+1) 12 where 𝑈=𝑛1⋅𝑛2+𝑛12+𝑛1 2−∑ð‘Ĩ𝑖 𝑛1 𝑖=1 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 5 [PHC] [PHI|xPHI|pPHI] Phi coefficient 𝛷 Yule (1912). 𝛷= 𝑎⋅𝑑−𝑏⋅𝑐 √(𝑎+𝑐)⋅(𝑏+𝑑)⋅(𝑎+𝑏)⋅(𝑐+𝑑) with 𝜒(𝑑𝑓) 2=𝑛⋅𝛷2 where 𝑑𝑓=1 [PHC] [rtet|srtet|prtet] Tetrachoric correlation ð‘Ÿð‘Ąð‘’ð‘Ą Pearson (1900a), Everitt (1910, 1912), s. e.g. Brown (1977), Digby (1983), also Bonett and Price (2005) or Long et al. (2009), proposed approximate algorithm. ð‘Ÿð‘Ąð‘’ð‘Ą=cosπ 1+√𝑏⋅𝑐 𝑎⋅𝑑 with 𝑧=ð‘Ÿð‘Ąð‘’ð‘Ą ðœŽð‘Ÿð‘Ąð‘’ð‘Ą, ðœŽð‘Ÿð‘Ąð‘’ð‘Ą=√𝑎+𝑏 𝑛⋅𝑎+𝑐 𝑛⋅𝑐+𝑑 𝑛⋅𝑏+𝑑 𝑛 𝑛⋅1 𝜗ð‘Ĩ⋅𝜗ð‘Ķ where 𝜗ð‘Ĩ=1 √2⋅π⋅e−ðđ(𝑝=𝑐+𝑑 𝑛)2 2 𝜗ð‘Ķ=1 √2⋅π⋅e−ðđ(𝑝=𝑏+𝑑 𝑛)2 2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 6 [PKR] [rxy_z|zrxy_z|prxy_z|ry_xz] Partial correlation 𝑟ð‘Ĩð‘Ķ⋅𝑧 𝑟ð‘Ĩð‘Ķ⋅𝑧=𝑟ð‘Ĩð‘Ķ−𝑟ð‘Ĩ𝑧⋅𝑟ð‘Ķ𝑧 √1−𝑟ð‘Ĩ𝑧 2⋅√1−𝑟ð‘Ķ𝑧 2 with 𝑧=𝑍𝑟ð‘Ĩð‘Ķ⋅𝑧⋅√𝑛−2 and semi partial correlation 𝑟ð‘Ķ(ð‘Ĩ⋅𝑧)=𝑟ð‘Ĩð‘Ķ−𝑟ð‘Ĩ𝑧⋅𝑟ð‘Ķ𝑧 √1−𝑟ð‘Ĩ𝑧 2, [ZCor] [Zr|rZ] Fisher 𝑍-transformation Fisher (1915). 𝑍=12⋅ln1+𝑟 1−𝑟 with 𝑧= 𝑍 √1 𝑛−3 and 𝑟𝑍=𝑒2⋅𝑍−1 𝑒2⋅𝑍+1 [Zrr|prr] Fisher 𝑍 difference, Cohen’s 𝑞 Cohen (1988, p. 110). 𝜃=𝑑𝑍=𝑍𝑟1−𝑍𝑟2 with 𝑧= 𝑑𝑍 √1 𝑛1−3+1 𝑛2−3 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 13 [Q01_|AMG|HM_] Harmonic mean ð‘Ĩ ð‘Ĩ=ðŧ= 𝑛 ∑ð‘Ĩ𝑖−1𝑛𝑖=1 , ð‘Ĩ󰇘=∑𝑛𝑖 𝑘𝑖=1 ∑𝑛𝑖 ð‘Ĩ𝑖 𝑘𝑖=1 [Q01_] Coefficient of variation 𝜔 𝜔=𝜎ð‘Ĩ;ð‘Ĩ>0 [MDN] Mean dispersion 𝑑 Schrausser (2022a, p. 33). 𝑑=∑ |ð‘Ĩ𝑖−ð‘Ĩ| 𝑛𝑖=1 ∑1 𝑛𝑖=1 ;ð‘Ĩ𝑖≠ð‘Ĩ, ð›ŋ=𝜎⋅𝜁⋅√𝑛 𝑛−1=𝜎⋅𝜁 with 𝜎𝑑=𝜎⋅ 1 2⋅𝜁⋅√𝑛 where 𝜁𝑑=ð›ŋ=45 [NVTLG] [E01|F02|F03] Standard normal distribution 𝑓(ð‘Ĩ=𝑧) De Moivre (1738), Gauss (1809, 1823). 𝑓(𝑧)=𝜗= 1 √2⋅𝜋⋅e−𝑧2 2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 14 with ðđ(𝑧)=𝑝=âˆŦ 𝑓(𝑧)𝑑𝑧 𝑧 −∞ , 𝑓â€ē(𝑧)=∂𝑓(𝑧) ∂𝑧 [NVXY] [F01Z] Bivariate normal distribution 𝑓(𝑧1,𝑧2) 𝑓(𝑧1,𝑧2)=𝜗= 1 2⋅π⋅√1−𝑟2⋅e −1 2⋅(1−𝑟2)⋅(𝑧12−2⋅𝑟⋅𝑧1⋅𝑧2+𝑧22), ðđ(𝑧1,𝑧2)=1=∎ 𝑓 ∞ −∞ (𝑧1,𝑧2)𝑑𝑧1𝑑𝑧2 where 𝑟 = correlation 𝑟(𝑧1,𝑧2) [tVTLG|F06_] [F02|F06|F03Z] Student’s ð‘Ą-distribution 𝑓(ð‘Ĩ=ð‘Ą) LÞroth (1876), Pearson (1895), Gosset (1908). 𝑓(ð‘Ą)=𝜗=ð›Ī𝑑𝑓+1 2 ð›Ī𝑑𝑓 2⋅(𝑑𝑓⋅π)−12⋅(1+ð‘Ą2 𝑑𝑓)−𝑑𝑓+1 2 with ðđ(ð‘Ą,𝑑𝑓)=𝑝=âˆŦ 𝑓(ð‘Ą)ð‘‘ð‘Ą ð‘Ą −∞ where ð›Īð‘Ĩ=âˆŦ ð‘Ķð‘Ĩ−1⋅e−ð‘Ķ𝑑ð‘Ķ+𝑐 ∞ 0 [ch2VTLG|F07_] [F02|F07|F03Z] 𝜒2-distribution 𝑓(ð‘Ĩ=𝜒2) Helmert (1876), Pearson (1900b, 1914), Elderton (1902), Plackett (1983). 𝑓(𝜒2)=𝜗= 1 2𝑑𝑓 2⋅ð›Ī𝑑𝑓 2⋅𝜒2𝑑𝑓 2−1⋅e−𝜒2 2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 15 with ðđ(𝜒2,𝑑𝑓)=1−𝑝𝛞2=âˆŦ 𝑓(𝜒2)𝑑𝜒2 𝜒2 0 where ð›Īð‘Ĩ=âˆŦ ð‘Ķð‘Ĩ−1⋅e−ð‘Ķ𝑑ð‘Ķ+𝑐 ∞ 0 [FVTLG] [F02|F03Z] ðđ distribution 𝑓(ð‘Ĩ=ðđ) Fisher (1924), Snedecor (1934), ScheffÃĐ (1959). 𝑓(ðđ)=𝜗=ð›Ī𝑑𝑓1+𝑑𝑓2 2 ð›Ī𝑑𝑓1 2⋅ð›Ī𝑑𝑓2 2⋅(𝑑𝑓1 𝑑𝑓2)𝑑𝑓1 2⋅ðđ𝑑𝑓1 2−1⋅(1+𝑑𝑓1 𝑑𝑓2⋅ðđ)−𝑑𝑓1+𝑑𝑓2 2 with ðđ(ðđ,𝑑𝑓1,𝑑𝑓2)=1−𝑝𝛞2=âˆŦ𝑓(ðđ)𝑑ðđ ðđ 0 where ð›Īð‘Ĩ=âˆŦ ð‘Ķð‘Ĩ−1⋅e−ð‘Ķ𝑑ð‘Ķ+𝑐 ∞ 0 [Q02_] Third standardized moment, skewness 𝛞3 𝛞3=∑𝑧𝑖3𝑛𝑖=1 𝑛, 𝛞3=𝑛⋅∑ (ð‘Ĩ𝑖−ð‘Ĩ)3 𝑛𝑖=1 (𝑛−1)⋅(𝑛−2)⋅𝜎3 with 𝑧=𝛞3 √6𝑛 [Q02_] Fourth standardized moment, excess kurtosis 𝛞4 𝛞4=∑𝑧𝑖4𝑛𝑖=1 𝑛−3, Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 16 𝛞4=𝑛⋅(𝑛+1)⋅∑ (ð‘Ĩ𝑖−ð‘Ĩ)4 𝑛𝑖=1 −3⋅∑ (ð‘Ĩ𝑖−ð‘Ĩ)2 𝑛𝑖=1 ⋅∑ (ð‘Ĩ𝑖−ð‘Ĩ)2⋅(𝑛−1) 𝑛𝑖=1 (𝑛−1)⋅(𝑛−2)⋅(𝑛−3)⋅𝜎4 with 𝑧= 𝛞4 2⋅√6𝑛 [SMG] [CIx] Estimated standard error of mean 𝜎ð‘Ĩ, confidence interval ðķ𝐞𝑝 Neyman (1937) introduced the confidence interval into statistical hypothesis testing vs. Fisher’s null hypothesis testing, the Neyman–Pearson lemma (Neyman & Pearson, 1933; Lehmann, 1993). 𝜎ð‘Ĩ=√𝜎⋅ 𝑛 𝑛−1 𝑛 with ðķ𝐞𝑝=ð‘ĨÂąð‘Ą(1−1−𝑝 2,𝑛−1)⋅𝜎ð‘Ĩ, ðķ𝐞𝑝=ðœƒÂąð‘Ą(1−1−𝑝 2,𝑑𝑓)⋅𝜎𝜃 where 𝑝 = probability 𝑛 = number of cases [CIXY] [CIr] [E01] Standard error of prediction 𝜎ð‘Ķð‘Ĩ , confidence interval ðķ𝐞𝑝 𝜎ð‘Ķð‘Ĩ=𝜎ð‘Ķ⋅√1−𝑟2 with ðķ𝐞𝑝=ð‘Ķð‘ĨÂąð‘§(1−1−𝑝 2)⋅𝜎ð‘Ķð‘Ĩ where 𝑝 = probability 𝑟 = correlation ð‘Ķ = predicted value ð‘Ķ Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 17 [EPSILON] [EFG|EFR] [E01] Effect size 𝜖, Cohen’s 𝑑 Cohen (1977, 1988, p. 20, p. 49, 1992), Borenstein et al. (1997), Borenstein et al. (2001). 𝜖=𝑑=𝜇1−𝜇0 𝜎 , 𝑑ð‘Ģ=𝑑 √1−𝑟 with ð‘Ĩð‘ð‘Ÿð‘–ð‘Ą ð›―=𝜇1Âąð‘Ą(ð‘ð‘ð‘Ÿð‘–ð‘Ą,𝑑𝑓)⋅𝜎ð‘Ĩ, ð‘Ą(𝑑𝑓) 𝛞=ð‘Ĩ01−𝜇0 𝜎ð‘Ĩ, ð‘Ą(𝑑𝑓) ð›―=ð‘Ĩ01−𝜇1 𝜎ð‘Ĩ where 𝑑ð‘Ģ = 𝑑 for paired samples 𝑟 = correlation Power = 𝑝1âˆ’ð›―=1âˆ’ð‘ð›― [EPSILON2] Optimal effect size 𝜖𝑝 𝜖𝑝=√(2â‹…ð‘Ą(ð‘ð‘ð‘Ÿð‘–ð‘Ą,𝑑𝑓))2 𝑛, [EPSILON2] Optimal alpha level ð‘Ą(ð‘œð‘ð‘Ą,𝑑𝑓) 𝛞=√𝜖2⋅𝑛 2 [TKV] [tTKV|pTKV] Variance difference ð‘Ą-test For paired samples (ð‘Ĩ1|ð‘Ĩ2). 𝜃=𝑑𝜎2=𝜎12−𝜎22 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 18 with ð‘Ą(𝑑𝑓)=𝑑𝜎2⋅√𝑛−2 2⋅√𝜎12⋅𝜎22⋅(1−𝑟2) where 𝑑𝑓 = 𝑛−2 [TV_] [tTV|pTV] Paired 2-sample ð‘Ą-test 𝜃=ð‘Ĩ𝑑=∑ð‘Ĩ(𝑖,1)−ð‘Ĩ(𝑖,2) 𝑛𝑖=1 𝑛 with ð‘Ą(𝑑𝑓)=ð‘Ĩ𝑑 𝜎ð‘Ĩ𝑑, 𝜎ð‘Ĩ𝑑=√∑(ð‘Ĩ(𝑖,1)−ð‘Ĩ(𝑖,2))2 𝑛𝑖=1 −(∑ð‘Ĩ(𝑖,1)−ð‘Ĩ(𝑖,2) 𝑛𝑖=1 )2 𝑛 𝑛−1 ⋅1 √𝑛 where ð‘Ĩ𝑑 = mean of the differences of ð‘Ĩ1 and ð‘Ĩ2 values 𝑑𝑓 = 𝑛−1 [TU_] [tTU_|pTU_|tTUx|pTUx] Unpaired 2-sample ð‘Ą-test 𝜃=𝑑ð‘Ĩ=ð‘Ĩ1−ð‘Ĩ2 with ð‘Ą(𝑑𝑓)=𝑑ð‘Ĩ 𝜎𝑑ð‘Ĩ, 𝜎𝑑ð‘Ĩ=√∑(ð‘Ĩ(𝑖,1)−ð‘Ĩ1)2 𝑛1 𝑖=1 +∑(ð‘Ĩ(𝑖,2)−ð‘Ĩ2)2 𝑛2 𝑖=1 𝑛−2 ⋅√1 𝑛1+1 𝑛2 where 𝑑ð‘Ĩ = difference of the means ð‘Ĩ1 and ð‘Ĩ2 𝑑𝑓 = 𝑛−2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 19 [TT_] [tTT_|pTT_] One-sample ð‘Ą-test 𝜃=𝑑ð‘Ĩð‘Ķ=ð‘Ĩ−ð‘Ķ with ð‘Ą(𝑑𝑓)=𝑑ð‘Ĩð‘Ķ √𝜎2 𝑛−1 where 𝑑ð‘Ĩð‘Ķ = difference between sample mean ð‘Ĩ and test value ð‘Ķ 𝑑𝑓 = 𝑛−1 [ABT1] [x2F|p2F|zBN|pzBN] 𝜒2-test for independence 𝜒2=∑(𝑓𝑒𝑖−𝑓𝑏𝑖)2 𝑓𝑏𝑖 𝑛 𝑖=1 with 𝑧=𝑏−𝑏+𝑐 2 √𝑏+𝑐 4 [VFCH] [x4F|p4F|x4FY|p4FY|z4F|pz4F] 2 × 2 𝜒2-test for independence For Yates’s correction for continuity see Yates (1934). 𝜒2=𝑁⋅(𝑎⋅𝑑−𝑏⋅𝑐)2 (𝑎+𝑏)⋅(𝑐+𝑑)⋅(𝑎+𝑐)⋅(𝑏+𝑑), ðœ’ð‘Œð‘Žð‘Ąð‘’ð‘  2=𝑁⋅(|𝑎⋅𝑑−𝑏⋅𝑐|⋅𝑁2)2 (𝑎+𝑏)⋅(𝑐+𝑑)⋅(𝑎+𝑐)⋅(𝑏+𝑑);4<𝑓𝑒<7 with 𝑧= 𝑑−𝑁⋅𝑃𝑑 √𝑁⋅𝑃𝑑⋅(1−𝑃𝑑)−𝑁⋅(𝑁−1)⋅𝑃𝑑⋅(𝑃𝑑−𝑃𝑑) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 20 where 𝑓𝑒 = expected frequency 𝑃𝑑=(𝑑+𝑏)⋅(𝑐+𝑑) 𝑁2 𝑃𝑑=(𝑑+𝑏−1)⋅(𝑐+𝑑−1) (𝑁−1)2 𝑑𝑓=1 [VFCH] [xMN|pMN|xMNY|pMNY] McNemar’s 𝜒2-test for paired 2 × 2 contingency tables with dichotomous trait McNemar (1947). 𝜒2=(𝑏−𝑐)2 𝑏+𝑐 , 𝜒2=(|𝑏−𝑐|−12)2 𝑏+𝑐 ;20<(𝑏+𝑐)<30 Probability Since until the Renaissance a probable opinion was merely confirmed by an authority and hence there was no further concept of inductive evidence (see Hacking, 1975; Hald, 2003, p. 31), an objective representation of probability as such was first discussed by Antoine Arnauld and Pierre Nicole (1662, 1682, 1693; c.f. also Arnauld et al., 1970; van Evra, 1997; DessÃŽ & Albury, 1997 or Finocchiaro, 1997). The binomial distribution is primarily attributable to de Moivre (1711, 1718, 1738) and Jacob Bernoulli (1713), see also Schneider (2005a, b). Although not included as function, due to its considerability in this context, the configuration frequency analysis, CFA should be mentioned particularly (c.f. Krauth, 1973; Krauth & Lienert, 1993). An account of the systematics and logic of dependent probabilities within the framework of Bayes’ theorem (Bayes & Price, 1763; c.f. Stigler, 2018) can be found in Schrausser (2024c). The arguably most important methods regarding the calculation of probability parameters are implemented. [Ch|ChA|ChB] Arcsine transformation, Cohen’s ℎ Cohen (1988, p. 181). 𝜃=𝑝1−𝑝2, ℎ=2⋅sin−1√𝑝1−2⋅sin−1√𝑝2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 21 with 𝑝1=sin(2⋅sin−1√𝑝2+ℎ 2)2, 𝑝2=−sin(−2⋅sin−1√𝑝1+ℎ 2)2 where probabilities = 𝑝1, 𝑝2 [ABT1] [ADDP] [E01] Additive probability for independent events ð‘Ē𝑝(∊𝑛ðī) Corresponds to the geometric distribution 𝑓(𝑋â‰Ī𝑟|𝑝). ð‘Ē𝑝(∊𝑛ðī)=1−(1−𝑝ðī)𝑛 where 𝑛 = number of events ðī 𝑝ðī = probability of event ðī [GMVTLG] Geometric distribution 𝑓(𝑋â‰Ī𝑟|𝑝) Corresponds to the additive probability ð‘Ē𝑝(∊𝑛ðī). 𝑓(𝑋=𝑟|𝑝)=𝑃𝑛=𝑝⋅𝑞𝑟 with 𝑓(𝑋â‰Ī𝑟|𝑝)=𝑝𝑛=∑𝑝⋅𝑞𝑖 𝑟 𝑖=0 where 𝑝 = probability of event 𝑟+1=𝑛 = number of events [NBNMVTLG] [NBINOM] [E01] Negative binomial distribution 𝑓(𝑋â‰Ī𝑟|𝑟,𝑝) With 𝑘=1 it corresponds to the geometric distribution 𝑓(𝑋â‰Ī𝑟|𝑝) and the additive probability ð‘Ē𝑝(∊𝑛ðī). 𝑓(𝑋=𝑟|𝑟,𝑝)=𝑃𝑛=(𝑘+𝑟−1)! 𝑟!⋅(𝑘−1)!⋅𝑝𝑘⋅𝑞𝑟 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 22 with 𝑓(𝑋â‰Ī𝑟|𝑟,𝑝)=𝑝𝑛=∑(𝑘+𝑖−1)! 𝑖!⋅(𝑘−1)!⋅𝑝𝑘⋅𝑞𝑖 𝑟 𝑖=0 where 𝑟+𝑘=𝑛 = number of events 𝑘 = number of successes [ABT1] [BINOM|zBN|pzBN] [E01] Exact binomial test 𝑓(𝑋=𝑏|𝑏,𝑐)=𝑃0=(𝑏+𝑐)! 𝑏!⋅𝑐! ⋅2−𝑏⋅2−𝑐 with 𝑓(𝑋â‰Ī𝑏|𝑏,𝑐)=𝑝=𝑝𝑒ð‘Ĩð‘Žð‘ð‘Ą1=∑ (𝑏+𝑐)! 𝑖!⋅(𝑏+𝑐−𝑖)! 𝑏 𝑖=0 ⋅2−𝑖⋅2−(𝑏+𝑐−𝑖);𝑝â‰Ī12, 𝑝𝑒ð‘Ĩð‘Žð‘ð‘Ą1=(1−𝑝)+𝑃0;𝑝>12 also 𝑧=𝑏−𝑏+𝑐 2 √𝑏+𝑐 4 [FX_] [z4F|pz4F] Exact hypergeometric 2 × 2 test Fisher Exact test (Fisher, 1922; Agresti, 1992). 𝑓(𝑋=𝑎|𝑎,𝑏,𝑐,𝑑)=𝑃0=(𝑎+𝑏)!⋅(𝑐+𝑑)!⋅(𝑎+𝑐)!⋅(𝑏+𝑑)! 𝑁!⋅𝑎!⋅𝑏!⋅𝑐!