> Y[Xo[ 9bjbj Ϟΐΐ0mmm<L< pyyyy|"v"$#<<<<<<<>ZA<-m$"^|"$$<yy
<'''$pRymy<'$<''a:K"<yM$:q<<0<;A-%rA(<Am<\*#"L#'d#x#*#*#*#<<&v*#*#*#<$$$$A*#*#*#*#*#*#*#*#*# : Experimental Design Basics
Hypothesis:
Design around 1 hypothesis
Be specific and concise
Identify independent and dependant variables
Be able to explain why you have made your prediction
Methods:
Have an appropriate control and constantsstate them clearly
Minimize number of variables
Sample sizethink big
You must measure in metric
Understand your methodology (ex: How does a pH meter work?)
Be able to explain why you did what you did (ex: Why did you mix for 30 secs?)
Take pictures (so you dont have to describe everything with words)
Analytical Approach:
Create easy to follow tables/graphs
Apply appropriate statistics (see statistical guide on following pages)
Is your data relevant?
Conclusion/Extension:
Revisit the hypothesis (dont say prove) and discuss null hypothesis
Use deductive reasoning (see article Think Like a Scientist)
Explain WHY, not just what
Declare and account for errors (and how would you correct them)
How is this project relevant?
Examine project extensions and what you would expect to happen
Presentation:
Avoid notes (use your slides, but dont read from them)
Project your voice and make eye contact
Dont rush (especially through data and conclusion)
Prepare for questions
Dress appropriately (professional attire)
Make slides visible (large font, no clutter or paragraphs)
Organize your slides (binder suggested)
Statistical Guide
Decide what type of data you have.
Create clear and meaningful tables and graphs.
Choose the correct test for your data
Use prior knowledge
T tests (see t-test section on following pages)
Chi Squared test (see chi squared section on following pages)
Find a website or book to fill in the blanks (below is a good start) HYPERLINK "http://www.wadsworth.com/psychology_d/templates/student_resources/workshops/stat_workshp/chose_stat/chose_stat_01.html" http://www.wadsworth.com/psychology_d/templates/student_resources/workshops/stat_workshp/chose_stat/chose_stat_01.html
The T-test
A t-test is used to determine if the mean of the control data is statistically different from the mean of the treatment group data. This statistic involves computing the difference between the two means and the difference in the variance of the two data sets. When there is great the variance in the data, which often results from factors that are difficult to control in the experiment, the difference in the means is less significant as depicted below in figure B.
SHAPE \* MERGEFORMAT
The following equation is provided only so that you will be able to explain how the statistic determines significance. It is highly recommended that you use a spreadsheet program to analyze your data.
EMBED Equation.3
The X symbol is the mean, with subscripts T and C representing the treatment and control groups respectively. Var is the variance in the data which is the square of the standard deviation of the data and n represents the degrees of freedom. The degrees of freedom in a data set is calculated as the number of data points minus one.
Once the value of t is calculated, the next task is to determine if it is significant. To do this, a level of confidence, alpha, needs to be chosen. This is typically set at 0.05. This value represents the percentage of times that the difference in the means of the data would occur at random. Alternately, at the 0.05 confidence level, 95 percent of the time it can be concluded that the difference in the means of two groups is significant, even given the variability in the data. Below is an abbreviated standard table of significance that can be used to determine if the results of a t-test is significant.
n 0.10 0.05 0.025 0.01 0.005 0.001
1. 3.078 6.314 12.706 31.821 63.657 318.313
2. 1.886 2.920 4.303 6.965 9.925 22.327
3. 1.638 2.353 3.182 4.541 5.841 10.215
4. 1.533 2.132 2.776 3.747 4.604 7.173
5. 1.476 2.015 2.571 3.365 4.032 5.893
6. 1.440 1.943 2.447 3.143 3.707 5.208
7. 1.415 1.895 2.365 2.998 3.499 4.782
8. 1.397 1.860 2.306 2.896 3.355 4.499
9. 1.383 1.833 2.262 2.821 3.250 4.296
10. 1.372 1.812 2.228 2.764 3.169 4.143
The first column represents the number of degrees of freedom and the remaining columns contain the critical values that the calculated t value needs to exceed, at each confidence interval, in order for the data to be deemed significant.
