Lesson 10: One-Way ANOVA/ Lesson 11: Associations between Categorical Variables Due Monday, July 24, 11:59PM ET Answer the following questions showing all work. If you use Minitab Express include the appropriate output along with an explanation. Round all answers to 3 decimal places. If you have any questions, post them to the course discussion board. (22 points) 1. Open the dataset Summer17Data.MTW. Assume this sample is representative of the population of all World Campus STAT 200 students. We want to know if the average height of students differs for students who prefer different winter sports. A. Why is a one-way ANOVA an appropriate statistical procedure for this scenario? B. Has the assumption of equal variances been met? Show how you checked for this. C. Construct a side-by-side boxplot comparing the heights of the students who prefer different winter sports. D. Perform the 95% confidence interval for the average heights of each of students preferring different winter sports. E. Based on the confidence intervals from part D, would you say that the average heights are the same? Why or why not? F. Setup the null and alternative hypothesis to determine if there is evidence that the mean height is different for students who prefer different winter sports. G. Perform the hypothesis test on Minitab for the above and paste the results here. H. What is your conclusion for the test at alpha = .05? I. Make all possible pairwise comparisons using Tukey's Honestly Significant Differences Test. Which pairs are different? J. How would you explain these results to a friend who has no knowledge of statistics? K. Can we conclude that sport preference causes differences in height? Why or why not? L. What is one possible confounding variable? Explain why this is a confounding variable. 2. Use the file Summer17Data.MTW to answer the following questions. A. Construct a contingency table of if a student is dieting and favorite winter sport. B. What proportion of dieting students prefer hockey? C. What proportion of non-dieting students prefers hockey? D. Given the proportions that you computed in parts B and C, are dieting and preference for hockey independent in this sample? Lesson 10: One-Way ANOVA/ Lesson 11: Associations between Categorical Variables Due Monday, July 24, 11:59PM ET E. Compute the \"relative risk\" of preferring hockey for dieters compared to non-dieters. F. How would you interpret the value that you computed in part E for a friend with no knowledge of statistics? G. Compute the odds ratio of preferring hockey for dieters compare to non-dieters. H. How would you interpret the value that you computed in part G for a friend with no knowledge of statistics? I. Conduct a chi-square test of independence to determine if there is evidence of a relationship between dieting and favorite winter sport in the population of all World Campus STAT 200 students. J. What is the conclusion of your test? ##################>### ############################################# ################ ### ####### ### ############################################### R#o#o#t# #E#n#t#r#y############################################################## ####################W############W#o#r#k#s#h#e#e#t#D#a#t#a#1#7############## ##################### ##################################################W#o#r#k#s#h#e#e#t #I#n#f#o#################################################################### ##############################C#o#m#p#r#e#s#s#e#d############################## ###########################################R W#RW################ ############################# ### ####### ### ################### ###############W ORK###############I###\\#\\#d#f#s#.#s#c#i#e#n#c#e#.#p#s#u#.#e#d#u#\\#s#t#o#r#a#g#e #\\#p#r#o#f#i#l#e#s#\\#w#a#z#1#0#7#\\#D#e#s#k#t#o#p#\\#S#P#1#6#S#T#U#D#E#N#T#D#A#T#A #.#M#T#W#6###############S#P#1#6#S#T#U#D#E#N#T#D#A#T#A#.