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Stat-701 Final Experimental Statistics
Experimental Statistics (University of Agriculture Faisalabad)
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Stat-703 Final
Stat-703 Final
Untitled Section
An ANOVA procedure for CRD is applied to data obtained from 6 samples,
where each sample contains 9 observations. The degrees of freedom for
the critical value of F are:
1 point
5 numerator and 8 denominator degrees of freedom
53 degrees of freedom
54 degrees of freedom
5 numerator and 48 denominator degrees of freedom
Clear selection
In factorial designs, the response produced when the treatments of one
factor interact with the treatments of another in influencing the response
variable is known as:
1 point
A factor
A replication
The main effect
An interaction effect
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Stat-703 Final
If in a block the number of units is less than the number of treatment s,
then the block is said to be
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Complete
Unit < treatment, block
Incomplete
Insufficient block
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A design has to be chosen in a manner that all the extraneous sources of
variations are brought under control. This required to use:
1 point
Randomization
Replication
Local Control
Blocking
Clear selection
If in two factor factor factorial experiment the d.f of A x B=6 and d.f
(Blocks)=3 then d.f(Error)=
1 point
33
30
47
36
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A BIBD (Balance Incomplete Block design) is said to be symmetrical if
Number of blocks =
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Number of levels
Number of factors
Number of degree of freedom
Number of treatments
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If in an experiment the d.f(Total)=19 SSBlocks=420 and MS Blocks=105 and
R.E(RCBD over CRD)=3 then MSError=
1 point
10
5.83
36
20
The purpose of RCBD and Latin Square design is to control a source of
variation in the
1 point
Experimental material
Treatments
System
Distribution
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Stat-703 Final
What are field experiments and natural experiments collectively known as?
1 point
False experiments
Pseudo-experiments
Quasi-experiments
Qualitative studies
Clear selection
What will be degree of freedom of error for a Latin Square Design (LSD)
with four total treatments (where one is control treatment and three are
studied treatments):
1 point
4
6
3
15
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In Latin square design with Four treatments the value of Fcal=15 and
MSTreatments=45 then SSE=
1 point
36
3
18
21
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In 2^k factorial Experiment with k=3 , replication=4 , [a]=50, [b]=40, [c]=20,
[abc]=200 [ABC]= -104 , [ac]=90 then SS(ABC)=
1 point
338
3.25
1250
- 338
Assumptions underlying ANOVA : (i) Normality (ii) Homogeneity (iii)
Additivity and
1 point
dependence
Randomization
Interaction etc
Independence etc
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It is impossible to calculate the MSE in __________ Latin Square design.
1 point
4x4
2x2
None of These
3x3
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If the number of treatments is very large, the size of block will increase and
increase in the block size may produce:
1 point
Simplicity
Confusion
Homogeneity
Heterogeneity
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In RCBD we may assume that the treatment are fixed and the blocks are
random, such a model is called
1 point
Random effect model
Fixed effect model
Mixed effect model
Rare effect model
In 4 X 4 Latin square design the total three orthogonal contrast were used
the Sum of square of these contrast are 150 , 100 and 50 respectively then
MStreatment =__________
1 point
100
200
can,t be determined
300
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Here we make two blocks. The row wise variation is controlled by making
column wise block and similarly the column wise variation is controlled by
row wise blocking:
1 point
Split plot design
RCBD
Latin square design
GLSD
For a design with four factors, how many interactions will there be?
1 point
4
12
8
11
Clear selection
For a one-factor ANOVA fixed-effects model, which of the following is
always true?
1 point
dferror + dfTreatments = dftotal
SSbetw + SSwith = MStotal
All of the above
MSbetw + MSwith = MStotal
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Sometimes we are required to compare several population means
simultaneously. This is also possible by using
1 point
Two sample t- test
Regression equation
Multinomial distribution
GLSD
Clear selection
An experimental design where the experimental units are randomly
assigned to the treatments under hetrogenuous environmental conditions
is known as:
1 point
Latin Square Design (LSD)
Factorial Experiment
Completely Randomized Design (CRD)
Randomized Complete Block Design (RCBD)
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Stat-703 Final
Suppose we wants to test the effects of 5 quantitative treatments after
rejecting the null hypothesis we use the Trend Analysis via orthogonal
polynomial and found that Quadratic and Quartic effects are nonsignificant then we recommend the ________ to predict the response
variable.
1 point
Cubic Model
Cubic Model Without Quadratic Term
Linear Model
No Model can be used
Clear selection
In a two-factor ANOVA, one independent variable has five levels and the
second has four levels. If each cell has seven observations, what is df
error?
1 point
140
20
139
120
In a factorial experiment when number of treatment combinations is large,
the device of confounding is used to reduce the
1 point
Standard error
Degree of freedom
Block size
MSE
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Stat-703 Final
1 point
6.75
9
can,t be determined
3
If R.E(RCBD over CRD) =3.50 and d.f (Blocks) =3, if the experimenter wants
to get the same precision as by using CRD instesd of RCBD then he used r
=____
1 point
11
8
14
10
Clear selection
The Randomization process is applied in two stages
1 point
Latin Square Design
Factorial under RCBD
Factorial under CRD
RCBD
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Stat-703 Final
2^K in factorial design means K factors each at any
1 point
Two levels.
Two parameters
Two treatments
Two Values.
The word “Latin” is used due to Euler who used Latin letters for symbols of:
1 point
Treatments
Factors
Observations
Levels
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In three factor factorial experiment If 2nd order interaction AB is used for
confounding then the Linear combination is used_____
1 point
L= (I)(B)+(I)(A) +(0)(C)
L= (0)(B)+(I)(A) + (I)(C)
L= (I)(B)+(0)(A) + (I)(C)
Non of these
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Stat-703 Final
Confounding may not be suitable when the same precision for all
treatments comparison is
1 point
Not required
Suitable
Required
Seldom required
It permits the introduction of new treatments into an experiment which is
already in progress.
1 point
Factorial Experiment
Confounded Design
Split Plot design
Strip Plot Design
If an Experimenter consider two factors A with 3 levels , B with 4 levels and
both factors are equally important further more MSA=50 and SS(A X
B)=500 then SSB=
1 point
None of these
450
50
100
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Stat-703 Final
When there is a control treatment among treatments to compare and Fcalculated < F-table, Then appropriate multiple comparison test after
ANOVA will be:
1 point
Tukey’s test
Dunnett’s Test
None of given options
LSI Test
Clear selection
If there are two sources of variations we introduce:
1 point
Split plot design
Latin square design
RCBD
Factorial Experiment
Clear selection
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