Need help in identifying these! Explanation will be super helpful! TIA!

Difficult and failed tracheal intubations are among the principal causes of anesthetic-related mortality and morbidity. Because a good laryngeal view facilitates successful tracheal intubation, new technologies have been introduced to improve visualization. The Pentax AWS is a novel video laryngoscope, available in Japan since 2006, which is designed to facilitate intubation by providing a video image of the glottis. Abdallah et al. conducted a study to examine whether intubation with the Pentax AWS would be easier and faster than with a standard Macintosh laryngoscope with a #4 blade. The sample included 99 adult patients having a body mass index between 30 and 50 kg/m who required orotracheal intubation for elective surgery. The variables collected included: GRP (1=Pentax AWS, 0=Macintosh #4 blade), AGE (years), GENDER (1=male, 0=female), BMI (body mass index in kg/m2), ASA (American Society of Anesthesiologists physical status where 1=I, 2=II, 3=III, 4=IV), MALLAMPATI (score predicting ease of intubation where 1=Full visibility of tonsils, uvula and soft palate; 2=Visibility of hard and soft palate, upper portion of tonsils and uvula; 3=Soft and hard palate and base of the uvula are visible; 4-Only hard palate visible), and VIEW (Cormack-Lebane grade of glottic view where 0="not good" Cormack-Lebane grade 1 or 2; 1="good" Cormack-Lebane grade 3 or 4), INTUBTIME (intubation time in seconds), and EASE (actual ease of tracheal intubation where 0=extremely easy and 100-extremely difficult). Note that patients missing data were excluded from the analysis. For questions 1-5, examine the computer output of the five different models provided for you in the separate packet labeled SAS Output #1-SAS Output #5. Match the output with the most appropriate analysis. You may select each letter (method) more than once, once, or not at all. (1 point each) 1. SAS Output #1 a. Multiple linear regression 2. SAS Output #2 b. ANCOVA 3. SAS Output #3 c. One-way ANOVA 4. SAS Output #4 d. Two-way ANOVA with an interaction 5. SAS Output #5 e. Two-way ANOVA without an interactionSAS Output #1 Sum of Source DF Squares Mean Square F Value ex > F Model A 1627.54474 D G 0.6355 Error B C E Corrected Total 97 90941.56122 R-Square Coeff Var Root MSE ease Mean 0.017897 68.08204 30.82449 45.27551 Source F Type Ill SS Mean Square F Value PR> F Mallarnosti 3 1627.544741 542.514914 G 0.6355 Standard Parameter Estimate Error t Value ex > It| Intercept 51.25000000 B 15.41224441 3.33 0.0013 Mallyvoati 1 1.39285714 B 16.26912399 -0.09 0.9320 Mallarpati 2 7.86538462 B 16.18332709 0.49 0.6281 Mallarpati 3 -11.50000000 B 16.88326785 F 0.4975 Mallampati 4 0.00000000 B Least Squares Means Adjustment for Multiple Comparisons: Tukey-Kramer LSMEAN MEVampatj ease LSMEAN Number 49.8571429 43.3846154 39.7500000 AWN 51.2500000 Least Squares Means for effect Mallargati ex> It| for HO: (SMean()=(SMeani) Dependent Variable: ease 1 2 3 0.8039 0.6473 0.9998 0.8039 0.9734 0.9620 0.6473 0.9734 0.9040 0.9998 0.9620 0.9040SAS Output #2 Sum of Source DF Squares Mean Square F Value PK > F Model 3 11088.61173 3696.20391 4.35 0.0065 Error 94 79852.94949 849.49946 Corrected Total 97 90941.56122 R-Square Coeff Var Root MSE ease Mean 0.121931 64.37514 29.14617 45.27551 Source DF Type Ill SS Mean Square F Value PR> F grp 1 1009.374636 1009.374636 1.19 0.2785 gender 1 5434.159912 5434.159912 6.40 0.0131 grp*gender 1 1280.540280 1280.540280 1.51 0.2226 Standard Parameter Estimate Error t Value ex > It| Intercept 59.54545455 B 8.78790213 6.78 <.0001 grp b gender e ii bsas output sum of source df squares mean square f value pr> F Mode 9808.07145 4904.03 5.74 0.0044 Error 81133.48978 854.03673 Corrected Total 97 90941.56122 R-Square Coeff Var Root MSE ease Mean 0.107850 64.54683 29.22391 45.27551 Source DF Type Ill SS Mean Square F Value ex > F grp 1 4360.010225 4360.010225 5.11 0.0261 gender 1 5053.676892 5053.676892 5.92 0.0169 Standard Parameter Estimate Error t Value er > It| Intercept 66.00920245 B 7.05515040 9.36 <.0001 grp b gender least squares means adjustment for multiple comparisons: tukey-kramer ho:lsmean1="LSMean2" ease lsmean ex> It| 0 43.7392638 0.0261 57.0930470 Least Squares Means Adjustment for Multiple Comparisons: Tukey-Kramer HO:LSMean1= LSMean2 gender ease LSMEAN 41.5000000 0.0169 59.3323108SAS Output #4 Sum of Source DF Squares Mean Square F Value PK > F Model 4 33339.51407 8334.87852 13.46 <.0001 error corrected total r-square coeff var root mse ease mean source df type ill ss square f value px> F grp 6610.69441 6610.69441 10.67 0.0015 age 1176.34663 1176.34663 1.90 0.1715 gender 1 742.49208 742.49208 1.20 0.2764 view 1 21482.39385 21482.39385 34.68 <.0001 standard parameter estimate error t value er> It| Intercept 38.27651851 B 12.83689741 2.98 0.0037 grp 0 -16.63625463 B 5.09224749 -3.27 0.0015 grp 0.00000000 B age 0.27606039 0.20031521 1.38 0.1715 gender 0 -7.19514460 B 6.57159966 -1.09 0.2764 gender 1 0.00000000 B view 0 39.60239485 B 6.72446283 5.89 <.0001 view b esas output sum of source df squares mean square f value pk> F Model 2 4523.87346 2261.93673 2.50 0.0877 Error 93 84198.62654 905.36158 Corrected Total 95 88722.50000 R-Square Coeff Var Root MSE ease Mean 0.050989 66.67973 30.08923 45.12500 Source DF Type Ill SS Mean Square F Value P F age 1 3721.464262 3721.464262 4.11 0.0455 BMI 1 161.609809 161.609809 0.18 0.6736 Standard Parameter Estimate Error t Value er> It| Intercept 31.78710587 30.64585746 1.04 0.3023 age 0.49005446 0.24171213 2.03 0.0455 BMI -0.25560020 0.60497630 -0.42 0.6736