When using Excel to analyze data, the value that is calculated for the t-test is the actual confidence level that the data represents as opposed to the t value itself. If the value obtained is less than or equal to 0.05, then difference between the two means is significant.
In Excel, besides identifying the data sets to be examined there are two other parameters that need to be selected. The first is how many tails you want to consider in determining the mean significance. A one-tailed test allows you determine if there the difference between two means is either positive OR negative. The only other option is to select a two-tailed test which examines the possibility of change in either direction. Generally speaking, as stated, most hypotheses typically lend themselves to a one-tailed test.
The second parameter that needs to be chosen in Excel is whether the data is paired or unpaired. If unpaired, then the type of distribution (normal variance or unequal variance) also needs to be designated. A paired t-test in one in which each subject (ex. Person, plant, etc) undergoes two different treatments. An unpaired test is used with two different groups, control and treatment.
The following are examples of how to analyze and interpret data in Excel from two different types of studies.
Example #1 (one-tailed=1, paired t-test=1)
Statement: The amount of sleep affects ones ability to memorize.
Hypothesis: If and individual gets more sleep, then they will be able to memorize a longer list of words than those who get less sleep.
Method: A standard memory test was administered to a group of ten students on two different occasions, once after 8 hours of uninterrupted sleep and another time after 2 hours of sleep.
Data:
Subject2 hour test8 hour test1823261837244103051228651378208718992110622
The statistic yielded from Excel is 2.7x10-7. This means that there is a negligible chance (way less than 1%) that the results are random.
Example #2 (one-tailed=1, two sample/equal variance=2)
Statement: Plant food affects plant growth.
Hypothesis: If a plant is fed plant food in addition to water, it will grow taller than plants that are fed water alone.
Data:
Control PlantsGrowth after 10 days (cm)Treament PlantsGrowth after 10 days (cm)1816272837394104125115965697871188899999106108
The t-statistic calculated from the above data is 0.08. This exceeds a confidence level of 0.05 which nullifies the hypothesis.
The Chi Squared Test
A chi squared test is a test of the null hypothesis. The null hypothesis is the hypothesis that says the results are completely determined by chance. In contrast, the alternative hypothesis says that the results are influenced by a nonrandom cause. For example, suppose we wanted to determine whether flipping a coin was fair and balanced. A null hypothesis (Ho) might be that half the flips would result in Heads and half, in Tails. The alternative hypothesis (Ha) might be that the number of Heads and Tails would be very different. Thus:
H0: P = 0.5 (the null hypothesis says the probability is 50:50)Ha: P `" 0.5 (the alternative hypothesis says the probability is not 50:50)
Again, the equation below is provided only so that you will be able to explain how the statistic determines significance. It is highly recommended that you use a spreadsheet program (Excel) to analyze your data. The formula for calculating chi squared ( x2) is:
x2= ((o-e)2/e
(Basically, chi-square is the sum of the squared difference between observed (o) and the expected (e) data (or the deviation, d), divided by the expected data in all possible categories.)
Below is an example obtained from http://www2.lv.psu.edu/jxm57/irp/chisquar.html. You can visit that site for a more detailed explanation.
Example: suppose that a cross between two pea plants yields a population of 880 plants, 639 with green seeds and 241 with yellow seeds. You are asked to propose the genotypes of the parents. Your hypothesis is that the allele for green is dominant to the allele for yellow and that the parent plants were both heterozygous for this trait. If your hypothesis is true, then the predicted ratio of offspring from this cross would be 3:1 (based on Mendel's laws) as predicted from the results of the Punnett square (Figure B. 1).
Figure B.1 - Punnett Square.
Predicted offspring from cross between green and yellow-seeded plants. Green (G) is dominant (3/4 green; 1/4 yellow).