⋅𝑑! with 𝑓(𝑋â‰Ī𝑎|𝑎,𝑏,𝑐,𝑑)=𝑝𝑒ð‘Ĩð‘Žð‘ð‘Ą1=∑𝑃 𝑎 𝑖=1 𝑖;𝑝â‰Ī12, 𝑓(𝑋â‰Ĩ𝑎|𝑎,𝑏,𝑐,𝑑)=𝑝𝑒ð‘Ĩð‘Žð‘ð‘Ą1=∑𝑃 𝑛 𝑖=𝑎 𝑖;𝑝>12 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 29 zrbis ZBS Rank biserial prbisR KBR rbisR ZBR U_1 U_2 zrbisR Phi PHC PHI KPH pPHI KPM xPHI XKP Tetrachoric PHC prtet KTET rtet STET srtet ZTET Partial PKR prxy_z PKR rxy_z ZKR ry_xz zrxy_z Fisher Z ZCor rZ FZR Zr RFZ SFZ Z difference, Cohen's q prr ZRR Zrr Averaged Fisher Z mZ mr GFZ mZ Multiple, Cohen's fÂē MCORR2 Cf2 MKR FMCORR SKM MCORR BMK pMCORR MBC SCR FMK Exposure Value Ev AEv E03 Ev E02 TEv Aperture Av for time Tv AvTv Aperture Av for speed S AvS E03 Aperture Av shift from time Tv AvTvk Aperture Av shift from speed S AvSk Integration Circular pi F01 F05 Spherical pi F01Z Gamma F01Z GAMMA F04 Distribution Standardizing Q01_ zVAL Z__ zVALp Z_P ZWERT Z_W Quantity proportion npz Weighted arithmetic mean AMG AMG Geometric mean AMG GM_ GM_ Q01_ Harmonic mean AMG HM_ HM_ Q01_ Coefficient of variation Q01_ SDV Mean dispersion MDN D__ Standard normal distribution NVTLG F02 DZW F03 PZD Bivariate normal distribution NVXY F01Z Student's t F06_ F02 DTW tVTLG F03Z PTD F06 ChiÂē ch2VTLG F02 DXW F07_ F03Z PXD F07 F FVTLG F02 DFW Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 30 F03Z PFD Third standardized moment Q02_ SCH SHP SSH ZSH Fourth standardized moment Q02_ EXZ EZP SEX ZEX Estimated standard error of mean SMG CIx SMG SMX Standard error of prediction CIXY CIr E01 Effect size, Cohen's d EPSILON EFG E01 EFG EFR Optimal effect size EPSILON2 EFS Optimal alpha level EPSILON2 OPP Variance difference TKV pTKV TKV tTKV Paired 2-sample t-test TV_ pTV TV_ tTV Unpaired 2-sample t-test TU_ pTU_ TU_ pTUx TUX tTU_ tTUx One-sample t-test TT_ pTT_ TT_ tTT_ ChiÂē-test for independence ABT1 p2F EDX pzBN ZBN x2F zBN 2 × 2 chiÂē-test for independence VFCH p4F VFX p4FY VFYX pz4F ZFX x4F x4FY z4F McNemar's chiÂē-test VFCH pMN MNX pMNY MNYX xMN xMNY Probability Arcsine transformation, Cohen's h ch chA chB Additive probability for independent events ABT1 ADDP E01 AWN Geometric GMVTLG GMP GMW Negative binomial NBNMVTLG NBINOM E01 NBP NBW Exact binomial test ABT1 BINOM E01 BN0 pzBN BN1 zBN BN2 Exact hypergeometric 2 × 2 test FX_ pz4F FX0 z4F FX1 FX2 Combinatorics Permutation matrix PRM2 P_M PM_ Permutation matrix to class m PRM3 nk ; P2M PMM PMW Variation matrix PRM4 VRW VWM Variation matrix, dependent 2 sample design PRM5 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 31 Resampling Permutation test P for 2 paired samples PV_ Randomized permutation test mP for 2 paired samples mPV_ Permutation test P for 2 independent samples PU_ Randomized permutation test mP for 2 independent samples mPU_ Bootstrap test Bt for 2 independent samples BtU_ Complex plane Argand diagram CPLX F02Z CPLX2 Complex function CPLHX Source codes CAS functions To create or edit: To select, insert and execute: A ABT1.pas //ABT1()/D.G.SCHRAUSSER/2022 //Binomialp[A1:bA2:c]/Addp[C1:pC2:nC3:k] #cas ABT1():= BEGIN STARTAPP("Arbeitsblatt"); STARTVIEW(1) 5â–ļA1; 5â–ļA2; =A1+A2â–ļA3; 0.5â–ļB1; =BINOMIAL_CDF(A3,B1,A1)â–ļB4 0.5â–ļC1; 8â–ļC2; 1â–ļC5; =1-(1-C1)^C2â–ļD4; =ÎĢ((C5+I-1)!/(I!*(C5-1)!)*C1^C5*(1-C1)^I,I,0,C2-C5)â–ļD6; END; #end // ABT2.pas //ABT2(cell count a,b,c,d)/D.G.SCHRAUSSER/2025 //2×2 chi-squared test for independence //Observed frequencies abcd fb //Expected frequencies fe //Probabilities p(A^B), p(B|A), p(A|B) //Chi-squared with 2-tailed p //e.g.ABT2(17,12,14,24)[Spreadsheet] #cas ABT2(a,b,c,d):= BEGIN STARTAPP("Arbeitsblatt"); STARTVIEW(1) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 32 "A1"â–ļB1 "A2"â–ļC1 "A1"â–ļE1 "A2"â–ļF1 "B1"â–ļA2 "B2"â–ļA3 "B1"â–ļA5 "B2"â–ļA6 "B1"â–ļA8 "B2"â–ļA9 "B1"â–ļA11 "B2"â–ļA12 aâ–ļB2 bâ–ļC2 câ–ļB3 dâ–ļC3 D2:==B2+C2 D3:==B3+C3 B4:==B2+B3 C4:==C2+C3 D4:==B2+C2+B3+C3 E5:==B2/D4 F5:==C2/D4 g5:==D2/D4 E6:==B3/D4 F6:==C3/D4 g6:==D3/D4 E7:==B4/D4 F7:==C4/D4 g7:==g5+g6 E2:==B2/D2 F2:==C2/D2 g2:==E2+F2 E3:==B3/D3 F3:==C3/D3 g3:==E3+F3 B5:==B2/B4 B6:==B3/B4 B7:==B5+B6 C5:==C2/C4 C6:==C3/C4 C7:==C5+C6 B8:==E7*D2 B9:==E7*D3 C8:==F7*D2 C9:==F7*D3 D13:==D4*(B2*C3-C2*B3)^2/((B2+C2)*(B3+C3)*(B2+B3)*(C2+C3)) B11:==(B2-B8)^2/B8 B12:==(B3-B9)^2/B9 C11:==(C2-C8)^2/C8 C12:==(C3-C9)^2/C9 D11:==((B2-B8)^2/B8)+(C2-C8)^2/C8 D12:==((B3-B9)^2/B9)+(C3-C9)^2/C9 B13:==B11+B12 C13:==C11+C12 g11:==1-(CHISQUARE_CDF(1,D11)) g12:==1-(CHISQUARE_CDF(1,D12)) g13:==1-(CHISQUARE_CDF(1,D13)) E11:==1-(CHISQUARE_CDF(1,B11)) E12:==1-(CHISQUARE_CDF(1,B12)) E13:==1-(CHISQUARE_CDF(1,B13)) F11:==1-(CHISQUARE_CDF(1,C11)) F12:==1-(CHISQUARE_CDF(1,C12)) F13:==1-(CHISQUARE_CDF(1,C13)) END; Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 33 #end // AMG.pas //AMG()/D.G.SCHRAUSSER/2025 //Weighted arithmetic, geometric and harmonic mean #cas AMG():= BEGIN //L1()(2) provided size(L1)â–ļL3(1) mean(L1)(1)â–ļL3(2) MAKELIST(L1(x)(1)*L1(x)(2),x,1,L3(1))â–ļL2 ÎĢLIST(L1)(2)â–ļL3(3) ÎĢ(L2)/L3(3)â–ļL3(4) L3(3) NTHROOT (product((L1(x)(1))^L1(x)(2),x,1,L3(1)))â–ļL3(5) L3(3)/(ÎĢ(L1(x)(2)/L1(x)(1),x,1,L3(1)))â–ļL3(6) //n,AM,sumni,GAM,GGM,GHM L3 END; #end // B BNMVTLG.pas //BNMVTLG(p[e],a=k,n)/D.G.SCHRAUSSER/2025 //e.g.BNMVTLG(0.5,5,10) #cas BNMVTLG(P,K,N):= BEGIN B=0; FOR I FROM 0 TO K DO BINOMIAL(N,P,I)â–ļL4(I) B=B+L4(I) END; D5=L4;L4={} FOR I FROM 0 TO N DO BINOMIAL(N,P,I)â–ļL5(I); END; D6=L5;L5={}; STARTAPP("Statistiken_1_Var"); STARTVIEW(1); "D5"â–ļH1(1);5â–ļH1(3); "D6"â–ļH2(1);5â–ļH2(3); //p RETURN(B); END; #end // BtU_.pas //BtU_(simulation cycles B)/D.G.SCHRAUSSER/2025 //Bootstrap method, Bt //2 independent samples (x|g) //e.g.BtU_(1000) #cas BtU_(B):= BEGIN //L1L2 provided {}â–ļL3;{}â–ļL4 {}â–ļL5;{}â–ļL6 0â–ļM11 0â–ļM12 0â–ļM2 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 34 SIZE(L1)â–ļN1 SIZE(L2)â–ļN2 N=N1+N2 ABS(mean(L1)-mean(L2))â–ļQ02 ÎĢLIST(L1)â–ļQ011 ÎĢLIST(L2)â–ļQ012 // CONCAT(L1,L2)â–ļL9 MSGBOX("BtU") FOR J FROM 1 TO B DO // FOR A FROM 1 TO N DO L9(RANDINT(N))â–ļL0(A) END; FOR A FROM 1 TO N1 DO L0(A)â–ļL3(A) END; FOR A FROM 1 TO N2 DO L0(N1+A)â–ļL4(A) END; ABS(mean(L3)-mean(L4))â–ļQJ2 ÎĢLIST(L3)â–ļQJ11 ÎĢLIST(L4)â–ļQJ12 IF QJ11â‰ĨQ011 THEN M11=M11+1 END; IF QJ12â‰ĨQ012 THEN M12=M12+1 END; IF QJ2â‰ĨQ02 THEN M2=M2+1 END; QJ11â–ļL5(J) QJ2â–ļL6(J) END; // SORT(L5)â–ļL5 SORT(L6)â–ļL6 {}â–ļL9 {}â–ļL0 N1,N2,[Q011,Q012,Q02],M11/B,M12/B,M2/B END; #end // C ch2VTLG.pas //ch2VTLG(chi-squared,df)/D.G.SCHRAUSSER/2025 //e.g.ch2VTLG(2.65,1)[AdvancedGraphing] #cas ch2VTLG(C2569,A7485):= BEGIN G=Gamma(A7485/2) //P=âˆŦ((1/(2^(A7485/2)*G))*X^((A7485/2)-1)*e^(-X/2),X,0,C) P=CHISQUARE_CDF(A7485,C2569) A7485â–ļA C2569â–ļC "Y=(1/(2^(A/2)*G))*X^((A/2)-1)*e^(-X/2)"â–ļV1 "Y<(1/(2^(A/2)*G))*X^((A/2)-1)*e^(-X/2) AND Y>0 AND X<C AND X>0"â–ļV2 STARTAPP("Erweiterte_Grafiken"); STARTVIEW(1); [1-P] END; #end // CIXY.pas //CIXY(x,y'CI)/D.G.SCHRAUSSER/2022 //Standard error of prediction sy'x, CI //e.g.CIXY(3,0.99[ZWERT,Statistics_2_Var,Spreadsheet,AdvancedGraphing] #cas CIXY(X,C):= BEGIN Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 35 //C1C2 provided STARTAPP("Statistiken_2_Var"); STARTVIEW(−6) A=Corr B=sY D=MeanY PredY(X)â–ļL3(2); √(1-A^2)*B*NORMALD_ICDF(1-((1-C)/2))â–ļL3(4) L3(4)+L3(2)â–ļL3(3) L3(2)-L3(4)â–ļL3(1) Câ–ļL3(5) ZWERT(L3(1),D,B)â–ļL4(1) ZWERT(L3(2),D,B)â–ļL4(2) ZWERT(L3(3),D,B)â–ļL4(3) L4(1)â–ļU L4(3)â–ļO ZWERT(X,MeanX,sX)â–ļQ STARTAPP("Arbeitsblatt"); "Å·-"â–ļA1;L3(1)â–ļB1;L4(1)â–ļC1 "Å·"â–ļA2;L3(2)â–ļB2;L4(2)â–ļC2 "Å·+"â–ļA3;L3(3)â–ļB3;L4(3)â–ļC3 "Âą"â–ļA4;L3(4)â–ļB4;L3(4)/Bâ–ļC4 "CI"â–ļA5;L3(5)â–ļB5; STARTAPP("Erweiterte_Grafiken"); STARTVIEW(1) "Y=A*X"â–ļV3 "Y>0 AND (Y<A^(-1)*X AND Y>A*X) OR Y<0 AND (Y>A^(-1)*X AND Y<A*X)"â–ļV4 "Y=√(1-X^2)"â–ļV5 "Y=-1*√(1-X^2)"â–ļV6 "X<A AND X>0 AND Y<A AND Y>0"â–ļV7 CAS((X,Y)->((Y<O) AND (Y>U)) AND ((X==Q))â–ļV0) //y'-,y',y'+,CI,sy'x,CIp RETURN(L3); END; #end // CPLHX.pas //CPLHX(complex number,a+bi)/D.G.SCHRAUSSER/2022 //e.g.CPLHX(2+i/2),[AdvancedGraphing] #cas CPLHX(C):= BEGIN Câ–ļZ1 RE(Z1)â–ļR IM(Z1)â–ļI Z1â–ļL1(1) ABS(Z1)â–ļL1(2) ARG(Z1)â–ļL1(3) "Y=R*X"â–ļV1 "Y=I"â–ļV2 "Y=√((R*X)^2+I^2)"â–ļV3 "Y=(I/ABS(I))*(π/2)-ATAN((R/I)*X)"â–ļV4 STARTAPP("Erweiterte_Grafiken") STARTVIEW(1) RETURN(L1); END; #end // CPLX.pas //CPLX(complex number,a+bi)/D.G.SCHRAUSSER/2022 //e.g.CPLX(2+i/2),[AdvancedGraphing] #cas CPLX(C):= Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 36 BEGIN Câ–ļZ1 RE(Z1)â–ļR IM(Z1)â–ļI ABS(Z1)â–ļL1(1) Râ–ļXâ–ļJ Iâ–ļK "Y=√(1-X^2)"â–ļV5 "Y=-1*√(1-X^2)"â–ļV6 "Y=I"â–ļV7 "X=R"â–ļV8 "X<R AND X>0 AND Y>0 AND Yâ‰Ī0.01"â–ļV0 "Y=(I/R)*X AND Y>0 AND X<R"â–ļV9 IF I<0 THEN "Y=(I/R)*X AND Y<0 AND X<R"â–ļV9 END; IF R<0 THEN "Y=(I/R)*X AND Y>0 AND X>R"â–ļV9 END; IF I<0 AND R<0 THEN "Y=(I/R)*X AND Y<0 AND X>R"â–ļV9 END; ARG(Z1)â–ļL1(2) CONVERT(L1(2)_rad,1_deg)â–ļL1(3) RETURN(L1); END; #end // CPLX.pas //CPLX2(complex number,a+bi)/D.G.SCHRAUSSER/2022 //e.g.CPLX2(2+i/2),[CPLX,Spreadsheet]// #cas CPLX2(C):= BEGIN CPLX(C) STARTAPP("Arbeitsblatt"); "z"â–ļA1;Z1â–ļB1 "|z|"â–ļA2;L1(1)â–ļB2 "âˆĄÏ€"â–ļA3;L1(2)â–ļB3 "∥°"â–ļA4;L1(3)â–ļB4 END; #end // D DELTA2.pas //DELTA2()/D.G.SCHRAUSSER/2025 //Somers' D for binary values [0,1] #cas DELTA2():= BEGIN SIZE(L1)â–ļN {}â–ļL3 0â–ļX01 0â–ļX02 FOR I FROM 1 TO N DO IF L1(I)=1 AND L2(I)=1 THEN X01=X01+1 END; IF L1(I)=1 AND L2(I)=0 THEN