#M#T#W################## ############################################################ ### ######### @ ###############S#e#g#o#e# #U#I#2###############S#e#g#o#e# #U#I# #S#e#m#i#b#o#l#d#################*######ADDRx# ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDRh# ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDRX# ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDRH# ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDR8# ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDR(# ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDR## ### ###CmColumnVar#ADDR# ### ###CmColumnVar#ADDR## ### ###CmColumnVar#ADDR# ### ###CmColumnVar##############G#######?############################## ################################################################################ ####_########################################################## ############;###~.v)@(+y0QmoRu(#]d6v#Qr\\eEs4UX+ #_{Eb}hM>#;<2gfI*F2>#+c#N#!Eq Lg2r@ %#yJ?#`K<707@;p#####NN]###r#o@w0;#~##o`} ###v#}#;#y###w;####}###@#p@x#;##@~+h#A##p{### _ #P<8#0#8# ##>##~###@#### ~(#S?#####,##0#######p#8#<#@ ###@#@rJ/#<#D##> ###,#AY# #vgi[#0##&#[Q}n#Dg#cyN 7#29sr qb{gM%.#Q$+,(n2_L%(FKQd#/FKQd#/FKQ_o? #&#Q#gg#+t#&? #IKQQdh14:L^M#b^HxO\\xn#:pQKV(]#>vD.vS| r=#5Q'p##2b#(?#B,&&W?2#Z6:51?D ##S##w#IHf\\vs##"W%1N&#;#nj#w|u##s4#[#Gjk .[ C;w6 <.#?/}@`'/#4a;(#\\#r#2398lugFDG #]Y>^x#?;#G#}c#mo*]# %#QsN#xrN_##>='H"VOa? C|'Qt`#RWe4#bKii#\\>#{MN%~K#?##i%~#d0^T3 x %F\\C?%a#:GZvfeFty/oo_xxM_ =f#l+g{1:l:#1| ~#^#wO##8q&Ngz\\#Kb<&m=c# I^y/c#S#\\#ty, oexU9Q#3>>$S?5rKw#~izjg3s/JqtvO$x s:Nt?f&,MZ{n# x3ji.Oj##k#NOO\\,%# RR ?TY# M.##v#=alQ## Y #= m; =,.rgS^t0#o#( '(Gs|aw(@ #A}#/&#jO#p##(v#w}Ev# '*?W##s 9&5vl~5u#y\\emgH{## )d a##WK3:Dl ?YX(#&U? }Blo#nw' ??/+{}#sm#=ORos b:n~#Zj}V=Z)Gqhv#Gg >######P<###<###y= D#>8### ###~DG`~t##?@#UC#8##(#&#(#*## y>X###| ###`~#?V<#?\\##?`#@~##P>l##p?p##>t# ?v#QoD{<#>| ###>##*D##@?#8#U##P##4#D#x#U#D#0?#@@>T2###? #H1whqxB%L:#,TWoe?+*LgJO{m6zm{#- i?e #zV)0Xg|p#>#iW1a#C@#{#a|Lm]6c+;# vGz[D =#MD{/##YQ9#<#9ure##&(##.'~~ Pso#`e#m+A#? W####0#GNd##[G"]###ew1J\\u62#Abn R#$^YnQ#8T~" %V&Os^l#Ywu#:SD9v#YQ8@&\\##tNS9Aw :?S`N#~~p##MoxRH # S$2?`yv;8|#Qw|#"|/*\\ 9Nqi# : a]Ew#+Gz]Ftgz:# mqe5`#:]yg<(n# 9'+H'cOR#aEu";g?^##d#DOZ{GXttWx{KutOt[3k^bu=w3:#WdssLF#}2##|}2#WWe# 5#|DLGCngG/ 5]]yw ##Dho#6## .##?=#On#zJvi7 g:h#D+F+##tHt7{+ *5t\\x5K ? #E8}##T5kn#ca>h#==#$:i^;? &#G#hf:a:6##u#)C:#~]obk #o2lD#F#<_;!2#~r##9+#| Tu##ZA/s#1_ x{\\ NSzTwG2s 9###ts#z]] +o9s#*8yTwGyTg6#Z#^f_39##qKi{3?va< #]+WwE/:s>LItW3,z##v##9##};*4swNf5D2yb #o##? zTganr#&W#9G*eFvJy:/9_c_yYr#mg#6>#^v#\\#sm[v6# #0 e b{# 5_DEEbS9 |gz4#.~wG;tsGwN9 ? K#&\\w#2# Uy# ?#4)OGLWG.###}#| O }5wfaO O}39sG$~g#U_o*#zjO##e##+9}s|@|7? DdW##?#I21#&#I?2KGIf#M#-[zkX-\\mv####'ys#? #em~+ i6h]v viaB#x~?Z!/8d'_tRD e~@eQ#IL#Hu_oqw#`6xq =#mH1<6];V@{P}~s[2 # [_ '#W H[}eooSm8`{/~_? bWG{l VzA>:fxu6.7nZ\\?#SW@87)E##90CL?`LW %&N&#;#_##?B;G--d2q_.5zGk9# o :.Ln#}G?L~>N#>3|}:-ytk%K/s## #Gr#23?#'###GI #u?#e@##%- %u;qD#/)ew1J\\u62#AbnFfafin%###|o| #sY)1#9#Ko#*#C/c\\#\\Nx##G~dV|8k{x( 2J.FgWx.#>A>^GJe##L#_#\\IxsUS fk3? >$pu&/{n]o[n}5}i#APXHQCW#UO Q#D###] Dfh,#B`t##ws # ##k##NaB 3#0`(Q#t,h ##V %}Y#m,qy1#PKm#eg#4)y:#GvCWQ}s##=#w^B#)># >.c!~#53#byTsmy#AM:##Ysc # #_/|#E##? # i?Z#~QS##A#^#G#<#2glyW|Y#=#!}c ^Q9N! #}G6A#Q|g#xQ^l?`_l_&{z]f##=h#3 #*I#y>Me#D ' |# %h#m9 ,=e|xc6#//r#Ys(~9Qe ? 2#7S#.QaE'|q> x###~L#qC\\#,+#/##:Qpv5>3s_9N#c>###I}|K PB#; >R#T=~:x_#[##*NQ#1Er\\e7626,xek#r1W8fB G| K ##d####/Qm#rg_s#x#9HvC#<9#5Oxb+>(|? mW`>l21*##{#X F]###e##9.62 ~VL S#p#}#o&#?9\\|g## %(#%<#y6~T# \\.##%##R<'8\\[Xo# $#6f{`##qgWYv#O8#Q-q#Fe'a|M#Gg|#<##i7 ? gI{f?#!y?*#? #u@x#Qo#{li#v5kg~#q *## # #AvJa)~G' w/nG-Nt _ ~^/iC |Ni`#R}QwLSK` #Jp/dx#]9>-A]/?#^#3 ?? w}"wE;'q'}2wNN ;_R=2w't:wN)wNi ;}ZwNwN) N}bwpm,zdftNyyQ'\\o#?:_as#z}w ? N#W'#iZ #( ##wq #$h#{#w###q_;G| #1##tlPD/bV#y#g\\#fbs'# ,//|O?K/#>0>f#Cd_tQX#y{#>v 9 2_ fga ? CH6}0^V8l/~E#w\\mH34M##>W_3w> q##s { @ # \\ 7qwQ0s#=d#_.MF?#5z #;i <8I#OmH#8D#wiu#NJj"R>#;^$ZtzY##= bGExi###h'#h.U#?##NAkZ#9&5hL+EI#Mh#Ur#w+D%rw:Woo#@:w#jF %]#><*A^:6*b?l7"{## w{\\=fG#A xP#g^^7nH##R#>Mz4 #qc/|w/|}_#\\F#e#w\\_qu| _# P#[1M^#+?"r##rU$/Zx #eZ_