For questions 47-53, refer to the following study and the accompanying SAS output: Dexterity, as a measure of hand function, is an important component of a thorough hand evaluation. This is especially true in children, for whom the relationship between the commonly measured parameters of range of motion, sensation, and strength may not reflect actual functional ability. Gogola et al. (2013) conducted a study to document normative values from the Functional Dexterity Test (FTD) for typically developing children and to optimize test administration and interpretation. The FDT is a timed pegboard test consisting of 16 thick cylindrical pegs arranged in 4 rows of 4 pegs each. Patients turn over all pegs in a specified order by manipulating each peg in their hand.A total of 174 typically developing children aged 3 to 17 years participated in the study. Children completed the 16-peg FDT with either their dominant (n=105) or non dominant (n=69) hand, and elapsed time was recorded in seconds. Data were analyzed as 16/time, interpreted as FDT speed (pegs per second). Using a 0.05 significance level and the given computer output, you need to test the claim that the mean FDT speeds for dominant (1) and non dominant (0) hands differ significantly after adjusting for age (in years) by answering the questions that follow. These data were analyzed using two different models. Results from the analysis are provided below labeled SAS Output 1 and SAS Output 2. To answer some of the questions that follow, you need to fill in some critical pieces of information that have been deleted. SAS Output 1 Sum of Source DF Squares Mean Square F Value Pr > F Model 1.90829775 <.0001 error corrected total r-square coeff var root mse speed mean source of type iii ss square f value pp> F Age 3.74614560 3.74614560 302.77 <.0901 dominant standard parameter estimate error t value pr> It| Intercept 0.2309333571 B 0. 02726990 8.47 <.0801 age dominant b e. least squares means adjustment for multiple comparisons: tukey-kramer ho : lsmean1="LSMean2" speed lsmean pr> ItlR-Square Coeff Var Root MSE Speed Mean 0. 642384 16. 69917 0. 111234 0. 666107 Source DF Type III SS Mean Square F Value Pr > F Age 3.74614560 3.74614560 302.77 <.0901 dominant e. standard parameter estimate error t value pr> Itl Intercept 0. 2309333571 B 8. 02726990 8.47 <.8001 age dominant b e. least squares means adjustment for multiple comparisons: tukey-kramer ho : lsmean1="LSMean2" speed lsmean pr> It| 0. 69828145 <.0001 e. sas output sum of source df squares mean square f value pr> F Model 1.26693001 <.0801 error corrected total r-square coeff var root mse speed mean source df type iii ss square f value pr> F Age 3.22634279 3.22634279 259. 25 <.0801 dominant age standard parameter estimate error t value pr> Itl Intercept 0.2264932870 B 0. 04482257 5.05 <.0801 age b dominant hop .0006074245 e bq what type of analysis should you perform to test the given hypothesis select one: o a. logistic regression b. ancova c. two-way anova d. one-way e. linear q in sas output number insert for unexplained degrees freedom which model is more appropriate these data: or statistic and p-value use make this decision because interaction not significant value omnibus null ho do non hands differ significantly their mean fdt speeds if so how yes were slower than at all ages. faster those younger ages but older no speed.q consider estimated from can be written as:="0.2265+0.0394z" z="age" years x="(1" hand otherwise speed using following statements true based on figure below lines are coincident. parallel there a same direction>