To calculate x2 , first determine the number expected in each category. If the ratio is 3:1 and the total number of observed individuals is 880, then the expected numerical values should be 660 green and 220 yellow. As youll see below, you get a x2 value of 2.668.
Calculating Chi-Square:
Green YellowObserved (o)639241 Expected (e)660220 Deviation (o - e)-21 21Deviation2 (d2)441441 d2/e0.6682 INCLUDEPICTURE "http://www2.lv.psu.edu/jxm57/irp/chi.gif" \* MERGEFORMATINET 2 = INCLUDEPICTURE "http://www2.lv.psu.edu/jxm57/irp/sigma.gif" \* MERGEFORMATINET d2/e = 2.668.. Here's how to interpret the x2 value:
1. Determine degrees of freedom (df). Degrees of freedom can be calculated as the number of categories in the problem minus 1. In our example, there are two categories (green and yellow); therefore, there is I degree of freedom.
2. Determine a relative standard to serve as the basis for accepting or rejecting the hypothesis. The relative standard commonly used in biological research is p > 0.05. The p value is the probability that the deviation of the observed from that expected is due to chance alone (no other forces acting). In this case, using p > 0.05, you would expect any deviation to be due to chance alone 5% of the time or less.
3. Refer to a chi-square distribution table (below). Using the appropriate degrees of 'freedom, locate the value closest to your calculated chi-square in the table. Determine the closest p (probability) value associated with your chi-square and degrees of freedom. In this case (x2=2.668), the p value is about 0.10, which means that there is a 10% probability that any deviation from expected results is due to chance only. Based on our standard p > 0.05, this is within the range of acceptable deviation. In terms of your hypothesis for this example, the observed chi-square is not significantly different from expected. The observed numbers are consistent with those expected under Mendel's law.
Degrees of
Freedom
(df) Probability (p)0.950.90 0.800.700.50 0.300.200.10 0.050.010.001 10.0040.020.060.15 0.461.071.64 2.713.846.64 10.8320.100.210.450.71 1.392.413.22 4.605.999.21 13.8230.350.581.011.42 2.373.664.64 6.257.8211.34 16.27Nonsignificant Significant
The final step is to state your statistical conclusion in terms of your hypothesis.
If the p value for the calculated x2 is p > 0.05, accept your hypothesis. 'The deviation is small enough that chance alone accounts for it. A p value of 0.6, for example, means that there is a 60% probability that any deviation from expected is due to chance only. This is within the range of acceptable deviation.
If the p value for the calculated x2 is p < 0.05, reject your hypothesis, and conclude that some factor other than chance is operating for the deviation to be so great. For example, a p value of 0.01 means that there is only a 1% chance that this deviation is due to chance alone. Therefore, other factors must be involved.
The easier way to calculate chi-squared is by using excel. A great tutorial can be found at HYPERLINK "http://www.gifted.uconn.edu/siegle/research/ChiSquare/chiexcel.htm" http://www.gifted.uconn.edu/siegle/research/ChiSquare/chiexcel.htm
. There, you will find a detailed example of how to create and interpret data in MS Excel.