X02=X02+1 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 37 END; END; X01/Nâ–ļL3(1) X02/Nâ–ļL3(2) L3(1)-L3(2)â–ļL3(3) //pA,pB,D approx(L3) END; #end // E EPSILON.pas //EPSILON(x1,m1,m2,s12,d)/D.G.SCHRAUSSER/2022 //e.g.EPSILON(106,100,110,15,25) #cas EPSILON(X,M,N,S,D):= BEGIN G=Gamma((D+1)/2)/Gamma(D/2) E=(N-M)/S P=1-âˆŦ(G*(D*π)^(-1/2)*(1+(X^2/D))^(-(D+1)/2),X,−∞,E) T=(((N+M)/2)-M)/S H=1-âˆŦ(G*(D*π)^(-1/2)*(1+(X^2/D))^(-(D+1)/2),X,−∞,T) Q=(X-M)/S R=1-âˆŦ(G*(D*π)^(-1/2)*(1+(X^2/D))^(-(D+1)/2),X,−∞,Q) U=âˆŦ(G*(D*π)^(-1/2)*(1+(X^2/D))^(-(D+1)/2),X,−∞,(X-N)/S) B=1-U "X>T AND Xâ‰ĪT"â–ļV1 "X>Q AND Xâ‰ĪQ"â–ļV2 // Dâ–ļK "X>0 AND X<E AND Y<0 AND Y>-0.01"â–ļV3 "Y<(G*(K*π)^(-1/2)*(1+((E-X)^2/K))^(-(K+1)/2)) AND Y>0 AND X>Q"â–ļV6 "Y<(G*(K*π)^(-1/2)*(1+((E-X)^2/K))^(-(K+1)/2)) AND Y>0 AND X<Q"â–ļV7 "Y=(G*(K*π)^(-1/2)*(1+((E-X)^2/K))^(-(K+1)/2))"â–ļV8 "Y=(G*(K*π)^(-1/2)*(1+((X)^2/K))^(-(K+1)/2))"â–ļV0 "Y<(G*(K*π)^(-1/2)*(1+((X)^2/K))^(-(K+1)/2)) AND Y>0 AND X>Q"â–ļV9 Eâ–ļL2(1);Pâ–ļL3(1);Nâ–ļL1(1) Tâ–ļL2(2);Hâ–ļL3(2);T*S+Mâ–ļL1(2) Qâ–ļL2(3);Râ–ļL3(3);Q*S+Mâ–ļL1(3) Uâ–ļL4(3);Bâ–ļL5(3) STARTAPP("Arbeitsblatt"); "Îĩ"â–ļA1;L2(3)â–ļB1;L2(2)â–ļC1;L2(1)â–ļD1; "x"â–ļA2;L1(3)â–ļB2;L1(2)â–ļC2;L1(1)â–ļD2; "Îą"â–ļA3;L3(3)â–ļB3;L3(2)â–ļC3;L3(1)â–ļD3; "Îē"â–ļA4;L4(3)â–ļB4; "1-Îē"â–ļA5;L5(3)â–ļB5; STARTAPP("Erweiterte_Grafiken") STARTVIEW(1) RETURN(L2(1),L3(3),L4(3)); END; #end // EPSILON2.pas //EPSILON2(epsilon,n,df,pcrit)/D.G.SCHRAUSSER/2022 //e.g.EPSILON2(0.38,100,99,0.95) //optimal effect size epsilon //optimal alpha t //1-p(alpha opt t) #cas EPSILON2(E,N,D,K):= BEGIN Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 38 #t opt niv V=√(E^2*N)/2â–ļL6(2) P=1-STUDENT_CDF(D,V)â–ļL6(3) #e opt eff stke L=√((2*STUDENT_ICDF(D,K))^2/N)â–ļL6(1) "X>0 AND X<L AND Y<0 AND Y>-0.02"â–ļV1 RETURN(L6); END; #end // EPSOLON3.pas //EPSILON3(100,110,15,25,0.99) #cas EPSILON3(M,N,S,D,K):= BEGIN F=STUDENT_ICDF(D,K) M+S*Fâ–ļL7(1) N-S*Fâ–ļL7(2) "X>E-F AND Xâ‰ĪE-F AND Y>0 AND Y<(G*(K*π)^(-1/2)*(1+((E-X)^2/K))^(-(K+1)/2))" â–ļV4 "X>F AND Xâ‰ĪF AND Y>0 AND Y<(G*(K*π)^(-1/2)*(1+((X)^2/K))^(-(K+1)/2))"â–ļV5 RETURN(L7); END; #end // F FVTLG.pas //FVTLG(F,df1,df2)/D.G.SCHRAUSSER/2025 //e.g.FVTLG(2.8,10,5) #cas FVTLG(F,A,B):= BEGIN Fâ–ļX CAS(Gamma((A+B)/2))â–ļH CAS(Gamma(A/2))â–ļD CAS(Gamma(B/2))â–ļE CAS(H/(D*E))â–ļC CAS((X,Y)->Y=C*((A/B)^(A/2)*X^((A/2)-1)*(1+(A/B)*X)^(−(((A+B)/2)))) AND X>0 â–ļV2) CAS((X,Y)->Y<C*((A/B)^(A/2)*X^((A/2)-1)*(1+(A/B)*X)^(−(((A+B)/2)))) AND Y>0 AND X<F AND X>0â–ļV1) FISHER_CDF(A,B,X)â–ļP STARTAPP("Erweiterte_Grafiken") STARTVIEW(1) P,[1-P] END; #end // FX.pas //FX_(cell count a,b,c,d)/D.G.SCHRAUSSER/2025 //e.g.FX_(1,2,3,1) //Exact hypergeometric 4-field test according to R. A. Fisher //(Fisher Exact Test): Hypergeometric probability p to cell a of the 4-field initial arrangement for all possible arrangements a //Exact significance levels p[exact1], p[exact2] #cas FX_(a,b,c,d):= BEGIN {}â–ļL1 1â–ļS Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 45 // N1,N2,[Q011,Q012,Q02],M11/M,M12/M,M2/M END; #end // mPU2.pas //mPU2(simulation cycles M)/D.G.SCHRAUSSER/2025 //Permutation test in the random sampling model, //randomized permutation, p-value not randomized, mP //2 independent samples (x|g) //e.g.mPU2(100) #cas mPU2(M):= BEGIN //L1L2 provided // {}â–ļL3;{}â–ļL4 {}â–ļL5;{}â–ļL6 0â–ļM11 0â–ļM12 0â–ļM02 0â–ļX 0â–ļJ SIZE(L1)â–ļN1 SIZE(L2)â–ļN2 N=N1+N2 // MAKELIST(x+1,x,0,N-1)â–ļL7 MAKELIST(0,x,0,1)â–ļL51 MAKELIST(x+1,x,0,N1-1)â–ļL71 // ABS(mean(L1)-mean(L2))â–ļQ02 ÎĢLIST(L1)â–ļQ011 ÎĢLIST(L2)â–ļQ012 // MSGBOX("mPU") WHILE J<M DO // FOR A FROM 1 TO N DO {RANDOM(),L7(A)}â–ļL0(A) END; //diff {}â–ļL9 SORT(L0)â–ļL31 // FOR A FROM 1 TO N1 DO L31(A)â–ļL51;L51(2)â–ļL6(A) END; sort(L0)â–ļL9 FOR A FROM 1 TO N DO L9(A)â–ļL8;L8(2)â–ļL9(A) END; {}â–ļL8 L9==L7â–ļV // IF DIFFERENCE(L6,L71)≠{} AND V=0 THEN J+1â–ļJ CONCAT(L1,L2)â–ļL8 FOR A FROM 1 TO N1 DO L8(L9(A))â–ļL3(A) END; FOR A FROM 1 TO N2 DO L8(L9(N1+A))â–ļL4(A) END; ABS(mean(L3)-mean(L4))â–ļQJ2 ÎĢLIST(L3)â–ļQJ11 ÎĢLIST(L4)â–ļQJ12 IF QJ11â‰ĨQ011 THEN M11=M11+1 END; Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 46 IF QJ12â‰ĨQ012 THEN M12=M12+1 END; IF QJ2â‰ĨQ02 THEN M02=M02+1 END; // QJ11â–ļL5(J) QJ2â–ļL6(J) // ELSE X+1â–ļX END; //IF diff END; //M // SORT(L5)â–ļL5 SORT(L6)â–ļL6 {}â–ļL8 {}â–ļL0 // X,J,N1,N2,[Q011,Q012,Q02],M11/M,M12/M,[M02/M] END; #end // mPV_.pas //mPV_(simulation cycles M)/D.G.SCHRAUSSER/2025 //Permutation test in the random sampling model, //randomized permutation, p-value not randomized, mP //2 paired samples (x1|x2) //e.g.mPV_(100) #cas mPV_(M):= BEGIN //L1()(2) provided 0â–ļM11;0â–ļM12;0â–ļM2 SIZE(L1)(1)â–ļN //sum,Q0 ÎĢLIST(L1)â–ļL2;ÎĢLIST(L2.^2)â–ļQ02 L2(1)â–ļQ011;L2(2)â–ļQ012 // MSGBOX("mPV") FOR A FROM 1 TO M DO //mP // FOR I FROM 1 TO N DO RANDINT(0,1)â–ļL8(I) END; FOR J FROM 1 TO N DO IF L8(J)=1 THEN REVERSE(L1(J))â–ļL3(J) ELSE L1(J)â–ļL3(J) END; END; //J ÎĢLIST(L3)â–ļL4;ÎĢLIST(L4.^2)â–ļQ2 L4(1)â–ļQ11;L4(2)â–ļQ12 Q11â–ļL5(A) Q12â–ļL6(A) Q2â–ļL7(A) IF Q11â‰ĨQ011 THEN M11=M11+1 END; IF Q12â‰ĨQ012 THEN M12=M12+1 END; IF Q2â‰ĨQ02 THEN M2=M2+1 END; // END;//mP // SORT(L5)â–ļL5;SORT(L6)â–ļL6;SORT(L7)â–ļL7 //n,Q011,Q012,Q02,p11,p12,p2 N,[Q011,Q012,Q02],M11/M,M12/M,M2/M END; #end // mZ.pas //mZ()/D.G.SCHRAUSSER/2025 //Averaged Fisher-Z, mean r/[ZCor] Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 47 #cas mZ():= BEGIN //L1(N)(2) provided size(L1)â–ļN FOR I FROM 1 TO N DO ZCor(L1(I,1),L1(I,2))(1)â–ļL3 L3(1)â–ļL2(I) END; ÎĢ((L1(I,2)-3)*L2(I),I,1,N)/ÎĢ(L1(I,2)-3,I,1,N)â–ļL3(1) L3(2):=(e^(2*L3(1))-1)/(e^(2*L3(1))+1) //n,_Z,_r N,L3 END; // N NBNMVTLG.pas //NBNMVTLG(k,p[e],n,m)/D.G.SCHRAUSSER/2025 //e.g.NBNMVTLG(1,0.2,8,10) #cas NBNMVTLG(K,P,N,M):= BEGIN B=0 FOR I FROM 0 TO N-K DO ((K+I-1)!/(I!*(K-1)!))*P^K*(1-P)^Iâ–ļL4(I+1) B=B+L4(I+1) END D7=L4;L4={} FOR I FROM 0 TO M-1 DO ((K+I-1)!/(I!*(K-1)!))*P^K*(1-P)^Iâ–ļL5(I+1) END D8=L5;L5={}; STARTAPP("Statistiken_1_Var"); STARTVIEW(1); "D7"â–ļH3(1);5â–ļH3(3); "D8"â–ļH4(1);5â–ļH4(3); //p RETURN(B); END; #end // NVTLG.pas //NVTLG(z[crit],tail[1/2])/D.G.SCHRAUSSER/2025 //e.g.NVTLG(1.96,2) #cas NVTLG(Z,S):= BEGIN "Y=(1/√(2*π))*e^((-1/2)*(X)^2)"â–ļV9 "Y<(1/√(2*π))*e^((-1/2)*(X)^2) AND Y>0 AND X<C"â–ļV8 NORMALD_CDF(Z)â–ļP; IF S=2 THEN "Y<(1/√(2*π))*e^((-1/2)*(X)^2) AND Y>0 AND X<C AND X>−C"â–ļV8 P=P-(1-P); END; C=Z; STARTAPP("Erweiterte_Grafiken"); STARTVIEW(1); //p-value RETURN(P); END; #end // Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 48 P pCor.pas //pCor(correlation r,n)/D.G.SCHRAUSSER/2025 //e.g.pCor(0.94,4) #cas pCor(r,n):= BEGIN t=(r*√(n-2))/√(1-r^2) p=STUDENT_CDF(n-2,t) 2*pâ–ļp2 IF p>0.5 THEN 2*(1-p)â–ļp2 END; //t-value, p-value, p2 [t],p,[p2] END; #end // PHC.pas //PHC(cell count a,b,c,d)/D.G.SCHRAUSSER/2025 //e.g.PHC(17,12,14,24) //Phiand tetrachoric correlation #cas PHC(a,b,c,d):= BEGIN a+b+c+dâ–ļN a+bâ–ļz1;c+dâ–ļz2 a+câ–ļs1;b+dâ–ļs2 z1/z2â–ļZ01;s1/s2â–ļS01 IF z2<z1 THEN Z01=1/Z01 END; IF s2<s1 THEN S01=1/S01 END; VFX=(N*(a*d-b*c)^2)/((a+b)*(c+d)*(a+c)*(b+d)) VFC=1-CHISQUARE_CDF(1,VFX) KPH=(a*d-b*c)/sqrt(((a+b)*(c+d)*(a+c)*(b+d))) KPM=sqrt(Z01*S01) KTET=cos(π/(1.+√(b*c/(a*d)))) STET=sqrt((((((a+b)/N))*(((a+c)/N))*(((c+d)/N))*(((b+d)/N))/N))) STET=STET*(1/(((1/(sqrt(2*π)))*e^(-normald_icdf(((c+d)/N))^2/2))*((1/(sqrt(2 *π)))*e^(-normald_icdf(((b+d)/N))^2/2)))) ZTET=KTET/STET P=normald_cdf(ZTET) IF P>0.5 THEN P=1-P END; P*2â–ļP2 //n,TET,z,p1,p2,phi,phimax,chi2,p2 N,[KTET],[ZTET],P,[P2],[KPH,KPM],[VFX],[VFC] END; #end // PKR.pas //PKR()/D.G.SCHRAUSSER/2025 //Partial corr rxy.z[pCor,ZCor] #cas PKR():= BEGIN //L1L2L3 provided size(L1)â–ļN df=N-2 //rxy FOR I FROM 1 TO N DO L1(I)â–ļL5(1) L2(I)â–ļL5(2) L5â–ļL4(I) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 49 END; approx(correlation(L4))â–ļr0 pCor(r0,N)(3)â–ļpr0 //rxz FOR I FROM 1 TO N DO L1(I)â–ļL5(1) L3(I)â–ļL5(2) L5â–ļL4(I) END; approx(correlation(L4))â–ļr1 pCor(r1,N)(3)â–ļpr1 //ryz FOR I FROM 1 TO N DO L2(I)â–ļL5(1) L3(I)â–ļL5(2) L5â–ļL4(I) END; approx(correlation(L4))â–ļr2 pCor(r2,N)(3)â–ļpr2 //rxy.z rp=(r0-r1*r2) rp=rp/(sqrt(1-r1^2)*sqrt(1-r2^2)) rp=approx(rp) ZCor(rp,N)(1)â–ļL0 L0(1)*SQRT(N-2)â–ļzrp prp=NORMALD_CDF(zrp) IF prp>0.5 THEN prp=1-prp END; prp2=2*prp //pCor(rp,N)(3)â–ļp //df,rxy,p2,rxz,p2,ryz,p2,rxy.z,p2 df,[r0,pr0],[r1,pr1],[r2,pr2],[rp,prp] END; #end // PRM1.pas //PRM1(n perm)/D.G.SCHRAUSSER/2025 //e.g.PRM1(5)/permutation vector (p)n from L1 #cas PRM1(N):= BEGIN //L1(N) provided {}â–ļL0 {}â–ļL2 FOR A FROM 1 TO N DO {RANDOM(),L1(A)}â–ļL0(A) END; // sort(L0)â–ļL2 FOR A FROM 1 TO N DO L2(A)â–ļL8;L8(2)â–ļL2(A) END; // L2 END; #end // PRM2.pas //PRM2(elements n)/D.G.SCHRAUSSER/2025 //Complete permutation matrix (P)n of elements n to 1 class, //where P=n! //e.g.PRM2(3) #cas PRM2(n):= BEGIN MAKELIST(1,P,1,n+1)â–ļL1 P=PERM(n,n) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 50 0â–ļL1(1) {}â–ļL2 0â–ļM1 1â–ļJ 0â–ļI 0â–ļSW // WHILE I≠n AND L1(I)â‰Īn DO FOR I FROM 1 TO n DO IF I=1 THEN L1(1)+1â–ļL1(1) END; IF I=n AND L1(I)>n THEN BREAK END; IF L1(I)>n THEN 1â–ļL1(I);L1(I+1)+1â–ļL1(I+1) END; END;//I // FOR K FROM 1 TO n DO FOR L FROM K+1 TO n DO IF L1(K)=L1(L) THEN 1â–ļSW BREAK; END; END; END; IF SW=0 THEN SUPPRESS(L1,n+1)â–ļL2(J);J+1â–ļJ END; 0â–ļSW END;//while // L2â–ļM1 IF n=2 THEN M1=[[1,2],[2,1]] M1â–ļL2 END; // P,M1 END; #end // PRM3.pas //PRM3(elements n, class m)/D.G.SCHRAUSSER/2025 //Complete permutation matrix w(P)n(km,kn-m) of n elements to class m, where P=n!