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Statistics for Business & Economics, Revised
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Chapter 2 / Exercise 13
Statistics for Business & Economics, Revised
Anderson
Expert Verified
21 7.36 7.48 7.60 7.71 7.81 7.91 7.99 8.07 8.15 8.22 11 4.39 5.14 5.62 5.97 6.25 6.48 6.67 6.84 6.99 7.13 7.25 7.36 7.46 7.56 7.65 7.73 7.81 7.88 7.95 12 4.32 5.04 5.50 5.84 6.10 6.32 6.51 6.67 6.81 6.94 7.06 7.17 7.26 7.36 7.44 7.52 7.59 7.66 7.73 13 4.26 4.96 5.40 5.73 5.98 6.19 6.37 6.53 6.67 6.79 6.90 7.01 7.10 7.19 7.27 7.34 7.42 7.48 7.55 14 4.21 4.89 5.32 5.63 5.88 6.08 6.26 6.41 6.54 6.66 6.77 6.87 6.96 7.05 7.12 7.20 7.27 7.33 7.39 15 4.17 4.83 5.25 5.56 5.80 5.99 6.16 6.31 6.44 6.55 6.66 6.76 6.84 6.93 7.00 7.07 7.14 7.20 7.26 16 4.13 4.78 5.19 5.49 5.72 5.92 6.08 6.22 6.35 6.46 6.56 6.66 6.74 6.82 6.90 6.97 7.03 7.09 7.15 17 4.10 4.74 5.14 5.43 5.66 5.85 6.01 6.15 6.27 6.38 6.48 6.57 6.66 6.73 6.80 6.87 6.94 7.00 7.05 18 4.07 4.70 5.09 5.38 5.60 5.79 5.94 6.08 6.20 6.31 6.41 6.50 6.58 6.65 6.72 6.79 6.85 6.91 6.96 19 4.05 4.67 5.05 5.33 5.55 5.73 5.89 6.02 6.14 6.25 6.34 6.43 6.51 6.58 6.65 6.72 6.78 6.84 6.89 20 4.02 4.64 5.02 5.29 5.51 5.69 5.84 5.97 6.09 6.19 6.29 6.37 6.45 6.52 6.59 6.65 6.71 6.76 6.82 24 3.96 4.54 4.91 5.17 5.37 5.54 5.69 5.81 5.92 6.02 6.11 6.19 6.26 6.33 6.39 6.45 6.51 6.56 6.61 30 3.89 4.45 4.80 5.05 5.24 5.40 5.54 5.65 5.76 5.85 5.93 6.01 6.08 6.14 6.20 6.26 6.31 6.36 6.41 40 3.82 4.37 4.70 4.93 5.11 5.27 5.39 5.50 5.60 5.69 5.77 5.84 5.90 5.96 6.02 6.07 6.12 6.17 6.21 60 3.76 4.28 4.60 4.82 4.99 5.13 5.25 5.36 5.45 5.53 5.60 5.67 5.73 5.79 5.84 5.89 5.93 5.98 6.02 120 3.70 4.20 4.50 4.71 4.87 501 5.12 5.21 5.30 5.38 5.44 5.51 5.56 5.61 5.66 5. 71 5.75 5.79 5.83 00 3.64 4.12 4.40 4.60 4.76 4.88 4.99 5.08 5.16 5.23 5.29 5.35 5.40 5.45 5.49 5.54 5.57 5.61 5.65 138
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Statistics for Business & Economics, Revised
The document you are viewing contains questions related to this textbook.