A
B
) " "
X
[
l
m
o
679CDнвŲš{w{swswokgwkwkwkwh#
h0mhhk9hG]=hvh;hk95CJaJh+5CJaJh#
h#
5CJaJhH95CJaJhvCJaJh#
h:*CJaJh#
CJaJh#
h(CJaJh#
h+CJaJh#
h2&CJaJh2&h#
h:*CJ$aJ$h#
h2&CJ$aJ$()D\ " 8 S "
#
8
\
&Fgd:*gd:*
&Fgd+gd+
&Fgd+gd2&$a$gd2&
Xs X0
X
Y
Z
[
m
n
o
&Fgd$a$gd#
&F
80`0gd(gd(h^hgd:*
&Fgd:*
789wxygdvp^pgd0m
&Fgd
&Fgd0m
&FgdG]=gdG]=
&Fgdgdk9DIvwyACDEpq»pfjhk9EHUj`VL
hk9CJUVaJjQhk9Ujhk9UmHnHujhk9Uha8hk95#ha8B*CJ OJQJ^JaJ phhvhvhvh#
hvhhh0JjhUhhjhUh;hk9hhG]=h0m'@^@`gdk9$a$gdk9gdk9$a$gda8
gdvprs Y=vgdk9gdk9"$) !!!!!!! !'!(!0!1!^!`!!!"""######"###,#-#5#6#?#@#H#I#Q#R#\#]####d%e%%%4&沪hN*jhk9H*hN*jhk9#hk9B*CJ OJQJ^JaJ ph hk9H*hk9CJaJhk9CJOJQJ^JaJha8hk95jhk9Uhk9 hk95C)*mn $Ifgdh'
d
gdk9gdk9 $$Ifa$gdh'ekdb$$IflFR''U'Uf644
laX $$Ifa$gdh'ekd$$IflFR''U'Uf644
laX $$Ifa$gdh'ekdj$$IflFR''U'Uf644
laX $$Ifa$gdh'ekd$$IflFR''U'Uf644
laX !!!$$Ifa$gdh'ekdr$$IflFR''U'Uf644
laX!!
!!!$$Ifa$gdh'ekd$$IflFR''U'Uf644
laX!!!!!$$Ifa$gdh'ekdz $$IflFR''U'Uf644
laX!!!!!$$Ifa$gdh'ekd $$IflFR''U'Uf644
laX! !"!$!'!$$Ifa$gdh'ekd
$$IflFR''U'Uf644
laX'!(!+!-!0!$$Ifa$gdh'ekd$$IflFR''U'Uf644
laX0!1!2!3!4!!!!!&"'""""""" $Ifgdh'gdk9ekd$$IflFR''U'Uf644
laX""""###}qqqq$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laX $Ifgdh'###
###zzzz$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laX######zzzz$$Ifa$gdh'xkd^
$$Ifl\i#''''
$644
laX#####"#zzzz$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laX"###%#(#*#,#zzzz$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laX,#-#/#1#3#5#zzzz$$Ifa$gdh'xkdV$$Ifl\i#''''
$644
laX5#6#8#:#<#?#zzzz$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laX?#@#B#D#F#H#zzzz$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laXH#I#K#M#O#Q#zzzz$$Ifa$gdh'xkdN$$Ifl\i#''''
$644
laXQ#R#U#W#Z#\#zzzz$$Ifa$gdh'xkd$$Ifl\i#''''
$644
laX\#]#^######2&P'(xpkb^gdk9gda8$a$gda8
Hgdk9gdk9xkd$$Ifl\i#''''
$644
laX
4&6&&&((((((((((()
)!)")<)=)****,3,r,s,,,,,,,L-f----------------.......߰߰ha8hN*jhk9>* jhN*jhk9UmHnHuhN*jhk95\ha8hk95hN*jhk96]hN*jhk90J jhN*jhk9hN*jhk9PJhN*jhk9H*hN*jhk9hN*jhk9H*:((z)*,3,,,------- $Ifgdh'gda8\$gdk9gdk9$a$gdk9----.pggg $Ifgdh'kdF$$IfF V[0 634ab.....pggg $Ifgdh'kd$$IfF V[0 634ab..&.*.+./.0.2.3.=.>.C.D.G.H.L.M.N.O.P.R.S.X.Y.[.\.].^........///// ///////7/8/0000j1k1l1n12222334444ȽȲhN*jhk95\jhN*jhk9UjhN*jhk9UjhN*jhk9UhN*jhk96H*]hN*jhk9H*hN*jhk96]hN*jhk9hN*jhk9PJB..+.0.3.pggg $Ifgdh'kd$$IfF V[0 634ab3.4.D.H.M.pggg $Ifgdh'kd$$IfF V[0 634abM.N.S.Y.\.pggg $Ifgdh'kdv$$IfF V[0 634ab\.].///pggg $Ifgdh'kdB$$IfF V[0 634ab//@/&0144444pkkkk____$$Ifa$gdh'gdk9kd6$$If&F V[0 634ab 44444444444444444444444444444444555
555555555 5%5&5+5,535458595=5>5C5D5H5I5M5N5S5T5X5Y5]5^5c5d5i5j5q5r5v5w5{5|5555555555555555hN*jhk9PJhN*jhk96]hN*jhk9[44444444444444|||||||||||| $Ifgdh'zkd$$If90!0634ab
44444555555 5&5,5-5/54595>5D5I5N5T5Y5^5d5j5Ff $Ifgdh'$$Ifa$gdh'Ffj5k5m5r5w5|5555555555555FfY' $Ifgdh'$$Ifa$gdh'Ff#555555(6)6D6E6I6M6667777899N9P9Q9999999999999Ҿ𩢝hh'hk9 hk95hvh)h0mhvB*CJ OJQJ^JaJ phh.