/IIki!;n>=m //equivalent to combination without repetition Cn(m) //e.g.PRM3(6,3)[PRM3a] #cas PRM3(n,m):= BEGIN MAKELIST(1,P,1,n+1)â–ļL1 0â–ļL1(1) {}â–ļL2 0â–ļM1 1â–ļJ 0â–ļI 0â–ļSW // WHILE I≠m AND L1(I)<n DO FOR I FROM 1 TO m DO IF I=1 THEN L1(1)+1â–ļL1(1) END; IF I=m AND L1(I)>n THEN BREAK END; IF L1(I)>n THEN 1â–ļL1(I) L1(I+1)+1â–ļL1(I+1) END; END;//I Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 51 // FOR K FROM 1 TO m DO FOR L FROM K+1 TO m DO IF L1(K)=L1(L) OR L1(K)>L1(L) THEN //<--- 1â–ļSW END; END; END; IF SW=0 THEN SUPPRESS(L1,n+1)â–ļL2(J) J+1â–ļJ END; 0â–ļSW END;//while // //L2â–ļM1 PRM3a(n,m)// END; #end // PRM3a.pas //PRM3a(elements n,class m)/D.G.SCHRAUSSER/2025 //e.g.PRM3a(6,3) #cas PRM3a(N,M):= BEGIN {}â–ļL3 COMB(N,M)â–ļP MAKELIST(x+1-1,x,1,N)â–ļL1 // FOR J FROM 1 TO P DO FOR I FROM 1 TO M DO L2(J,I)â–ļL3(I) END; L3â–ļL4(J)â–ļM1 END; FOR I FROM 1 TO P DO L4(I)â–ļL5;DIFFERENCE(L5,L1)â–ļL7(I) END; FOR I FROM 1 TO P DO CONCAT(L4(I),L7(I))â–ļL8(I) END; L4â–ļL2;L8â–ļL3 {}â–ļL4 {}â–ļL8 {}â–ļL5 {}â–ļL6 {}â–ļL7 L3â–ļM2 P,M2 END; #end // PRM4.pas //PRM4(elements n, class m)/D.G.SCHRAUSSER/2025 //variation matrix w(V)n(m), where V=n^m;n>=m //e.g.PRM4(4,2)[PRM4a] #cas PRM4(n,m):= BEGIN MAKELIST(1,P,1,n+1)â–ļL1 0â–ļL1(1) {}â–ļL2 0â–ļM1 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 52 1â–ļJ 0â–ļI 0â–ļSW // WHILE I≠m AND L1(I)<n DO FOR I FROM 1 TO m DO IF I=1 THEN L1(1)+1â–ļL1(1) END; IF I=m AND L1(I)>n THEN BREAK END; IF L1(I)>n THEN 1â–ļL1(I) L1(I+1)+1â–ļL1(I+1) END; END;//I // SUPPRESS(L1,n+1)â–ļL2(J);J+1â–ļJ END;//while // //L2â–ļM1 PRM4a(n,m) END; #end // PRM4a.pas //PRM4a(elements n, class m)/D.G.SCHRAUSSER/2025 //e.g.PRM4a(4,3) #cas PRM4a(N,M):= BEGIN //L2 provided V=N^M X=M+1 FOR I FROM 1 TO V DO L2(I)â–ļL3 SUPPRESS(L3,X,N)â–ļL4(I) END; L4â–ļM1 V,M1 END; #end // PRM5.pas //PRM5(cases m)/D.G.SCHRAUSSER/2025 //variation matrix w(V)2(m) for paired 2 sample design PV_, //where V=2^m //e.g.PRM5(3) #cas PRM5(N):= BEGIN M1=0 2^Nâ–ļP X=−1 0â–ļZ P/2â–ļA // FOR I FROM 1 TO N DO FOR J FROM 1 TO P DO Xâ–ļM1(J,I) Z=Z+1 IF Z=A THEN X=X*−1;0â–ļZ; END; END; 0â–ļZ // A=A/2 // A=A*0.5 END; // Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 53 M1â–ļL3 P,M1 END; #end // PRMDAT.pas //PRMDAT(rows n, cols k)/D.G.SCHRAUSSER/2025 //e.g.PRMDAT(720,6) #cas PRMDAT(N,K):= BEGIN //L3 provided FOR J FROM 1 TO N DO FOR I FROM 1 TO K DO L3(M1(J,I))â–ļM2(J,I) END; END; END; #end // Q Q01_.pas //Q01_()/D.G.SCHRAUSSER/2025 //Statistical parameters 1.0 //[L1:Raw] //L2:Distribution //L3:z-value //L4:zÂī-value #cas Q01_():= BEGIN //L1 provided SORT(L1)â–ļL2 //distr SIZE(L1)â–ļN mean(L1)â–ļAM stddev(L1)â–ļSD stddevp(L1)â–ļSD1 variance(L1)â–ļVA VA1=VA*(N/(N-1)) //SD1^2 SEM=sqrt((VA1/N)) VQ=SD/AM QGM= N NTHROOT(product(L1)) QHM=N/ÎĢ(1/L1) approx(MAKELIST(((L2(X)-AM)/SD),X,1,N))â–ļL3 //z approx(MAKELIST(((L2(X)-AM)/SD1),X,1,N))â–ļL4 //zÂī // approx(N,[AM,SEM],SD,SD1,VA,VA1,VQ,[QGM,QHM]) END; #end // Q02_.pas //Q02_()/D.G.SCHRAUSSER/2025 //Statistical parameters 2.0 //[L1:Raw] //L2:Distribution //L3:z-value #cas Q02_():= BEGIN //L1 provided Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 54 SORT(L1)â–ļL2 // SIZE(L1)â–ļN mean(L1)â–ļAM stddev(L1)â–ļSD stddevp(L1)â–ļSD1 approx(MAKELIST(((L2(X)-AM)/SD),X,1,N))â–ļL3 // ÎĢ(L3.^3)/Nâ–ļA3 sqrt(6/N)â–ļSA3 ÎĢ(L3.^4)/N-3â–ļA4 2*SA3â–ļSA4 ÎĢ((L1 .- AM) .^ 3)*N/((N-1)*(N-2)*SD1^3)â–ļA31 A41=((N-1)*(N-2)*(N-3)*SD1^4) EX1=ÎĢ((L1 .- AM) .^ 4)*N*(N+1) EX2=ÎĢ((L1 .- AM) .^ 2) EX2=3*EX2*EX2*(N-1) A41=(EX1-EX2)/A41 NORMALD_CDF(A3/SA3)â–ļPA3 P2A3=2*PA3 IF PA3>0.5 THEN P2A3=2*(1-PA3) END; NORMALD_CDF(A4/SA4)â–ļPA4 P2A4=2*PA4 IF PA4>0.5 THEN P2A4=2*(1-PA4) END; // approx(N,[A3,A31],A3/SA3,[P2A3],[A4,A41],A4/SA4,[P2A4]) END; #end // R rDiff.pas //rDiff(r1,n1,r2,n2)/D.G.SCHRAUSSER/2025 //e.g.rDiff(0.78,12,0.34,8)[ZCor] #cas rDiff(R1,N1,R2,N2):= BEGIN ZCor(R1,N1)(1)â–ļL2 L2(1)â–ļL1(1) ZCor(R2,N2)(1)â–ļL2 L2(1)â–ļL1(2) L1(1)-L1(2)â–ļL2(1) sqrt((1/(N1-3))+1/(N2-3))â–ļL2(2) L2(1)/L2(2)â–ļL2(3) NORMALD_CDF(L2(3))â–ļL2(4) 1-L2(4)â–ļL2(5) 2*L2(5)â–ļL2(6) IF L2(5)>0.5 THEN 2*L2(4)â–ļL2(6) END; //Zd,sZd,z,p,1-p,p2 [L2(1),L2(2)],[L2(3)],L2(4),L2(5),[L2(6)] END; #end // RHO.pas //RHO()/D.G.SCHRAUSSER/2025 //Spearman's rank correlation coefficient rho rs/[pCor] #cas RHO():= BEGIN //L1()(2) provided size(L1)â–ļN Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 61 AvSk(A0,S steps k),e.g.AvSk(100,3) A*((2) NTHROOT (2))^K AvTv(Av0,Tv0,Tv) A*e^(0.5*LN((T/B))) AvTvk(Av0,Tv steps k),e.g.AvTvk(8,-3) A*(√2^K) B BINOM(a,b) ÎĢ(((A+B)!/(I!*(A+B-I)!))*2.000^(-I)*2.000^(-(A+BI)),I,0.000,A) C Cf2(R)‘Cohen’s fÂē’ R^2/(1-R^2) Ch(p1,p2)‘Cohen’s h for proportion differences’ 2*ASIN(√A)-2*ASIN(√B) ChA(p2,h) SIN((1/2)*(2*ASIN(√B)+H))^2 ChB(p1,h) (-SIN((1/2)*(-2*ASIN(√A)+H)))^2 CIr(P,S,R),CIr(ci%,sdx or sdy,rxy),e.g.CIr(0.99,0.342,0.98) NORMALD_CDF(1-(1-P)/2)*S*√(1-R^2) CIx [CIx(B,C,A),CIx(n,ci%,sd),e.g.6.7+CIx(10,0.99,1.676)] STUDENT_ICDF(B-1,1-((1-C)/2))*√((A^2)*(B/(B-1))/B) D D2R(deg) X/180.000*π Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 62 E EFG(x1,L1)‘epsilon, Cohen’s d’ (A-mean(L1))/stddevp(L1) EFR(x1,L1,R)‘epsilon, Cohen’s d for paired samples’ ((A-mean(L1))/stddevp(L1))/√(1-R) Ev(Tv,Av)‘exposure value’ (LN(2))^(-1)*LN(T*A^2) F FMCORR(R,n) A^2.000*(B-4.000)/(3.000*(-(A^2.000)+1.000)) H HM_[e.g.HM_(L2)] SIZE(L1)/ÎĢ((L1(B))^(-1),B,1,SIZE(L1)) I ISOA(iso°) 10^(0.1*(S-1)) ISOL(iso) (10*LN(S)/LN(10))+1 M MCORR(r1,r2,r3) √((A^2.000+B^2.000-2.000*C*A*B)/(1.000-C^2.000)) mr(r1,n1,r2,n2) (e^(2*(((0.5*LN(((1+A)/(1-A)))*(B-3))+(0.5*LN(((1+C)/(1C)))*(D-3)))/((B-3)+(D-3))))-1)/(e^(2*(((0.5*LN(((1+A)/(1A)))*(B-3))+(0.5*LN(((1+C)/(1-C)))*(D-3)))/((B-3)+(D-3))))+1) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 63 mZ(r1,n1,r2,n2) ((0.5*LN(((1+A)/(1-A)))*(B-3))+(0.5*LN(((1+C)/(1-C)))*(D3)))/((B-3)+(D-3)) N NBINOM(k,p,n) ÎĢ(((K+I-1.000)!/(I!*(K-1.000)!))*P^K*(1.000-P)^I,I,0.000,N-K) nk(N,K) N!/(K!*(N-K)!) npz(x,am,sd,N)‘p>=z quantity at N’,e.g.npz(160,100,15,8*10^9) (1-NORMALD_CDF(((X-A)/S)))*N P p2F(a,b) (1-CHISQUARE_CDF(1,((A-((A+B)/2))^2/((A+B)/2))+((B- ((A+B)/2))^2/((A+B)/2))))/2 p4F(a,b,c,d) (1-CHISQUARE_CDF(1,((A+B+C+D)*(A*DB*C)^2/((A+B)*(C+D)*(A+C)*(B+D)))))/2 p4FY(a,b,c,d) Yates corr 4<fe<7 (1-CHISQUARE_CDF(1,((A+B+C+D)*(ABS(A*D-B*C)- ((A+B+C+D)/2))^2/((A+B)*(C+D)*(A+C)*(B+D)))))/2 PHI(a,d,b,c) (A*D-B*C)/(sqrt((A+C)*(B+D)*(A+B)*(C+D))) pMCORR(n,R) (1-FISHER_CDF(3,(B-4),(A^2*(B-4)/(3*(-(A^2)+1)))))/2 pMN(b,c) (1-CHISQUARE_CDF(1,((A-B)^2/(A+B))))/2 pMNY(b,c) Yates corr 20<b+c<30 (1-CHISQUARE_CDF(1,((ABS(A-B)-0.5)^2/(A+B))))/2 POL(x) polar_coordinates(X) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 64 pPHI(a,d,b,c) (1-CHISQUARE_CDF(1,((A*DB*C)/(√((A+C)*(B+D)*(A+B)*(C+D))))^2*(A+B+C+D)))/2 prbis(L1,L2) normald_cdf((((mean(L1)- mean(L2))/stddev(CONCAT(L1,L2)))*SIZE(L1)*SIZE(L2)/((1/(√(2*π) ))*e^(- (NORMALD_ICDF((SIZE(L2)/SIZE(CONCAT(L1,L2))))^2)/2)*SIZE(CONCA T(L1,L2))^2))/(√(SIZE(L1)*SIZE(L2))/((√SIZE(CONCAT(L1,L2))*SIZ E(CONCAT(L1,L2))*1/(√(2*π)))*e^(- (NORMALD_ICDF((SIZE(L2)/SIZE(CONCAT(L1,L2))))^2)/2)))) prbisR(L1,L2) normald_cdf(((size(L1)*size(L2)+(((size(L1))^2+size(L1))/2)- ÎĢLIST(L1)- size(L1)*size(L2)/2)/(sqrt(size(L1)*size(L2)*(size(L1)+size(L2 )+1)/12)))) prr(r1,r2,n1,n2) NORMALD_CDF(((0.5*LN(((1+A)/(1-A)))-0.5*LN(((1+B)/(1B))))/(√((1/(C-3))+1/(D-3))))) prtet(b,c,a,d) NORMALD_CDF((COS((π/(1+√(B*C/(A*D)))))/(√(((A+B)/(A+B+C+D))*(( A+C)/(A+B+C+D))*((C+D)/(A+B+C+D))*((B+D)/(A+B+C+D))/(A+B+C+D)) *(1/(((1/(√(2*π)))*e^(- (NORMALD_ICDF(((C+D)/(A+B+C+D)))^2)/2))*((1/(√(2*π)))*e^(- (NORMALD_ICDF(((B+D)/(A+B+C+D)))^2)/2))))))) pRW(L1,L2) STUDENT_CDF(SIZE(L1)-2,((correlation(L1,L2)*√(SIZE(L1)- 2))/(√(1-correlation(L1,L2)^2)))) pRWx(n,r) STUDENT_CDF(B-2,((A*√(B-2))/(√(1-A^2)))) prxy_z(rxy_z,n) NORMALD_CDF(0.5*LN(((1+A)/(1-A)))*√(B-2)) PTG(a,b) √(A^2+B^2) pTKV(L1,L2) STUDENT_CDF(SIZE(L1)+SIZE(L2)-2,(((variance(L1)- variance(L2))*√(SIZE(L1)- 2))/(2*√(variance(L1)*variance(L2)*(1-correlation(L1,L2)))))) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 