Notice how the sample size changed as the effect level was increased. Describe the changes and how this influences the choice of an effect size for an a priori sample size determination.

Central and noncentral distributions Protocol of power analyses [1] -- Wednesday, October 27, 2021 -- 12:03:12 F tests - Linear multiple regression: Fixed model, R2 deviation from zero Analysis : A priori: Compute required sample size Input : Effect size f2 = 0. 15 a err prob = 0 . 05 Power (1-B err prob) . 8 Number of predictors 2 Output : Noncentrality parameter A 10 . 2000000 Critical F = 3. 1381419 Numerator df 2 Denominator df 65 Total sample size = 68 Actual power 0 . 8044183] -- Wednesday, October 27, 2021 -- 12:04:41 F tests - Linear multiple regression: Fixed model, R2 deviation from zero Analysis : A priori: Compute required sample size Input : Effect size f2 . 02 a err prob 0 . 05 Power (1-B err prob) . 80 Number of predictors = 2 Output : Noncentrality parameter A 9 . 7000000 Critical F 3 . 0144288 Numerator df 2 Denominator df 482 Total sample size 485 Actual power 0 . 8002244lesday, F tests - Linear multiple regression: Fixed model, R2 deviation from zero Analysis : A priori: Compute required sample size Input : Effect size f2 = . 35 a err prob = 0 . 05 Power (1-B err prob) = . 8 Number of predictors 2 Output : Noncentrality parameter A = 10 . 8500000 II Critical F 3. 3403856 Numerator df = 2 Denominator df 28 Total sample size 31 Actual power 0 . 8040921