hbz0JhN*jhbzhbzjhbzUhN*jhk9H*hN*jhk96]ha8hN*jhk9hN*jhk9PJ#55555!6]78899rmmmm^^mmm
&Fdd[$\$gdk9gdk9kd)$$IfF!0634ab
99999999999
gdvgd0mgdk9gdk9
21h:pa8/ =!"#$%n!b~9u:UPNG
IHDRzQbKGD#2cmPPJCmp0712Om#IDATXuA-k9w$ld19pg^/hrpb)0fz𣀃0 /ry]D Ha2Gq2 CF֒VPcy~y7*i?Y%B>hA WAlJیھȀY,qoqNBO|[hK[jԨX"5'T#W
6jT2qGncv]NK=~'RDqZH9VD N4DӉ N4pUvv}W5%eNqZE8-Z2iQ)N^MqZh8I:*P'NPiR8pJӁ
ѯU4GdjTˮ镹6Gksn'a{8KF"N>ɔo,aO̢w>-{ֶ`?x]vM'6
.g@r8/Oa) Zλ NL8@'
@ ˟' N8PC
hq ' NotXb)j-N ݗ '^d6_X@+Nd:K{P
(-oIENDB`QDyKwhttp://www.wadsworth.com/psychology_d/templates/student_resources/workshops/stat_workshp/chose_stat/chose_stat_01.htmlyKhttp://www.wadsworth.com/psychology_d/templates/student_resources/workshops/stat_workshp/chose_stat/chose_stat_01.htmlDd :D
3@@"?Dd
$\
c$A??#"`25E
5#~Idj
%*`! E
5#~Idj
HD
xڝSkAٻ[,MZ8,"AҤ\m`EŽȱ"JH' !h0uW"$u~{o_v@~#́ZUb4M5j:P%pR{ʽ'pccp#&#+R=*`{gpSuxs3m(|Ol@}{Xe]),rq#j)mK
V(_O0HMgQu0[8t|ؼT{rEx29{^->aw)}Uķf7}}I/"aӗ?X}M'.h-|e+)%
x+I:x)>UˡuW{s|>SZ?@ABCDEFGHIJKLMNOQRSTUVW^Z]l_`abcdefghijkmnqrstuvwxyz{|}~Root Entry
FKM\Data
P*WordDocument ϞObjectPoolPMKM_1280729321FPMPMOle
CompObjfObjInfo
FMicrosoft Equation 3.0DS EquationEquation.39q;÷,
t=T
"C
varT
nT
+varC
nCEquation Native 1TablepBSummaryInformation(DocumentSummaryInformation8$$IfX!vh5555
#v#v#v#v
:Vl$6,5555
9/aX$$IfX!vh5555
#v#v#v#v
:Vl$6,5555
9/aX$$IfX!vh5555
#v#v#v#v
:Vl$6,5555
9/aX$$IfX!vh5555
#v#v#v#v
:Vl$6,5555
9/aX$$IfX!vh5555
#v#v#v#v
:Vl$6,5555
9/aX$$If!vh5 5 5E #v #v #vE :V0 6,55V5[/34$$If!vh5 5 5E #v #v #vE :V0 6,55V5[/34$$If!vh5 5 5E #v #v #vE :V0 6,55V5[/34$$If!vh5 5 5E #v #v #vE :V0 6,55V5[/34$$If!vh5 5 5E #v #v #vE :V0 6,55V5[/34$$If!vh5 5 5E #v #v #vE :V0 6,55V5[/34Dd
#F
C"chiDd
#J
C&sigma$$If!vh5 5 5E #v #v #vE :V&0 6,55V5[/34$$If!vh5"5#v"#v:V906,55/348$$If!vh5"55555555 5
5
5,#v"#v#v #v
#v
#v,:V06,555 5
5
5/34kd$$Ifg A}!06000034ab08$$If!vh5"55555555 5
5
5,#v"#v#v #v
#v
#v,:V06,555 5
5
5/34kd$$Ifg A}!06000034ab08$$If!vh5"55555555 5
5
5,#v"#v#v #v
#v
#v,:V06,555 5
5
5/34kdR"$$Ifg A}!06000034ab08$$If!vh5"55555555 5
5
5,#v"#v#v #v
#v