65 pTT_(L1,y) STUDENT_CDF(SIZE(L1)-1,((mean(L1)- A)/(√(stddev(L1)^2/(SIZE(L1)-1))))) pTU_(L1,L2) STUDENT_CDF(SIZE(L1)+SIZE(L2)-2,(mean(L1)- mean(L2))/(√((ÎĢLIST(MAKELIST((L1(A)- mean(L1))^2,A,1,SIZE(L1)))+ÎĢLIST(MAKELIST((L2(A)- mean(L2))^2,A,1,SIZE(L2))))/(SIZE(L1)-1+SIZE(L2)- 1))*(√(1/SIZE(L1))+√(1/SIZE(L2))))) pTUX(n1,n2,x1,x2,s21,s22) STUDENT_CDF(D+F-2,((A-B)/(√((C*D+E*F)/(D-1+F1))*(√(1/D)+√(1/F))))) pTV_(L1) student_cdf(size(L1)-1,ÎĢLIST(MAKELIST(L1(A)- (L2(A)),A,1,size(L1)))/(size(L1))/(√((ÎĢLIST(MAKELIST((L1(A)- (L2(A)))^2,A,1,size(L1)))-ÎĢLIST(MAKELIST(L1(A)- (L2(A)),A,1,size(L1)))^2/(size(L1)))/(size(L1)- 1))*1/(√(size(L1)-1)))) pz4F(a,b,c,d)[e.g.pz4F(11,20,80,58)] NORMALD_CDF(((D- (A+B+C+D)*((D+B)*(C+D)/(A+B+C+D)^2))/(√((A+B+C+D)*(1- ((D+B)*(C+D)/(A+B+C+D)^2))-(A+B+C+D)*(A+B+C+D1)*((D+B)*(C+D)/(A+B+C+D)^2)*(((D+B)*(C+D)/(A+B+C+D)^2)-((D+B1)*(C+D-1)/(A+B+C+D-1)^2)))))) pzBN(a,b) NORMALD_CDF(((A-(A+B)/2)/(√((A+B)/4)))) R R2D(rad) X/π*180.000 rbis(L1,L2) ((mean(L1)- mean(L2))/stddev(CONCAT(L1,L2)))*SIZE(L1)*SIZE(L2)/((1/(√(2*π) ))*e^(- (NORMALD_ICDF((SIZE(L2)/SIZE(CONCAT(L1,L2))))^2)/2)*SIZE(CONCA T(L1,L2))^2) rbisR(L1,L2) (2/(SIZE(L1)+SIZE(L2)))*(mean(L1)-mean(L2)) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 66 RED(r) A^2*100 RND1(n) MAKELIST(RANDNORM,A,1,B) RND2(n) MAKELIST(RANDOM,A,1,B) rpbis(L1,L2) (mean(L1)- mean(L2))/stddev(CONCAT(L1,L2))*√(SIZE(L1)*SIZE(L2)/(SIZE(CONC AT(L1,L2)))^2) rxy(x1,x2) approx(correlation(L1,L2)) rtet(b,c,a,d),rad COS((π/(1+√(B*C/(A*D))))) rxy_z(rxy,rxz,ryz) (A-B*C)/(√(1-B^2)*√(1-C^2)) ry_xz(rxy,rxz,ryz) (A-B*C)/(sqrt(1-B^2)) rZ(Z) (e^(2*A)-1)/(e^(2*A)+1) S SCR(n,k,R) 1.00-((A-3.00)/(A-B-2.00))*((1.00-C^2.00)+((2.00/(AB)))*(1.00-C^2.00)^2.00) SMG [SMG(A,B),SMG(sd,n)] √((A^2)*(B/(B-1))/B) SQR(x) A^2 srbis(L1,L2) √(SIZE(L1)*SIZE(L2))/((√SIZE(CONCAT(L1,L2))*SIZE(CONCAT(L1,L2) )*1/(√(2*π)))*e^(- (NORMALD_ICDF((SIZE(L2)/SIZE(CONCAT(L1,L2))))^2)/2)) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 67 srtet(b,c,a,d) √(((A+B)/(A+B+C+D))*((A+C)/(A+B+C+D))*((C+D)/(A+B+C+D))*((B+D) /(A+B+C+D))/(A+B+C+D))*(1/(((1/(√(2*π)))*e^(- (NORMALD_ICDF(((C+D)/(A+B+C+D)))^2)/2))*((1/(√(2*π)))*e^(- (NORMALD_ICDF(((B+D)/(A+B+C+D)))^2)/2)))) sumd2(L1) ÎĢLIST(MAKELIST((L1(A)-(L2(A)))^2,A,1,SIZE(L1))) CAS input L4:=ÎĢLIST(L3:=MAKELIST((L1(x)-(L2(x)))^2,x,1,size(L1))) L4:=ÎĢLIST(L3:=MAKELIST(L1(x)-(L2(x)),x,1,size(L1))) L4:=ÎĢLIST(L3:=MAKELIST(L1(x)-(L2(x)),x,1,size(L1)))^2 sumx2(L1,L2) CAS input ÎĢLIST(L3:=approx(MAKELIST((L1(x)-mean(L1))^2,x,1,size(L1)))) T TEv(Ev,Av) 2^E/A^2 tr(r,n) (R*√(N-2))/(√(1-R^2)) TRW(L1,L2) (correlation(L1,L2)*√(SIZE(L1)-2))/(√(1-correlation(L1,L2)^2)) tTKV(L1,L2) ((variance(L1)-variance(L2))*sqrt(size(L1)- 2))/(2*sqrt(variance(L1)*variance(L2)*(1-correlation(L1,L2)))) tTT_(L1,y) (mean(L1)-A)/(√(stddev(L1)^2/(SIZE(L1)-1))) tTU_(L1,L2) (mean(L1)-mean(L2))/(sqrt((ÎĢLIST(MAKELIST((L1(x)- mean(L1))^2,x,1,size(L1)))+ ÎĢLIST(MAKELIST((L2(x)- mean(L2))^2,x,1,size(L2))))/ (size(L1)-1+size(L2)- 1))*(sqrt(1/size(L1))+sqrt(1/size(L2)))) tTUX(x1,x2,s21,n1,s22,n2) (A-B)/(√((C*D+E*F)/(D-1+F-1))*(√(1/D)+√(1/F))) tTV_(L1) ÎĢLIST(MAKELIST(L1(A)- (L2(A)),A,1,size(L1)))/(size(L1))/(√((ÎĢLIST(MAKELIST((L1(A)- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 68 (L2(A)))^2,A,1,size(L1)))-ÎĢLIST(MAKELIST(L1(A)- (L2(A)),A,1,size(L1)))^2/(size(L1)))/(size(L1)- 1))*1/(√(size(L1)-1))) U U_1(L1,L2) SIZE(L1)*SIZE(L2)+(((SIZE(L1))^2+SIZE(L1))/2)-ÎĢLIST(L1) U_2(L1,L2) SIZE(L1)*SIZE(L2)+(((SIZE(L2))^2+SIZE(L2))/2)-ÎĢLIST(L2) X x2F(a,b) ((A-((A+B)/2))^2/((A+B)/2))+(B-((A+B)/2))^2/((A+B)/2) x4F(a,b,c,d) (A+B+C+D)*(A*D-B*C)^2/((A+B)*(C+D)*(A+C)*(B+D)) x4FY(a,b,c,d) Yates corr 4<fe<7 (A+B+C+D)*(ABS(A*D-B*C)- ((A+B+C+D)/2))^2/((A+B)*(C+D)*(A+C)*(B+D)) xMN(b,c) (A-B)^2/(A+B) xMNY(b,c) Yates corr 20<b+c<30 (ABS(A-B)-0.5)^2/(A+B) xPHI(a,d,b,c) (((A*D-B*C)/(√((A+C)*(B+D)*(A+B)*(C+D)))))^2*(A+B+C+D) Z z4F(a,b,c,d) (D-(A+B+C+D)*((D+B)*(C+D)/(A+B+C+D)^2))/(√((A+B+C+D)*(1- ((D+B)*(C+D)/(A+B+C+D)^2))-(A+B+C+D)*(A+B+C+D1)*((D+B)*(C+D)/(A+B+C+D)^2)*(((D+B)*(C+D)/(A+B+C+D)^2)-((D+B1)*(C+D-1)/(A+B+C+D-1)^2)))) zBN(a,b) (A-(A+B)/2)/(√((A+B)/4)) Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 69 Zr(r) 0.5*LN((1+A)/(1-A)) zrbis(L1,L2) (((mean(L1)- mean(L2))/stddev(CONCAT(L1,L2)))*SIZE(L1)*SIZE(L2)/((1/(√(2*π) ))*e^(- (NORMALD_ICDF((SIZE(L2)/SIZE(CONCAT(L1,L2))))^2)/2)*SIZE(CONCA T(L1,L2))^2))/(√(SIZE(L1)*SIZE(L2))/((√SIZE(CONCAT(L1,L2))*SIZ E(CONCAT(L1,L2))*1/(√(2*π)))*e^(- (NORMALD_ICDF((SIZE(L2)/SIZE(CONCAT(L1,L2))))^2)/2))) zrbisR(L1,L2) (size(L1)*size(L2)+(((size(L1))^2+size(L1))/2)-ÎĢLIST(L1)- size(L1)*size(L2)/2)/(sqrt(size(L1)*size(L2)*(size(L1)+size(L2 )+1)/12)) zrr(r1,r2,n1,n2) (0.5*LN((1+A)/(1-A))-0.5*LN((1+B)/(1-B)))/(√(1/(C-3)+1/(D-3))) zrxy_z(rxy_z,n) 0.5*LN(((1+A)/(1-A)))*√(B-2) ZWERT(x1,x,s) (A-B)/C zVAL(L1),e.g.L2:=zVAL(L1) approx(MAKELIST(((L1(X)-mean(L1))/stddev(L1)),X,1,SIZE(L1))) zVALp(L1),e.g.L3:=zVAL(L1) approx(MAKELIST(((L1(X)-mean(L1))/stddevp(L1)),X,1,SIZE(L1))) Application functions Function To select: F01.pas //F01()/D.G.SCHRAUSSER/2022 //Function: Equations 1.0 EXPORT F01() BEGIN "√(1-((X-W)/A)^2)*A+V"â–ļF1; "−√(1-((X-W)/A)^2)*A+V"â–ļF2; "√(1-((X-T)/B)^2)*B+U"â–ļF3; "-√(1-((X-T)/B)^2)*B+U"â–ļF4; "√(1-((X-R)/C)^2)*C+S"â–ļF5; Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 70 "-√(1-((X-R)/C)^2)*C+S"â–ļF6; 200â–ļA; 150â–ļB; 344â–ļC; 1â–ļW; 450â–ļT; 1000â–ļR; "Function: Equations 1.0" END; // F02.pas //F02()/D.G.SCHRAUSSER/2022 //Function: Equations 2.0 EXPORT F02() BEGIN "NORMALD_CDF(0,1,X)"â–ļF1; "NORMALD(0,1,X)"â–ļF2; "STUDENT(50,X)"â–ļF3; "STUDENT_CDF(50,X)"â–ļF4; "CHISQUARE(1,X)"â–ļF5; "CHISQUARE_CDF(1,X)"â–ļF6; "FISHER_CDF(25,3,X)"â–ļF7; "FISHER(25,3,X)"â–ļF8; "0"â–ļF9; "0"â–ļF0; "Function: Equations 2.0" END; // F03.pas //F03()/D.G.SCHRAUSSER/2025 //Function: Equations 3.0 //F3-7:Derivatives of the standard normal distribution function, f'(z)- f'''''(z) EXPORT F03() BEGIN "NORMALD_CDF(0,1,X)"â–ļF1; "NORMALD(0,1,X)"â–ļF2; "∂((1/√(2*π))*e^((-1/2)*X^2),X=X)"â–ļF3; "∂(∂((1/√(2*π))*e^((-1/2)*X^2),X),X)"â–ļF4; "∂(∂(∂((1/√(2*π))*e^((-1/2)*X^2),X),X),X)"â–ļF5; "∂(∂(∂(∂((1/√(2*π))*e^((-1/2)*X^2),X),X),X),X)"â–ļF6; "∂(∂(∂(∂(∂((1/√(2*π))*e^((-1/2)*X^2),X),X),X),X),X)"â–ļF7; "0"â–ļF8; "0"â–ļF9; "0"â–ļF0; "Function: Equations 3.0" END; // F04.pas //F04()/D.G.SCHRAUSSER/2025 //Function: Equations 4.0 //F3:Derivative of Gamma, f'(x) //F5-7:Derivatives of the exponential function, f(x)={f'(x)-f'''(x)...} EXPORT F04() BEGIN "CAS.Gamma(X)"â–ļF1; "(X)!"â–ļF2; "∂(Gamma(X),X=X)"â–ļF3; "EXP(X)"â–ļF4; Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 77 {1.,0.,0.,1.,0.,1.}â–ļL1 {0.,1.,0.,1.,0.,0.}â–ļL2 {1.,0.,0.,1.,0.,1.,1.,1.,1.,0.,0.,1.,0.,0.,0.,1.,1.,0.,1.,0.,0.,0.,0.,0.,1.} â–ļL1 {0.,1.,0.,1.,0.,0.,1.,1.,1.,1.,0.,0.,1.,1.,0.,1.,1.,1.,0.,1.,1.,1.,0.,0.,1.} â–ļL2 PV_ {{1.,4.},{2.,6.},{2.,7.}}â–ļL1 (Scambor, 1997; Scambor & Schrausser, 2022, p. 7) {{8.,9.},{2.,3.},{9.,7.},{2.,7.},{9.,8.}}â–ļL1 {{8.6,4.5},{9.2,4.1},{6.7,1.2},{9.6,4.5},{6.2,7.6},{6.1,8.5},{9.3,4.2},{7.3, 8.2}}â–ļL1 PU_ {18.,30.,54.}â–ļL1 {6.,12.}â–ļL2 (Schrausser, 1996, 2022b, p. 2) {18.04,10.07,22.27,1.96,18.88,12.81,3.08,21.49,1.96,24.93}â–ļL1 {24.96,22.39}â–ļL2 (Schrausser, 1997, 1998a) Matrices wP8_4_4.dat M1=[[1,2,3,4,5,6,7,8],[1,2,3,5,4,6,7,8],[1,2,4,5,3,6,7,8],[1,3,4,5,2,6,7,8],[2,3,4,5, 1,6,7,8],[1,2,3,6,4,5,7,8],[1,2,4,6,3,5,7,8],[1,3,4,6,2,5,7,8],[2,3,4,6,1,5,7,8],[1,2 ,5,6,3,4,7,8],[1,3,5,6,2,4,7,8],[2,3,5,6,1,4,7,8],[1,4,5,6,2,3,7,8],[2,4,5,6,1,3,7,8] ,[3,4,5,6,1,2,7,8],[1,2,3,7,4,5,6,8],[1,2,4,7,3,5,6,8],[1,3,4,7,2,5,6,8],[2,3,4,7,1,5 ,6,8],[1,2,5,7,3,4,6,8],[1,3,5,7,2,4,6,8],[2,3,5,7,1,4,6,8],[1,4,5,7,2,3,6,8],[2,4,5, 7,1,3,6,8],[3,4,5,7,1,2,6,8],[1,2,6,7,3,4,5,8],[1,3,6,7,2,4,5,8],[2,3,6,7,1,4,5,8],[1 ,4,6,7,2,3,5,8],[2,4,6,7,1,3,5,8],[3,4,6,7,1,2,5,8],[1,5,6,7,2,3,4,8],[2,5,6,7,1,3,4, 8],[3,5,6,7,1,2,4,8],[4,5,6,7,1,2,3,8],[1,2,3,8,4,5,6,7],[1,2,4,8,3,5,6,7],[1,3,4,8,2 ,5,6,7],[2,3,4,8,1,5,6,7],[1,2,5,8,3,4,6,7],[1,3,5,8,2,4,6,7],[2,3,5,8,1,4,6,7],[1,4, 5,8,2,3,6,7],[2,4,5,8,1,3,6,7],[3,4,5,8,1,2,6,7],[1,2,6,8,3,4,5,7],[1,3,6,8,2,4,5,7], [2,3,6,8,1,4,5,7],[1,4,6,8,2,3,5,7],[2,4,6,8,1,3,5,7],[3,4,6,8,1,2,5,7],[1,5,6,8,2,3, 4,7],[2,5,6,8,1,3,4,7],[3,5,6,8,1,2,4,7],[4,5,6,8,1,2,3,7],[1,2,7,8,3,4,5,6],[1,3,7,8 ,2,4,5,6],[2,3,7,8,1,4,5,6],[1,4,7,8,2,3,5,6],[2,4,7,8,1,3,5,6],[3,4,7,8,1,2,5,6],[1, 5,7,8,2,3,4,6],[2,5,7,8,1,3,4,6],[3,5,7,8,1,2,4,6],[4,5,7,8,1,2,3,6],[1,6,7,8,2,3,4,5 ],[2,6,7,8,1,3,4,5],[3,6,7,8,1,2,4,5],[4,6,7,8,1,2,3,5],[5,6,7,8,1,2,3,4]] L1={{1,2,3,4,5,6,7,8},{1,2,3,5,4,6,7,8},{1,2,4,5,3,6,7,8},{1,3,4,5,2,6,7,8},{2,3,4,5, 