Chapter 2 / Exercise 13
Statistics for Business & Economics, Revised
Anderson
Expert Verified
a known,iarge samples (2: 25) x - μo z = -CJ--l1n How many populations are of interest? x+z . ('(J')· H!:>: μ = μ0 vs.H1: J.l =t= μ0 Independent Groups? .:.= ct/ . -., .::;, ,r-')/'n-Hr;:: tt ~ f:lc, 11s. H 1, fl < f.l-r:, 1,.----------------------~ a1mkno1,1ni H0: ft S μ.0 vs. ff1: μ. > f.to x-μ() t=--s; /n w + ta/7,n- l. c;) ,.._ , _ ' • " I a kmrnm,Large samples (2: 25) ) (:i\ -i'z) -(J-11 -μz} z = ------=--====--=--f 2 2 :!(Jl + uz \I n 1 n2 /u2 u 2 (--") + ~· 1 + 2 X 1 -X1:; ~,',ah 1--:--."\I n1 n3 H 0: Jt1 = t-i1 vs. H1: J,t1 =I= μ2 Ho: f !1 2 μz v s. Hi.: μ1 < J12 H0: 1-i1 :::;. /1,z 11s. H1: J.•i > J-l2 Yes (J unkn own, assunied = (x1 -xz) -(tl1 -μ,z) t =-" I 5z (_!_ + _!_) ...JP n1 n2 2 _ (n1 -l )si + (n2 -1)s~ s -' . . P (n1 -1_) + (n2 -1) ~ ., 1 ,I Ji (;i\ -xz) ± t a/7.n,+->2. -:l; s; [- +'--). , •. . '\/' . ,n1 n 2 . -u tmknawn,assmned ::I= (x·1 -x,,) -1 -μ..,) ,.. -"' l(,, l -l 2 ' ls s.:; -1. _._--"-!n1 , nz "4 (sf , s})2 crazy d f = n1 '·ti; [(~~)"/Cn1 -1)] + ((~!Y1cn2 -j] ~ /sf , Sf ( -) ' t. 1- · . X1 -X2:. I .af2,=azy-.df ' n 1 nz "l / ) An F Test on equal population variances will tell you which scenario to use. H1«:r'; * ui" Hr:,:CJt, = al ANOVA ... er kno,vn, forge samples (~ 25) 15 -flo z = aD/ ·,/ii 15' a I Zrn _E.. .02 f-vn a unknown ,---_ D -;iD t = -=---. SD, 1 ,-, ';jn s D D ± t.a/2-""-1 v'ri H0: 1-•D = 0 't'S. H1: J.,tD #-0 H0:μD 2 0 vs.H1: μ D < 0 H1;/f:lD $ 0 vs.H1: PD> O ( )
) 1) Normality How many factors are of interest? Assumptions I 2) Equal Population V a.riances 3) Randomness/Independence Blocking? Yes Randomized Block Design Ies tin~ Treatment Effects Critical Value Method MS'A Fo=--MSE fA_J,,vays an upper fail test} Reject HD ifFo > F",(<-l l'r-lifo-DJ P-vaJueMethod P (F,.u,-t){c-l, 2: F-0) [Always a.n upper tail test] Reiect Ho if P-value < a Te.sting Block Effects Critical Value Method Fs = MSBL C MSE [Always an upper tail test] Reject H0 ifF" > F., ,,. __ ! <"r~!X,-m P-Yalne Method P CF,-L (r-l)(c -1) 2 Fa} {Always an uppe.r fail test] Re,:ect HQ if P-vaine < a: L ___ If,c μ, = jJ; _ _= = f', IH1~ At leMt t.wo 111~ans a.re Gifforeut T = a: r-iMSE Cfu/_--\} r q u = qu.,a,r,-n-c (With c treatments) (r -l}MSBL + r(c - 1)Jv1SE RE = .. (re -l)MSE No One-Way Completely Randomized Des~n Hi_i: j-t1_= μ2 = · · · = P.c J H1~ At ]east two means a.re different I Critical Value Method Fo:.,,-1,,.--.J {.-\lwap upper tail t~•t] Reject H,, if F~ > Fci.fr-l.o-<I P-value Method P {F,-1, '""° 2': Fv) [Always up_per tail test] Reiect Ho if P-value < a I_:i.WS_E(_-_ 1 _1 ·, T =q-J-- +-) ·G. ·-u __ ·. 2 ,n1 n.

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