#v,:V06,555 5
5
5/34kd&$$Ifg A}!06000034ab0$$If!vh5"555#v"#v5#v:V06,555/34Oh+'0 (
HT
`lt|Experimental Design BasicsudsdNormalJoanne Lilliendahl8Microsoft Office Word@Ff@&!@MN)՜.+,D՜.+,d hp
Upper Dublin School DistrictX0Experimental Design BasicsTitle, 8@_PID_HLINKSA0zChttp://www.gifted.uconn.edu/siegle/research/ChiSquare/chiexcel.htm*Owhttp://www.wadsworth.com/psychology_d/templates/student_resources/workshops/stat_workshp/chose_stat/chose_stat_01.html*
F'Microsoft Office Word 97-2003 Document
MSWordDocWord.Document.89q^666666666vvvvvvvvv666666>6666666666666666666666666666666666666666666666666hH6666666666666666666666666666666666666666666666666666666666666666662 0@P`p2( 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p8XV~_HmH nH sH tH @`@NormalCJ_HaJmH sH tH DA`DDefault Paragraph FontRi@RTable Normal4
l4a(k (No Listjj2&
Table Grid7:V06U@6 Hyperlink>*B*phB^@BvNormal (Web)dd[$\$N!Nvsublft1'5@CJOJQJ\^JaJo(phf3e@2k9HTML Preformatted7
2(
Px4 #\'*.25@9CJOJQJ^JaJ.X@A.k9Emphasis6]FVQF
;0FollowedHyperlink>*B*phPK![Content_Types].xmlj0Eжr(Iw},-j4 wP-t#bΙ{UTU^hd}㨫)*1P' ^W0)T9<l#$yi};~@(Hu*Dנz/0ǰ$X3aZ,D0j~3߶b~i>3\`?/[G\!-Rk.sԻ..a濭?PK!֧6_rels/.relsj0}Q%v/C/}(h"O
= C?hv=Ʌ%[xp{۵_Pѣ<1H0ORBdJE4b$q_6LR7`0̞O,En7Lib/SeеPK!kytheme/theme/themeManager.xmlM
@}w7c(EbˮCAǠҟ7՛K
Y,
e.|,H,lxɴIsQ}#Ր ֵ+!,^$j=GW)E+&
8PK!Ptheme/theme/theme1.xmlYOo6w toc'vuر-MniP@I}úama[إ4:lЯGRX^6؊>$!)O^rC$y@/yH*)UDb`}"qۋJחX^)I`nEp)liV[]1M<OP6r=zgbIguSebORD۫qu gZo~ٺlAplxpT0+[}`jzAV2Fi@qv֬5\|ʜ̭NleXdsjcs7f
W+Ն7`gȘJj|h(KD-
dXiJ؇(x$(:;˹!I_TS1?E??ZBΪmU/?~xY'y5g&/ɋ>GMGeD3Vq%'#q$8K)fw9:ĵ
x}rxwr:\TZaG*y8IjbRc|XŻǿI
u3KGnD1NIBs
RuK>V.EL+M2#'fi~Vvl{u8zH
*:(W☕
~JTe\O*tHGHY}KNP*ݾ˦TѼ9/#A7qZ$*c?qUnwN%Oi4=3ڗP
1Pm\\9Mؓ2aD];Yt\[x]}Wr|]g-
eW
)6-rCSj
id DЇAΜIqbJ#x꺃6k#ASh&ʌt(Q%p%m&]caSl=X\P1Mh9MVdDAaVB[݈fJíP|8քAV^f
Hn-"d>znǊ ة>b&2vKyϼD:,AGm\nziÙ.uχYC6OMf3or$5NHT[XF64T,ќM0E)`#5XY`פ;%1U٥m;R>QDDcpU'&LE/pm%]8firS4d7y\`JnίIR3U~7+#mqBiDi*L69mY&iHE=(K&N!V.KeLDĕ{D vEꦚdeNƟe(MN9ߜR6&3(a/DUz<{ˊYȳV)9Z[4^n5!J?Q3eBoCMm<.vpIYfZY_p[=al-Y}Nc͙ŋ4vfavl'SA8|*u{-ߟ0%M07%<ҍPK!