1,6,7,8},{1,2,3,6,4,5,7,8},{1,2,4,6,3,5,7,8},{1,3,4,6,2,5,7,8},{2,3,4,6,1,5,7,8},{1,2 ,5,6,3,4,7,8},{1,3,5,6,2,4,7,8},{2,3,5,6,1,4,7,8},{1,4,5,6,2,3,7,8},{2,4,5,6,1,3,7,8} ,{3,4,5,6,1,2,7,8},{1,2,3,7,4,5,6,8},{1,2,4,7,3,5,6,8},{1,3,4,7,2,5,6,8},{2,3,4,7,1,5 ,6,8},{1,2,5,7,3,4,6,8},{1,3,5,7,2,4,6,8},{2,3,5,7,1,4,6,8},{1,4,5,7,2,3,6,8},{2,4,5, 7,1,3,6,8},{3,4,5,7,1,2,6,8},{1,2,6,7,3,4,5,8},{1,3,6,7,2,4,5,8},{2,3,6,7,1,4,5,8},{1 ,4,6,7,2,3,5,8},{2,4,6,7,1,3,5,8},{3,4,6,7,1,2,5,8},{1,5,6,7,2,3,4,8},{2,5,6,7,1,3,4, 8},{3,5,6,7,1,2,4,8},{4,5,6,7,1,2,3,8},{1,2,3,8,4,5,6,7},{1,2,4,8,3,5,6,7},{1,3,4,8,2 ,5,6,7},{2,3,4,8,1,5,6,7},{1,2,5,8,3,4,6,7},{1,3,5,8,2,4,6,7},{2,3,5,8,1,4,6,7},{1,4, 5,8,2,3,6,7},{2,4,5,8,1,3,6,7},{3,4,5,8,1,2,6,7},{1,2,6,8,3,4,5,7},{1,3,6,8,2,4,5,7}, {2,3,6,8,1,4,5,7},{1,4,6,8,2,3,5,7},{2,4,6,8,1,3,5,7},{3,4,6,8,1,2,5,7},{1,5,6,8,2,3, 4,7},{2,5,6,8,1,3,4,7},{3,5,6,8,1,2,4,7},{4,5,6,8,1,2,3,7},{1,2,7,8,3,4,5,6},{1,3,7,8 ,2,4,5,6},{2,3,7,8,1,4,5,6},{1,4,7,8,2,3,5,6},{2,4,7,8,1,3,5,6},{3,4,7,8,1,2,5,6},{1, 5,7,8,2,3,4,6},{2,5,7,8,1,3,4,6},{3,5,7,8,1,2,4,6},{4,5,7,8,1,2,3,6},{1,6,7,8,2,3,4,5 },{2,6,7,8,1,3,4,5},{3,6,7,8,1,2,4,5},{4,6,7,8,1,2,3,5},{5,6,7,8,1,2,3,4}} Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 78 wP9_4_5.dat M1=[[1.,2.,3.,4.,5.,6.,7.,8.,9.],[1.,2.,3.,5.,4.,6.,7.,8.,9.],[1.,2.,4.,5.,3.,6.,7.,8 .,9.],[1.,3.,4.,5.,2.,6.,7.,8.,9.],[2.,3.,4.,5.,1.,6.,7.,8.,9.],[1.,2.,3.,6.,4.,5.,7. ,8.,9.],[1.,2.,4.,6.,3.,5.,7.,8.,9.],[1.,3.,4.,6.,2.,5.,7.,8.,9.],[2.,3.,4.,6.,1.,5., 7.,8.,9.],[1.,2.,5.,6.,3.,4.,7.,8.,9.],[1.,3.,5.,6.,2.,4.,7.,8.,9.],[2.,3.,5.,6.,1.,4 .,7.,8.,9.],[1.,4.,5.,6.,2.,3.,7.,8.,9.],[2.,4.,5.,6.,1.,3.,7.,8.,9.],[3.,4.,5.,6.,1. ,2.,7.,8.,9.],[1.,2.,3.,7.,4.,5.,6.,8.,9.],[1.,2.,4.,7.,3.,5.,6.,8.,9.],[1.,3.,4.,7., 2.,5.,6.,8.,9.],[2.,3.,4.,7.,1.,5.,6.,8.,9.],[1.,2.,5.,7.,3.,4.,6.,8.,9.],[1.,3.,5.,7 .,2.,4.,6.,8.,9.],[2.,3.,5.,7.,1.,4.,6.,8.,9.],[1.,4.,5.,7.,2.,3.,6.,8.,9.],[2.,4.,5. ,7.,1.,3.,6.,8.,9.],[3.,4.,5.,7.,1.,2.,6.,8.,9.],[1.,2.,6.,7.,3.,4.,5.,8.,9.],[1.,3., 6.,7.,2.,4.,5.,8.,9.],[2.,3.,6.,7.,1.,4.,5.,8.,9.],[1.,4.,6.,7.,2.,3.,5.,8.,9.],[2.,4 .,6.,7.,1.,3.,5.,8.,9.],[3.,4.,6.,7.,1.,2.,5.,8.,9.],[1.,5.,6.,7.,2.,3.,4.,8.,9.],[2. ,5.,6.,7.,1.,3.,4.,8.,9.],[3.,5.,6.,7.,1.,2.,4.,8.,9.],[4.,5.,6.,7.,1.,2.,3.,8.,9.],[ 1.,2.,3.,8.,4.,5.,6.,7.,9.],[1.,2.,4.,8.,3.,5.,6.,7.,9.],[1.,3.,4.,8.,2.,5.,6.,7.,9.] ,[2.,3.,4.,8.,1.,5.,6.,7.,9.],[1.,2.,5.,8.,3.,4.,6.,7.,9.],[1.,3.,5.,8.,2.,4.,6.,7.,9 .],[2.,3.,5.,8.,1.,4.,6.,7.,9.],[1.,4.,5.,8.,2.,3.,6.,7.,9.],[2.,4.,5.,8.,1.,3.,6.,7. ,9.],[3.,4.,5.,8.,1.,2.,6.,7.,9.],[1.,2.,6.,8.,3.,4.,5.,7.,9.],[1.,3.,6.,8.,2.,4.,5., 7.,9.],[2.,3.,6.,8.,1.,4.,5.,7.,9.],[1.,4.,6.,8.,2.,3.,5.,7.,9.],[2.,4.,6.,8.,1.,3.,5 .,7.,9.],[3.,4.,6.,8.,1.,2.,5.,7.,9.],[1.,5.,6.,8.,2.,3.,4.,7.,9.],[2.,5.,6.,8.,1.,3. ,4.,7.,9.],[3.,5.,6.,8.,1.,2.,4.,7.,9.],[4.,5.,6.,8.,1.,2.,3.,7.,9.],[1.,2.,7.,8.,3., 4.,5.,6.,9.],[1.,3.,7.,8.,2.,4.,5.,6.,9.],[2.,3.,7.,8.,1.,4.,5.,6.,9.],[1.,4.,7.,8.,2 .,3.,5.,6.,9.],[2.,4.,7.,8.,1.,3.,5.,6.,9.],[3.,4.,7.,8.,1.,2.,5.,6.,9.],[1.,5.,7.,8. ,2.,3.,4.,6.,9.],[2.,5.,7.,8.,1.,3.,4.,6.,9.],[3.,5.,7.,8.,1.,2.,4.,6.,9.],[4.,5.,7., 8.,1.,2.,3.,6.,9.],[1.,6.,7.,8.,2.,3.,4.,5.,9.],[2.,6.,7.,8.,1.,3.,4.,5.,9.],[3.,6.,7 .,8.,1.,2.,4.,5.,9.],[4.,6.,7.,8.,1.,2.,3.,5.,9.],[5.,6.,7.,8.,1.,2.,3.,4.,9.],[1.,2. ,3.,9.,4.,5.,6.,7.,8.],[1.,2.,4.,9.,3.,5.,6.,7.,8.],[1.,3.,4.,9.,2.,5.,6.,7.,8.],[2., 3.,4.,9.,1.,5.,6.,7.,8.],[1.,2.,5.,9.,3.,4.,6.,7.,8.],[1.,3.,5.,9.,2.,4.,6.,7.,8.],[2 .,3.,5.,9.,1.,4.,6.,7.,8.],[1.,4.,5.,9.,2.,3.,6.,7.,8.],[2.,4.,5.,9.,1.,3.,6.,7.,8.], [3.,4.,5.,9.,1.,2.,6.,7.,8.],[1.,2.,6.,9.,3.,4.,5.,7.,8.],[1.,3.,6.,9.,2.,4.,5.,7.,8. ],[2.,3.,6.,9.,1.,4.,5.,7.,8.],[1.,4.,6.,9.,2.,3.,5.,7.,8.],[2.,4.,6.,9.,1.,3.,5.,7., 8.],[3.,4.,6.,9.,1.,2.,5.,7.,8.],[1.,5.,6.,9.,2.,3.,4.,7.,8.],[2.,5.,6.,9.,1.,3.,4.,7 .,8.],[3.,5.,6.,9.,1.,2.,4.,7.,8.],[4.,5.,6.,9.,1.,2.,3.,7.,8.],[1.,2.,7.,9.,3.,4.,5. ,6.,8.],[1.,3.,7.,9.,2.,4.,5.,6.,8.],[2.,3.,7.,9.,1.,4.,5.,6.,8.],[1.,4.,7.,9.,2.,3., 5.,6.,8.],[2.,4.,7.,9.,1.,3.,5.,6.,8.],[3.,4.,7.,9.,1.,2.,5.,6.,8.],[1.,5.,7.,9.,2.,3 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G. (2025). HP_Prime_MATH: Manual. 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0},{2,3,6,7,8,1,4,5,9,10},{1,4,6,7,8,2,3,5,9,10},{2,4,6,7,8,1,3,5,9,10},{3,4,6,7,8,1, 2,5,9,10},{1,5,6,7,8,2,3,4,9,10},{2,5,6,7,8,1,3,4,9,10},{3,5,6,7,8,1,2,4,9,10},{4,5,6 ,7,8,1,2,3,9,10},{1,2,3,4,9,5,6,7,8,10},{1,2,3,5,9,4,6,7,8,10},{1,2,4,5,9,3,6,7,8,10} ,{1,3,4,5,9,2,6,7,8,10},{2,3,4,5,9,1,6,7,8,10},{1,2,3,6,9,4,5,7,8,10},{1,2,4,6,9,3,5, 7,8,10},{1,3,4,6,9,2,5,7,8,10},{2,3,4,6,9,1,5,7,8,10},{1,2,5,6,9,3,4,7,8,10},{1,3,5,6 ,9,2,4,7,8,10},{2,3,5,6,9,1,4,7,8,10},{1,4,5,6,9,2,3,7,8,10},{2,4,5,6,9,1,3,7,8,10},{ 3,4,5,6,9,1,2,7,8,10},{1,2,3,7,9,4,5,6,8,10},{1,2,4,7,9,3,5,6,8,10},{1,3,4,7,9,2,5,6, 8,10},{2,3,4,7,9,1,5,6,8,10},{1,2,5,7,9,3,4,6,8,10},{1,3,5,7,9,2,4,6,8,10},{2,3,5,7,9 ,1,4,6,8,10},{1,4,5,7,9,2,3,6,8,10},{2,4,5,7,9,1,3,6,8,10},{3,4,5,7,9,1,2,6,8,10},{1, 2,6,7,9,3,4,5,8,10},{1,3,6,7,9,2,4,5,8,10},{2,3,6,7,9,1,4,5,8,10},{1,4,6,7,9,2,3,5,8, 10},{2,4,6,7,9,1,3,5,8,10},{3,4,6,7,9,1,2,5,8,10},{1,5,6,7,9,2,3,4,8,10},{2,5,6,7,9,1 ,3,4,8,10},{3,5,6,7,9,1,2,4,8,10},{4,5,6,7,9,1,2,3,8,10},{1,2,3,8,9,4,5,6,7,10},{1,2, 4,8,9,3,5,6,7,10},{1,3,4,8,9,2,5,6,7,10},{2,3,4,8,9,1,5,6,7,10},{1,2,5,8,9,3,4,6,7,10 },{1,3,5,8,9,2,4,6,7,10},{2,3,5,8,9,1,4,6,7,10},{1,4,5,8,9,2,3,6,7,10},{2,4,5,8,9,1,3 ,6,7,10},{3,4,5,8,9,1,2,6,7,10},{1,2,6,8,9,3,4,5,7,10},{1,3,6,8,9,2,4,5,7,10},{2,3,6, 8,9,1,4,5,7,10},{1,4,6,8,9,2,3,5,7,10},{2,4,6,8,9,1,3,5,7,10},{3,4,6,8,9,1,2,5,7,10}, {1,5,6,8,9,2,3,4,7,10},{2,5,6,8,9,1,3,4,7,10},{3,5,6,8,9,1,2,4,7,10},{4,5,6,8,9,1,2,3 ,7,10},{1,2,7,8,9,3,4,5,6,10},{1,3,7,8,9,2,4,5,6,10},{2,3,7,8,9,1,4,5,6,10},{1,4,7,8, 9,2,3,5,6,10},{2,4,7,8,9,1,3,5,6,10},{3,4,7,8,9,1,2,5,6,10},{1,5,7,8,9,2,3,4,6,10},{2 ,5,7,8,9,1,3,4,6,10},{3,5,7,8,9,1,2,4,6,10},{4,5,7,8,9,1,2,3,6,10},{1,6,7,8,9,2,3,4,5 ,10},{2,6,7,8,9,1,3,4,5,10},{3,6,7,8,9,1,2,4,5,10},{4,6,7,8,9,1,2,3,5,10},{5,6,7,8,9, 1,2,3,4,10},{1,2,3,4,10,5,6,7,8,9},{1,2,3,5,10,4,6,7,8,9},{1,2,4,5,10,3,6,7,8,9},{1,3 ,4,5,10,2,6,7,8,9},{2,3,4,5,10,1,6,7,8,9},{1,2,3,6,10,4,5,7,8,9},{1,2,4,6,10,3,5,7,8, 9},{1,3,4,6,10,2,5,7,8,9},{2,3,4,6,10,1,5,7,8,9},{1,2,5,6,10,3,4,7,8,9},{1,3,5,6,10,2 ,4,7,8,9},{2,3,5,6,10,1,4,7,8,9},{1,4,5,6,10,2,3,7,8,9},{2,4,5,6,10,1,3,7,8,9},{3,4,5 ,6,10,1,2,7,8,9},{1,2,3,7,10,4,5,6,8,9},{1,2,4,7,10,3,5,6,8,9},{1,3,4,7,10,2,5,6,8,9} ,{2,3,4,7,10,1,5,6,8,9},{1,2,5,7,10,3,4,6,8,9},{1,3,5,7,10,2,4,6,8,9},{2,3,5,7,10,1,4 ,6,8,9},{1,4,5,7,10,2,3,6,8,9},{2,4,5,7,10,1,3,6,8,9},{3,4,5,7,10,1,2,6,8,9},{1,2,6,7 ,10,3,4,5,8,9},{1,3,6,7,10,2,4,5,8,9},{2,3,6,7,10,1,4,5,8,9},{1,4,6,7,10,2,3,5,8,9},{ 2,4,6,7,10,1,3,5,8,9},{3,4,6,7,10,1,2,5,8,9},{1,5,6,7,10,2,3,4,8,9},{2,5,6,7,10,1,3,4 ,8,9},{3,5,6,7,10,1,2,4,8,9},{4,5,6,7,10,1,2,3,8,9},{1,2,3,8,10,4,5,6,7,9},{1,2,4,8,1 0,3,5,6,7,9},{1,3,4,8,10,2,5,6,7,9},{2,3,4,8,10,1,5,6,7,9},{1,2,5,8,10,3,4,6,7,9},{1, 3,5,8,10,2,4,6,7,9},{2,3,5,8,10,1,4,6,7,9},{1,4,5,8,10,2,3,6,7,9},{2,4,5,8,10,1,3,6,7 ,9},{3,4,5,8,10,1,2,6,7,9},{1,2,6,8,10,3,4,5,7,9},{1,3,6,8,10,2,4,5,7,9},{2,3,6,8,10, 1,4,5,7,9},{1,4,6,8,10,2,3,5,7,9},{2,4,6,8,10,1,3,5,7,9},{3,4,6,8,10,1,2,5,7,9},{1,5, 6,8,10,2,3,4,7,9},{2,5,6,8,10,1,3,4,7,9},{3,5,6,8,10,1,2,4,7,9},{4,5,6,8,10,1,2,3,7,9 },{1,2,7,8,10,3,4,5,6,9},{1,3,7,8,10,2,4,5,6,9},{2,3,7,8,10,1,4,5,6,9},{1,4,7,8,10,2, 3,5,6,9},{2,4,7,8,10,1,3,5,6,9},{3,4,7,8,10,1,2,5,6,9},{1,5,7,8,10,2,3,4,6,9},{2,5,7, 8,10,1,3,4,6,9},{3,5,7,8,10,1,2,4,6,9},{4,5,7,8,10,1,2,3,6,9},{1,6,7,8,10,2,3,4,5,9}, {2,6,7,8,10,1,3,4,5,9},{3,6,7,8,10,1,2,4,5,9},{4,6,7,8,10,1,2,3,5,9},{5,6,7,8,10,1,2, 3,4,9},{1,2,3,9,10,4,5,6,7,8},{1,2,4,9,10,3,5,6,7,8},{1,3,4,9,10,2,5,6,7,8},{2,3,4,9, 10,1,5,6,7,8},{1,2,5,9,10,3,4,6,7,8},{1,3,5,9,10,2,4,6,7,8},{2,3,5,9,10,1,4,6,7,8},{1 ,4,5,9,10,2,3,6,7,8},{2,4,5,9,10,1,3,6,7,8},{3,4,5,9,10,1,2,6,7,8},{1,2,6,9,10,3,4,5, 7,8},{1,3,6,9,10,2,4,5,7,8},{2,3,6,9,10,1,4,5,7,8},{1,4,6,9,10,2,3,5,7,8},{2,4,6,9,10 ,1,3,5,7,8},{3,4,6,9,10,1,2,5,7,8},{1,5,6,9,10,2,3,4,7,8},{2,5,6,9,10,1,3,4,7,8},{3,5 ,6,9,10,1,2,4,7,8},{4,5,6,9,10,1,2,3,7,8},{1,2,7,9,10,3,4,5,6,8},{1,3,7,9,10,2,4,5,6, 8},{2,3,7,9,10,1,4,5,6,8},{1,4,7,9,10,2,3,5,6,8},{2,4,7,9,10,1,3,5,6,8},{3,4,7,9,10,1 ,2,5,6,8},{1,5,7,9,10,2,3,4,6,8},{2,5,7,9,10,1,3,4,6,8},{3,5,7,9,10,1,2,4,6,8},{4,5,7 ,9,10,1,2,3,6,8},{1,6,7,9,10,2,3,4,5,8},{2,6,7,9,10,1,3,4,5,8},{3,6,7,9,10,1,2,4,5,8} ,{4,6,7,9,10,1,2,3,5,8},{5,6,7,9,10,1,2,3,4,8},{1,2,8,9,10,3,4,5,6,7},{1,3,8,9,10,2,4 ,5,6,7},{2,3,8,9,10,1,4,5,6,7},{1,4,8,9,10,2,3,5,6,7},{2,4,8,9,10,1,3,5,6,7},{3,4,8,9 ,10,1,2,5,6,7},{1,5,8,9,10,2,3,4,6,7},{2,5,8,9,10,1,3,4,6,7},{3,5,8,9,10,1,2,4,6,7},{ 