Comparing lethal dose ratios using probit regression with arbitrary slopes

Table 3 Goodness-of-fit of the probit-log(dose) regression models calculated from the example data using the ML procedure (Excel), Polo-Plus and SPSS

Examples	χ ²				h ^b				g ^c
Examples	Excel	Polo-Plus	SPSS^1a	SPSS^2a	Excel	Polo-Plus	SPSS^1a	SPSS^2a	Excel
Rotenone	1.729	1.729	1.729	1.729	0.576	0.576	0.576	0.576	0.050
Deguelin	12.026^d	12.026^d	12.026^d	12.026^d	3.006	3.006	3.006	3.006	0.260
Mixture	4.995	4.995	4.995	4.995	1.249	1.249	1.249	1.249	0.043
Fairfax	3.754	3.754	3.754	3.754	1.251	1.251	1.251	1.251	0.071
Schaefer	11.384^d	11.384^d	11.384^d	11.384^d	3.795	3.795	3.795	3.795	0.384
Pixley ^e	2.671	2.708	2.712	0.064	1.335	1.354	1.356	0.032	0.069
BugRes	1.382	1.358	1.362	1.266	0.461	0.453	0.454	0.633	0.094
BugLab	13.555	11.081	27.454	10.181	1.936	1.583	3.922	1.697	0.325

^aSPSS includes the natural responses proportion by inputting the value of C, and SPSS calculates the corrected p from the data
^bh, heterogeneity factor (see Eq.(17)). SPSS did not give h. To compare the results from this study and Polo-Plus, it was shown as h = χ²/d.f. here
^cThe g value was calculated as Eq.(22). Polo-Plus and SPSS did not calculate the g values
^dχ² indicated the goodness-of-fit test was significant at α = 0.05
^eBold items indicated the data sets included natural responses

ISSN: 2050-6511