ѐ'theme/theme/_rels/themeManager.xml.relsM
0wooӺ&݈Э5
6?$Q
,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}|$b{P8g/]QAsم(#L[PK-![Content_Types].xmlPK-!֧6+_rels/.relsPK-!kytheme/theme/themeManager.xmlPK-!Ptheme/theme/theme1.xmlPK-!
ѐ' theme/theme/_rels/themeManager.xml.relsPK]
0 0
D4&.459!$<@FJ
!!!!'!0!"###"#,#5#?#H#Q#\#(-..3.M.\./44j5599 "#%&'()*+,-./0123456789:;=>?ABCDEGHIKLD
]%%%%&&/P000X_:CCXl,b$!b~9u:U4@(
(
&
3 "0?`
c$X99? &Z g
gTB
CDgTB
CD4t g
##"sg&TB
CDgTB
CD
B$CFDEdF,FUV6u3F-
[2dr'HRij
$2@ :o"
B$CFDEdF,FUV6u3F-
[2dr'HRij
$2@ #"^H
BC)DEdF,)|3Z%8qB|
)H! B/
6Xa
@ ""
BC)DEdF,)|3Z%8qB|
)H! B/
6Xa
@ #"J&b
#"`Oj
b
#"`j
P
3 "?
s<Apunnett3"?B
S ? pr#0! tH|YtT
_Hlt271223222
_Hlt271223223
_Hlt271224542
_Hlt271224543ll001@@@@ll001.*.<*"#"#10#0#1:*urn:schemas-microsoft-com:office:smarttagsStreet;*urn:schemas-microsoft-com:office:smarttagsaddress5)%("""#)#a&c&++,,01uvKXZ[ ##$$a&c&//013333333333333()[78S#8XsX[lu4^$$&+,-*.].v///000001X[$$,,/01
`:E ?Fcnm4Uv(vp4ݴVrt822$vzh88^8`OJQJo(hHh
^`o(hH.h ^ `OJQJo(hHh^`OJQJo(hHhxx^x`OJQJ^Jo(hHohHH^H`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@@^@`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@@^@`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@@^@`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@@^@`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohpp^p`OJQJo(hHh@@^@`OJQJo(hHh^`OJQJ^Jo(hHoh^`OJQJo(hHh^`OJQJo(hHh^`OJQJ^Jo(hHohPP^P`OJQJo(hH^`.^`.pp^p`.@@^@`.^`.^`.^`.^`.PP^P`.c(vpnm:E Vrt
$v 2.lb^r"x2{$#
2&Y*:*a8H9k9G]=0mv-+(bzh';00@(0h@h&hP@UnknownG*Ax Times New Roman5Symbol3.*Cx ArialI.??Arial Unicode MS?= *Cx Courier New;WingdingsA BCambria Math"qhF3&N)XN)XYx24002QHX ?2&2!xxExperimental Design BasicsudsdJoanne Lilliendahl(CompObjy