4,5,8,9,10,1,2,3,6,7},{1,6,8,9,10,2,3,4,5,7},{2,6,8,9,10,1,3,4,5,7},{3,6,8,9,10,1,2,4 ,5,7},{4,6,8,9,10,1,2,3,5,7},{5,6,8,9,10,1,2,3,4,7},{1,7,8,9,10,2,3,4,5,6},{2,7,8,9,1 Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 83 0,1,3,4,5,6},{3,7,8,9,10,1,2,4,5,6},{4,7,8,9,10,1,2,3,5,6},{5,7,8,9,10,1,2,3,4,6},{6, 7,8,9,10,1,2,3,4,5}} wP12_2_10.dat M1=[[1,2,3,4,5,6,7,8,9,10,11,12],[1,3,2,4,5,6,7,8,9,10,11,12],[2,3,1,4,5,6,7,8,9,10,1 1,12],[1,4,2,3,5,6,7,8,9,10,11,12],[2,4,1,3,5,6,7,8,9,10,11,12],[3,4,1,2,5,6,7,8,9,10 ,11,12],[1,5,2,3,4,6,7,8,9,10,11,12],[2,5,1,3,4,6,7,8,9,10,11,12],[3,5,1,2,4,6,7,8,9, 10,11,12],[4,5,1,2,3,6,7,8,9,10,11,12],[1,6,2,3,4,5,7,8,9,10,11,12],[2,6,1,3,4,5,7,8, 9,10,11,12],[3,6,1,2,4,5,7,8,9,10,11,12],[4,6,1,2,3,5,7,8,9,10,11,12],[5,6,1,2,3,4,7, 8,9,10,11,12],[1,7,2,3,4,5,6,8,9,10,11,12],[2,7,1,3,4,5,6,8,9,10,11,12],[3,7,1,2,4,5, 6,8,9,10,11,12],[4,7,1,2,3,5,6,8,9,10,11,12],[5,7,1,2,3,4,6,8,9,10,11,12],[6,7,1,2,3, 4,5,8,9,10,11,12],[1,8,2,3,4,5,6,7,9,10,11,12],[2,8,1,3,4,5,6,7,9,10,11,12],[3,8,1,2, 4,5,6,7,9,10,11,12],[4,8,1,2,3,5,6,7,9,10,11,12],[5,8,1,2,3,4,6,7,9,10,11,12],[6,8,1, 2,3,4,5,7,9,10,11,12],[7,8,1,2,3,4,5,6,9,10,11,12],[1,9,2,3,4,5,6,7,8,10,11,12],[2,9, 1,3,4,5,6,7,8,10,11,12],[3,9,1,2,4,5,6,7,8,10,11,12],[4,9,1,2,3,5,6,7,8,10,11,12],[5, 9,1,2,3,4,6,7,8,10,11,12],[6,9,1,2,3,4,5,7,8,10,11,12],[7,9,1,2,3,4,5,6,8,10,11,12],[ 8,9,1,2,3,4,5,6,7,10,11,12],[1,10,2,3,4,5,6,7,8,9,11,12],[2,10,1,3,4,5,6,7,8,9,11,12] ,[3,10,1,2,4,5,6,7,8,9,11,12],[4,10,1,2,3,5,6,7,8,9,11,12],[5,10,1,2,3,4,6,7,8,9,11,1 2],[6,10,1,2,3,4,5,7,8,9,11,12],[7,10,1,2,3,4,5,6,8,9,11,12],[8,10,1,2,3,4,5,6,7,9,11 ,12],[9,10,1,2,3,4,5,6,7,8,11,12],[1,11,2,3,4,5,6,7,8,9,10,12],[2,11,1,3,4,5,6,7,8,9, 10,12],[3,11,1,2,4,5,6,7,8,9,10,12],[4,11,1,2,3,5,6,7,8,9,10,12],[5,11,1,2,3,4,6,7,8, 9,10,12],[6,11,1,2,3,4,5,7,8,9,10,12],[7,11,1,2,3,4,5,6,8,9,10,12],[8,11,1,2,3,4,5,6, 7,9,10,12],[9,11,1,2,3,4,5,6,7,8,10,12],[10,11,1,2,3,4,5,6,7,8,9,12],[1,12,2,3,4,5,6, 7,8,9,10,11],[2,12,1,3,4,5,6,7,8,9,10,11],[3,12,1,2,4,5,6,7,8,9,10,11],[4,12,1,2,3,5, 6,7,8,9,10,11],[5,12,1,2,3,4,6,7,8,9,10,11],[6,12,1,2,3,4,5,7,8,9,10,11],[7,12,1,2,3, 4,5,6,8,9,10,11],[8,12,1,2,3,4,5,6,7,9,10,11],[9,12,1,2,3,4,5,6,7,8,10,11],[10,12,1,2 ,3,4,5,6,7,8,9,11],[11,12,1,2,3,4,5,6,7,8,9,10]] L1={{1,2,3,4,5,6,7,8,9,10,11,12},{1,3,2,4,5,6,7,8,9,10,11,12},{2,3,1,4,5,6,7,8,9,10,1 1,12},{1,4,2,3,5,6,7,8,9,10,11,12},{2,4,1,3,5,6,7,8,9,10,11,12},{3,4,1,2,5,6,7,8,9,10 ,11,12},{1,5,2,3,4,6,7,8,9,10,11,12},{2,5,1,3,4,6,7,8,9,10,11,12},{3,5,1,2,4,6,7,8,9, 10,11,12},{4,5,1,2,3,6,7,8,9,10,11,12},{1,6,2,3,4,5,7,8,9,10,11,12},{2,6,1,3,4,5,7,8, 9,10,11,12},{3,6,1,2,4,5,7,8,9,10,11,12},{4,6,1,2,3,5,7,8,9,10,11,12},{5,6,1,2,3,4,7, 8,9,10,11,12},{1,7,2,3,4,5,6,8,9,10,11,12},{2,7,1,3,4,5,6,8,9,10,11,12},{3,7,1,2,4,5, 6,8,9,10,11,12},{4,7,1,2,3,5,6,8,9,10,11,12},{5,7,1,2,3,4,6,8,9,10,11,12},{6,7,1,2,3, 4,5,8,9,10,11,12},{1,8,2,3,4,5,6,7,9,10,11,12},{2,8,1,3,4,5,6,7,9,10,11,12},{3,8,1,2, 4,5,6,7,9,10,11,12},{4,8,1,2,3,5,6,7,9,10,11,12},{5,8,1,2,3,4,6,7,9,10,11,12},{6,8,1, 2,3,4,5,7,9,10,11,12},{7,8,1,2,3,4,5,6,9,10,11,12},{1,9,2,3,4,5,6,7,8,10,11,12},{2,9, 1,3,4,5,6,7,8,10,11,12},{3,9,1,2,4,5,6,7,8,10,11,12},{4,9,1,2,3,5,6,7,8,10,11,12},{5, 9,1,2,3,4,6,7,8,10,11,12},{6,9,1,2,3,4,5,7,8,10,11,12},{7,9,1,2,3,4,5,6,8,10,11,12},{ 8,9,1,2,3,4,5,6,7,10,11,12},{1,10,2,3,4,5,6,7,8,9,11,12},{2,10,1,3,4,5,6,7,8,9,11,12} ,{3,10,1,2,4,5,6,7,8,9,11,12},{4,10,1,2,3,5,6,7,8,9,11,12},{5,10,1,2,3,4,6,7,8,9,11,1 2},{6,10,1,2,3,4,5,7,8,9,11,12},{7,10,1,2,3,4,5,6,8,9,11,12},{8,10,1,2,3,4,5,6,7,9,11 ,12},{9,10,1,2,3,4,5,6,7,8,11,12},{1,11,2,3,4,5,6,7,8,9,10,12},{2,11,1,3,4,5,6,7,8,9, 10,12},{3,11,1,2,4,5,6,7,8,9,10,12},{4,11,1,2,3,5,6,7,8,9,10,12},{5,11,1,2,3,4,6,7,8, 9,10,12},{6,11,1,2,3,4,5,7,8,9,10,12},{7,11,1,2,3,4,5,6,8,9,10,12},{8,11,1,2,3,4,5,6, 7,9,10,12},{9,11,1,2,3,4,5,6,7,8,10,12},{10,11,1,2,3,4,5,6,7,8,9,12},{1,12,2,3,4,5,6, 7,8,9,10,11},{2,12,1,3,4,5,6,7,8,9,10,11},{3,12,1,2,4,5,6,7,8,9,10,11},{4,12,1,2,3,5, 6,7,8,9,10,11},{5,12,1,2,3,4,6,7,8,9,10,11},{6,12,1,2,3,4,5,7,8,9,10,11},{7,12,1,2,3, 4,5,6,8,9,10,11},{8,12,1,2,3,4,5,6,7,9,10,11},{9,12,1,2,3,4,5,6,7,8,10,11},{10,12,1,2 ,3,4,5,6,7,8,9,11},{11,12,1,2,3,4,5,6,7,8,9,10}} wV2_3.dat M1=[[-1,-1,-1],[-1,-1,1],[-1,1,-1],[-1,1,1],[1,-1,-1],[1,-1,1],[1,1,-1],[1,1,1]] L1={{-1,-1,-1},{-1,-1,1},{-1,1,-1},{-1,1,1},{1,-1,-1},{1,-1,1},{1,1,-1},{1,1,1}} wV2_9.dat M1=[[-1,-1,-1,-1,-1,-1,-1,-1,-1],[-1,- 1,-1,-1,-1,-1,-1,-1,1],[-1,-1,-1,-1,- 1,-1,-1,1,-1],[-1,-1,-1,-1,-1,-1,- 1,1,1],[-1,-1,-1,-1,-1,-1,1,-1,-1],[- 1,-1,-1,-1,-1,-1,1,-1,1],[-1,-1,-1,-1,- 1,-1,1,1,-1],[-1,-1,-1,-1,-1,- 1,1,1,1],[-1,-1,-1,-1,-1,1,-1,-1,-1],[- 1,-1,-1,-1,-1,1,-1,-1,1],[-1,-1,-1,-1,- 1,1,-1,1,-1],[-1,-1,-1,-1,-1,1,- 1,1,1],[-1,-1,-1,-1,-1,1,1,-1,-1],[-1,- 1,-1,-1,-1,1,1,-1,1],[-1,-1,-1,-1,- 1,1,1,1,-1],[-1,-1,-1,-1,-1,1,1,1,1],[- 1,-1,-1,-1,1,-1,-1,-1,-1],[-1,-1,-1,- 1,1,-1,-1,-1,1],[-1,-1,-1,-1,1,-1,- 1,1,-1],[-1,-1,-1,-1,1,-1,-1,1,1],[-1,- 1,-1,-1,1,-1,1,-1,-1],[-1,-1,-1,-1,1,- 1,1,-1,1],[-1,-1,-1,-1,1,-1,1,1,-1],[- 1,-1,-1,-1,1,-1,1,1,1],[-1,-1,-1,- 1,1,1,-1,-1,-1],[-1,-1,-1,-1,1,1,-1,- 1,1],[-1,-1,-1,-1,1,1,-1,1,-1],[-1,-1,- 1,-1,1,1,-1,1,1],[-1,-1,-1,-1,1,1,1,- 1,-1],[-1,-1,-1,-1,1,1,1,-1,1],[-1,-1,- 1,-1,1,1,1,1,-1],[-1,-1,-1,- 1,1,1,1,1,1],[-1,-1,-1,1,-1,-1,-1,-1,- 1],[-1,-1,-1,1,-1,-1,-1,-1,1],[-1,-1,- 1,1,-1,-1,-1,1,-1],[-1,-1,-1,1,-1,-1,- 1,1,1],[-1,-1,-1,1,-1,-1,1,-1,-1],[-1,- 1,-1,1,-1,-1,1,-1,1],[-1,-1,-1,1,-1,- 1,1,1,-1],[-1,-1,-1,1,-1,-1,1,1,1],[- 1,-1,-1,1,-1,1,-1,-1,-1],[-1,-1,-1,1,- 1,1,-1,-1,1],[-1,-1,-1,1,-1,1,-1,1,- 1],[-1,-1,-1,1,-1,1,-1,1,1],[-1,-1,- 1,1,-1,1,1,-1,-1],[-1,-1,-1,1,-1,1,1,- 1,1],[-1,-1,-1,1,-1,1,1,1,-1],[-1,-1,- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 84 1,1,-1,1,1,1,1],[-1,-1,-1,1,1,-1,-1,- 1,-1],[-1,-1,-1,1,1,-1,-1,-1,1],[-1,- 1,-1,1,1,-1,-1,1,-1],[-1,-1,-1,1,1,-1,- 1,1,1],[-1,-1,-1,1,1,-1,1,-1,-1],[-1,- 1,-1,1,1,-1,1,-1,1],[-1,-1,-1,1,1,- 1,1,1,-1],[-1,-1,-1,1,1,-1,1,1,1],[-1,- 1,-1,1,1,1,-1,-1,-1],[-1,-1,-1,1,1,1,- 1,-1,1],[-1,-1,-1,1,1,1,-1,1,-1],[-1,- 1,-1,1,1,1,-1,1,1],[-1,-1,-1,1,1,1,1,- 1,-1],[-1,-1,-1,1,1,1,1,-1,1],[-1,-1,- 1,1,1,1,1,1,-1],[-1,-1,- 1,1,1,1,1,1,1],[-1,-1,1,-1,-1,-1,-1,- 1,-1],[-1,-1,1,-1,-1,-1,-1,-1,1],[-1,- 1,1,-1,-1,-1,-1,1,-1],[-1,-1,1,-1,-1,- 1,-1,1,1],[-1,-1,1,-1,-1,-1,1,-1,-1],[- 1,-1,1,-1,-1,-1,1,-1,1],[-1,-1,1,-1,- 1,-1,1,1,-1],[-1,-1,1,-1,-1,- 1,1,1,1],[-1,-1,1,-1,-1,1,-1,-1,-1],[- 1,-1,1,-1,-1,1,-1,-1,1],[-1,-1,1,-1,- 1,1,-1,1,-1],[-1,-1,1,-1,-1,1,- 1,1,1],[-1,-1,1,-1,-1,1,1,-1,-1],[-1,- 1,1,-1,-1,1,1,-1,1],[-1,-1,1,-1,- 1,1,1,1,-1],[-1,-1,1,-1,-1,1,1,1,1],[- 1,-1,1,-1,1,-1,-1,-1,-1],[-1,-1,1,- 1,1,-1,-1,-1,1],[-1,-1,1,-1,1,-1,-1,1,- 1],[-1,-1,1,-1,1,-1,-1,1,1],[-1,-1,1,- 1,1,-1,1,-1,-1],[-1,-1,1,-1,1,-1,1,- 1,1],[-1,-1,1,-1,1,-1,1,1,-1],[-1,- 1,1,-1,1,-1,1,1,1],[-1,-1,1,-1,1,1,-1,- 1,-1],[-1,-1,1,-1,1,1,-1,-1,1],[-1,- 1,1,-1,1,1,-1,1,-1],[-1,-1,1,-1,1,1,- 1,1,1],[-1,-1,1,-1,1,1,1,-1,-1],[-1,- 1,1,-1,1,1,1,-1,1],[-1,-1,1,- 1,1,1,1,1,-1],[-1,-1,1,-1,1,1,1,1,1],[- 1,-1,1,1,-1,-1,-1,-1,-1],[-1,-1,1,1,- 1,-1,-1,-1,1],[-1,-1,1,1,-1,-1,-1,1,- 1],[-1,-1,1,1,-1,-1,-1,1,1],[-1,- 1,1,1,-1,-1,1,-1,-1],[-1,-1,1,1,-1,- 1,1,-1,1],[-1,-1,1,1,-1,-1,1,1,-1],[- 1,-1,1,1,-1,-1,1,1,1],[-1,-1,1,1,-1,1,- 1,-1,-1],[-1,-1,1,1,-1,1,-1,-1,1],[-1,- 1,1,1,-1,1,-1,1,-1],[-1,-1,1,1,-1,1,- 1,1,1],[-1,-1,1,1,-1,1,1,-1,-1],[-1,- 1,1,1,-1,1,1,-1,1],[-1,-1,1,1,- 1,1,1,1,-1],[-1,-1,1,1,-1,1,1,1,1],[- 1,-1,1,1,1,-1,-1,-1,-1],[-1,-1,1,1,1,- 1,-1,-1,1],[-1,-1,1,1,1,-1,-1,1,-1],[- 1,-1,1,1,1,-1,-1,1,1],[-1,-1,1,1,1,- 1,1,-1,-1],[-1,-1,1,1,1,-1,1,-1,1],[- 1,-1,1,1,1,-1,1,1,-1],[-1,-1,1,1,1,- 1,1,1,1],[-1,-1,1,1,1,1,-1,-1,-1],[-1,- 1,1,1,1,1,-1,-1,1],[-1,-1,1,1,1,1,- 1,1,-1],[-1,-1,1,1,1,1,-1,1,1],[-1,- 1,1,1,1,1,1,-1,-1],[-1,-1,1,1,1,1,1,- 1,1],[-1,-1,1,1,1,1,1,1,-1],[-1,- 1,1,1,1,1,1,1,1],[-1,1,-1,-1,-1,-1,-1,- 1,-1],[-1,1,-1,-1,-1,-1,-1,-1,1],[- 1,1,-1,-1,-1,-1,-1,1,-1],[-1,1,-1,-1,- 1,-1,-1,1,1],[-1,1,-1,-1,-1,-1,1,-1,- 1],[-1,1,-1,-1,-1,-1,1,-1,1],[-1,1,-1,- 1,-1,-1,1,1,-1],[-1,1,-1,-1,-1,- 1,1,1,1],[-1,1,-1,-1,-1,1,-1,-1,-1],[- 1,1,-1,-1,-1,1,-1,-1,1],[-1,1,-1,-1,- 1,1,-1,1,-1],[-1,1,-1,-1,-1,1,- 1,1,1],[-1,1,-1,-1,-1,1,1,-1,-1],[- 1,1,-1,-1,-1,1,1,-1,1],[-1,1,-1,-1,- 1,1,1,1,-1],[-1,1,-1,-1,-1,1,1,1,1],[- 1,1,-1,-1,1,-1,-1,-1,-1],[-1,1,-1,- 1,1,-1,-1,-1,1],[-1,1,-1,-1,1,-1,-1,1,- 1],[-1,1,-1,-1,1,-1,-1,1,1],[-1,1,-1,- 1,1,-1,1,-1,-1],[-1,1,-1,-1,1,-1,1,- 1,1],[-1,1,-1,-1,1,-1,1,1,-1],[-1,1,- 1,-1,1,-1,1,1,1],[-1,1,-1,-1,1,1,-1,- 1,-1],[-1,1,-1,-1,1,1,-1,-1,1],[-1,1,- 1,-1,1,1,-1,1,-1],[-1,1,-1,-1,1,1,- 1,1,1],[-1,1,-1,-1,1,1,1,-1,-1],[-1,1,- 1,-1,1,1,1,-1,1],[-1,1,-1,-1,1,1,1,1,- 1],[-1,1,-1,-1,1,1,1,1,1],[-1,1,-1,1,- 1,-1,-1,-1,-1],[-1,1,-1,1,-1,-1,-1,- 1,1],[-1,1,-1,1,-1,-1,-1,1,-1],[-1,1,- 1,1,-1,-1,-1,1,1],[-1,1,-1,1,-1,-1,1,- 1,-1],[-1,1,-1,1,-1,-1,1,-1,1],[-1,1,- 1,1,-1,-1,1,1,-1],[-1,1,-1,1,-1,- 1,1,1,1],[-1,1,-1,1,-1,1,-1,-1,-1],[- 1,1,-1,1,-1,1,-1,-1,1],[-1,1,-1,1,- 1,1,-1,1,-1],[-1,1,-1,1,-1,1,-1,1,1],[- 1,1,-1,1,-1,1,1,-1,-1],[-1,1,-1,1,- 1,1,1,-1,1],[-1,1,-1,1,-1,1,1,1,-1],[- 1,1,-1,1,-1,1,1,1,1],[-1,1,-1,1,1,-1,- 1,-1,-1],[-1,1,-1,1,1,-1,-1,-1,1],[- 1,1,-1,1,1,-1,-1,1,-1],[-1,1,-1,1,1,- 1,-1,1,1],[-1,1,-1,1,1,-1,1,-1,-1],[- 1,1,-1,1,1,-1,1,-1,1],[-1,1,-1,1,1,- 1,1,1,-1],[-1,1,-1,1,1,-1,1,1,1],[- 1,1,-1,1,1,1,-1,-1,-1],[-1,1,-1,1,1,1,- 1,-1,1],[-1,1,-1,1,1,1,-1,1,-1],[-1,1,- 1,1,1,1,-1,1,1],[-1,1,-1,1,1,1,1,-1,- 1],[-1,1,-1,1,1,1,1,-1,1],[-1,1,- 1,1,1,1,1,1,-1],[-1,1,- 1,1,1,1,1,1,1],[-1,1,1,-1,-1,-1,-1,-1,- 1],[-1,1,1,-1,-1,-1,-1,-1,1],[-1,1,1,- 1,-1,-1,-1,1,-1],[-1,1,1,-1,-1,-1,- 1,1,1],[-1,1,1,-1,-1,-1,1,-1,-1],[- 1,1,1,-1,-1,-1,1,-1,1],[-1,1,1,-1,-1,- 1,1,1,-1],[-1,1,1,-1,-1,-1,1,1,1],[- 1,1,1,-1,-1,1,-1,-1,-1],[-1,1,1,-1,- 1,1,-1,-1,1],[-1,1,1,-1,-1,1,-1,1,- 1],[-1,1,1,-1,-1,1,-1,1,1],[-1,1,1,-1,- 1,1,1,-1,-1],[-1,1,1,-1,-1,1,1,-1,1],[- 1,1,1,-1,-1,1,1,1,-1],[-1,1,1,-1,- 1,1,1,1,1],[-1,1,1,-1,1,-1,-1,-1,-1],[- 1,1,1,-1,1,-1,-1,-1,1],[-1,1,1,-1,1,- 1,-1,1,-1],[-1,1,1,-1,1,-1,-1,1,1],[- 1,1,1,-1,1,-1,1,-1,-1],[-1,1,1,-1,1,- 1,1,-1,1],[-1,1,1,-1,1,-1,1,1,-1],[- 1,1,1,-1,1,-1,1,1,1],[-1,1,1,-1,1,1,- 1,-1,-1],[-1,1,1,-1,1,1,-1,-1,1],[- 1,1,1,-1,1,1,-1,1,-1],[-1,1,1,-1,1,1,- 1,1,1],[-1,1,1,-1,1,1,1,-1,-1],[- 1,1,1,-1,1,1,1,-1,1],[-1,1,1,- 1,1,1,1,1,-1],[-1,1,1,-1,1,1,1,1,1],[- 1,1,1,1,-1,-1,-1,-1,-1],[-1,1,1,1,-1,- 1,-1,-1,1],[-1,1,1,1,-1,-1,-1,1,-1],[- 1,1,1,1,-1,-1,-1,1,1],[-1,1,1,1,-1,- 1,1,-1,-1],[-1,1,1,1,-1,-1,1,-1,1],[- 1,1,1,1,-1,-1,1,1,-1],[-1,1,1,1,-1,- 1,1,1,1],[-1,1,1,1,-1,1,-1,-1,-1],[- 1,1,1,1,-1,1,-1,-1,1],[-1,1,1,1,-1,1,- 1,1,-1],[-1,1,1,1,-1,1,-1,1,1],[- 1,1,1,1,-1,1,1,-1,-1],[-1,1,1,1,- 1,1,1,-1,1],[-1,1,1,1,-1,1,1,1,-1],[- 1,1,1,1,-1,1,1,1,1],[-1,1,1,1,1,-1,-1,- 1,-1],[-1,1,1,1,1,-1,-1,-1,1],[- 1,1,1,1,1,-1,-1,1,-1],[-1,1,1,1,1,-1,- 1,1,1],[-1,1,1,1,1,-1,1,-1,-1],[- 1,1,1,1,1,-1,1,-1,1],[-1,1,1,1,1,- 1,1,1,-1],[-1,1,1,1,1,-1,1,1,1],[- 1,1,1,1,1,1,-1,-1,-1],[-1,1,1,1,1,1,- 1,-1,1],[-1,1,1,1,1,1,-1,1,-1],[- 1,1,1,1,1,1,-1,1,1],[-1,1,1,1,1,1,1,- 1,-1],[-1,1,1,1,1,1,1,-1,1],[- 1,1,1,1,1,1,1,1,-1],[- 1,1,1,1,1,1,1,1,1],[1,-1,-1,-1,-1,-1,- 1,-1,-1],[1,-1,-1,-1,-1,-1,-1,- 1,1],[1,-1,-1,-1,-1,-1,-1,1,-1],[1,-1,- 1,-1,-1,-1,-1,1,1],[1,-1,-1,-1,-1,- 1,1,-1,-1],[1,-1,-1,-1,-1,-1,1,- 1,1],[1,-1,-1,-1,-1,-1,1,1,-1],[1,-1,- 1,-1,-1,-1,1,1,1],[1,-1,-1,-1,-1,1,-1,- 1,-1],[1,-1,-1,-1,-1,1,-1,-1,1],[1,-1,- 1,-1,-1,1,-1,1,-1],[1,-1,-1,-1,-1,1,- 1,1,1],[1,-1,-1,-1,-1,1,1,-1,-1],[1,- 1,-1,-1,-1,1,1,-1,1],[1,-1,-1,-1,- 1,1,1,1,-1],[1,-1,-1,-1,- 1,1,1,1,1],[1,-1,-1,-1,1,-1,-1,-1,- 1],[1,-1,-1,-1,1,-1,-1,-1,1],[1,-1,-1,- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 85 1,1,-1,-1,1,-1],[1,-1,-1,-1,1,-1,- 1,1,1],[1,-1,-1,-1,1,-1,1,-1,-1],[1,- 1,-1,-1,1,-1,1,-1,1],[1,-1,-1,-1,1,- 1,1,1,-1],[1,-1,-1,-1,1,-1,1,1,1],[1,- 1,-1,-1,1,1,-1,-1,-1],[1,-1,-1,-1,1,1,- 1,-1,1],[1,-1,-1,-1,1,1,-1,1,-1],[1,- 1,-1,-1,1,1,-1,1,1],[1,-1,-1,-1,1,1,1,- 1,-1],[1,-1,-1,-1,1,1,1,-1,1],[1,-1,- 1,-1,1,1,1,1,-1],[1,-1,-1,- 1,1,1,1,1,1],[1,-1,-1,1,-1,-1,-1,-1,- 1],[1,-1,-1,1,-1,-1,-1,-1,1],[1,-1,- 1,1,-1,-1,-1,1,-1],[1,-1,-1,1,-1,-1,- 1,1,1],[1,-1,-1,1,-1,-1,1,-1,-1],[1,- 1,-1,1,-1,-1,1,-1,1],[1,-1,-1,1,-1,- 1,1,1,-1],[1,-1,-1,1,-1,-1,1,1,1],[1,- 1,-1,1,-1,1,-1,-1,-1],[1,-1,-1,1,-1,1,- 1,-1,1],[1,-1,-1,1,-1,1,-1,1,-1],[1,- 1,-1,1,-1,1,-1,1,1],[1,-1,-1,1,-1,1,1,- 1,-1],[1,-1,-1,1,-1,1,1,-1,1],[1,-1,- 1,1,-1,1,1,1,-1],[1,-1,-1,1,- 1,1,1,1,1],[1,-1,-1,1,1,-1,-1,-1,- 1],[1,-1,-1,1,1,-1,-1,-1,1],[1,-1,- 1,1,1,-1,-1,1,-1],[1,-1,-1,1,1,-1,- 1,1,1],[1,-1,-1,1,1,-1,1,-1,-1],[1,-1,- 1,1,1,-1,1,-1,1],[1,-1,-1,1,1,-1,1,1,- 1],[1,-1,-1,1,1,-1,1,1,1],[1,-1,- 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1,1,1,1],[1,1,1,-1,1,1,-1,-1,- 1],[1,1,1,-1,1,1,-1,-1,1],[1,1,1,- 1,1,1,-1,1,-1],[1,1,1,-1,1,1,- 1,1,1],[1,1,1,-1,1,1,1,-1,-1],[1,1,1,- 1,1,1,1,-1,1],[1,1,1,-1,1,1,1,1,- 1],[1,1,1,-1,1,1,1,1,1],[1,1,1,1,-1,- 1,-1,-1,-1],[1,1,1,1,-1,-1,-1,- 1,1],[1,1,1,1,-1,-1,-1,1,-1],[1,1,1,1,- 1,-1,-1,1,1],[1,1,1,1,-1,-1,1,-1,- 1],[1,1,1,1,-1,-1,1,-1,1],[1,1,1,1,-1,- 1,1,1,-1],[1,1,1,1,-1,- 1,1,1,1],[1,1,1,1,-1,1,-1,-1,- 1],[1,1,1,1,-1,1,-1,-1,1],[1,1,1,1,- 1,1,-1,1,-1],[1,1,1,1,-1,1,- 1,1,1],[1,1,1,1,-1,1,1,-1,- 1],[1,1,1,1,-1,1,1,-1,1],[1,1,1,1,- 1,1,1,1,-1],[1,1,1,1,- 1,1,1,1,1],[1,1,1,1,1,-1,-1,-1,- 1],[1,1,1,1,1,-1,-1,-1,1],[1,1,1,1,1,- 1,-1,1,-1],[1,1,1,1,1,-1,- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. 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G. (2025). HP_Prime_MATH: Manual. 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G. (2025). HP_Prime_MATH: Manual. 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1,-1,1,-1,-1,1,-1,-1},{-1,1,-1,-1,1,- 1,-1,1,-1,1},{-1,1,-1,-1,1,-1,-1,1,1,- 1},{-1,1,-1,-1,1,-1,-1,1,1,1},{-1,1,- 1,-1,1,-1,1,-1,-1,-1},{-1,1,-1,-1,1,- 1,1,-1,-1,1},{-1,1,-1,-1,1,-1,1,-1,1,- 1},{-1,1,-1,-1,1,-1,1,-1,1,1},{-1,1,- 1,-1,1,-1,1,1,-1,-1},{-1,1,-1,-1,1,- 1,1,1,-1,1},{-1,1,-1,-1,1,-1,1,1,1,- 1},{-1,1,-1,-1,1,-1,1,1,1,1},{-1,1,-1,- 1,1,1,-1,-1,-1,-1},{-1,1,-1,-1,1,1,-1,- 1,-1,1},{-1,1,-1,-1,1,1,-1,-1,1,-1},{- 1,1,-1,-1,1,1,-1,-1,1,1},{-1,1,-1,- 1,1,1,-1,1,-1,-1},{-1,1,-1,-1,1,1,- 1,1,-1,1},{-1,1,-1,-1,1,1,-1,1,1,-1},{- 1,1,-1,-1,1,1,-1,1,1,1},{-1,1,-1,- 1,1,1,1,-1,-1,-1},{-1,1,-1,-1,1,1,1,- 1,-1,1},{-1,1,-1,-1,1,1,1,-1,1,-1},{- 1,1,-1,-1,1,1,1,-1,1,1},{-1,1,-1,- 1,1,1,1,1,-1,-1},{-1,1,-1,-1,1,1,1,1,- 1,1},{-1,1,-1,-1,1,1,1,1,1,-1},{-1,1,- 1,-1,1,1,1,1,1,1},{-1,1,-1,1,-1,-1,-1,- 1,-1,-1},{-1,1,-1,1,-1,-1,-1,-1,- 1,1},{-1,1,-1,1,-1,-1,-1,-1,1,-1},{- 1,1,-1,1,-1,-1,-1,-1,1,1},{-1,1,-1,1,- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. 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1,1,1,-1},{-1,1,1,1,1,1,-1,1,1,1},{- 1,1,1,1,1,1,1,-1,-1,-1},{- 1,1,1,1,1,1,1,-1,-1,1},{- 1,1,1,1,1,1,1,-1,1,-1},{- 1,1,1,1,1,1,1,-1,1,1},{- 1,1,1,1,1,1,1,1,-1,-1},{- 1,1,1,1,1,1,1,1,-1,1},{- 1,1,1,1,1,1,1,1,1,-1},{- 1,1,1,1,1,1,1,1,1,1},{1,-1,-1,-1,-1,- 1,-1,-1,-1,-1},{1,-1,-1,-1,-1,-1,-1,- 1,-1,1},{1,-1,-1,-1,-1,-1,-1,-1,1,- 1},{1,-1,-1,-1,-1,-1,-1,-1,1,1},{1,-1,- 1,-1,-1,-1,-1,1,-1,-1},{1,-1,-1,-1,-1,- 1,-1,1,-1,1},{1,-1,-1,-1,-1,-1,-1,1,1,- 1},{1,-1,-1,-1,-1,-1,-1,1,1,1},{1,-1,- 1,-1,-1,-1,1,-1,-1,-1},{1,-1,-1,-1,-1,- 1,1,-1,-1,1},{1,-1,-1,-1,-1,-1,1,-1,1,- 1},{1,-1,-1,-1,-1,-1,1,-1,1,1},{1,-1,- 1,-1,-1,-1,1,1,-1,-1},{1,-1,-1,-1,-1,- 1,1,1,-1,1},{1,-1,-1,-1,-1,-1,1,1,1,- 1},{1,-1,-1,-1,-1,-1,1,1,1,1},{1,-1,- 1,-1,-1,1,-1,-1,-1,-1},{1,-1,-1,-1,- 1,1,-1,-1,-1,1},{1,-1,-1,-1,-1,1,-1,- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. OL60452436M 96 1,1,-1},{1,-1,-1,-1,-1,1,-1,- 1,1,1},{1,-1,-1,-1,-1,1,-1,1,-1,- 1},{1,-1,-1,-1,-1,1,-1,1,-1,1},{1,-1,- 1,-1,-1,1,-1,1,1,-1},{1,-1,-1,-1,-1,1,- 1,1,1,1},{1,-1,-1,-1,-1,1,1,-1,-1,- 1},{1,-1,-1,-1,-1,1,1,-1,-1,1},{1,-1,- 1,-1,-1,1,1,-1,1,-1},{1,-1,-1,-1,- 1,1,1,-1,1,1},{1,-1,-1,-1,-1,1,1,1,-1,- 1},{1,-1,-1,-1,-1,1,1,1,-1,1},{1,-1,- 1,-1,-1,1,1,1,1,-1},{1,-1,-1,-1,- 1,1,1,1,1,1},{1,-1,-1,-1,1,-1,-1,-1,- 1,-1},{1,-1,-1,-1,1,-1,-1,-1,-1,1},{1,- 1,-1,-1,1,-1,-1,-1,1,-1},{1,-1,-1,- 1,1,-1,-1,-1,1,1},{1,-1,-1,-1,1,-1,- 1,1,-1,-1},{1,-1,-1,-1,1,-1,-1,1,- 1,1},{1,-1,-1,-1,1,-1,-1,1,1,-1},{1,- 1,-1,-1,1,-1,-1,1,1,1},{1,-1,-1,-1,1,- 1,1,-1,-1,-1},{1,-1,-1,-1,1,-1,1,-1,- 1,1},{1,-1,-1,-1,1,-1,1,-1,1,-1},{1,- 1,-1,-1,1,-1,1,-1,1,1},{1,-1,-1,-1,1,- 1,1,1,-1,-1},{1,-1,-1,-1,1,-1,1,1,- 1,1},{1,-1,-1,-1,1,-1,1,1,1,-1},{1,-1,- 1,-1,1,-1,1,1,1,1},{1,-1,-1,-1,1,1,-1,- 1,-1,-1},{1,-1,-1,-1,1,1,-1,-1,- 1,1},{1,-1,-1,-1,1,1,-1,-1,1,-1},{1,- 1,-1,-1,1,1,-1,-1,1,1},{1,-1,-1,- 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1,-1,-1,1,1},{1,-1,1,1,-1,-1,-1,1,-1,- 1},{1,-1,1,1,-1,-1,-1,1,-1,1},{1,- 1,1,1,-1,-1,-1,1,1,-1},{1,-1,1,1,-1,- 1,-1,1,1,1},{1,-1,1,1,-1,-1,1,-1,-1,- 1},{1,-1,1,1,-1,-1,1,-1,-1,1},{1,- 1,1,1,-1,-1,1,-1,1,-1},{1,-1,1,1,-1,- 1,1,-1,1,1},{1,-1,1,1,-1,-1,1,1,-1,- 1},{1,-1,1,1,-1,-1,1,1,-1,1},{1,- 1,1,1,-1,-1,1,1,1,-1},{1,-1,1,1,-1,- 1,1,1,1,1},{1,-1,1,1,-1,1,-1,-1,-1,- 1},{1,-1,1,1,-1,1,-1,-1,-1,1},{1,- 1,1,1,-1,1,-1,-1,1,-1},{1,-1,1,1,-1,1,- 1,-1,1,1},{1,-1,1,1,-1,1,-1,1,-1,- 1},{1,-1,1,1,-1,1,-1,1,-1,1},{1,- 1,1,1,-1,1,-1,1,1,-1},{1,-1,1,1,-1,1,- 1,1,1,1},{1,-1,1,1,-1,1,1,-1,-1,- 1},{1,-1,1,1,-1,1,1,-1,-1,1},{1,- 1,1,1,-1,1,1,-1,1,-1},{1,-1,1,1,- 1,1,1,-1,1,1},{1,-1,1,1,-1,1,1,1,-1,- 1},{1,-1,1,1,-1,1,1,1,-1,1},{1,-1,1,1,- 1,1,1,1,1,-1},{1,-1,1,1,- 1,1,1,1,1,1},{1,-1,1,1,1,-1,-1,-1,-1,- 1},{1,-1,1,1,1,-1,-1,-1,-1,1},{1,- Schrausser, D. G. (2025). HP_Prime_MATH: Manual. 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