Experimental Design Test 3

Question 1 of 97

1

What is the difference between a 2-factor CRD and a 1-way CRD?

Select one of the following:

2-factor CRD allows for simultaneous testing of 2 treatment effects in the same experiment
1-factor CRD allows for simultaneous testing of 2 treatment effects in the same experiment
2-factor CRD tests the average of 2 Mu's for each Eu
1-factor CRD tests the average of 2 Mu's for each EU

Explanation

Question 2 of 97

1

Can you add nesting or blocking to a 2-factor CRD?

Select one of the following:

Yes
No

Explanation

Question 3 of 97

1

What are some of the advantages to using a 2-way CRD?

Select one or more of the following:

Can use fewer resources
Can gain efficiency in testing single factors
Allows you to test for interaction
Decreases the number of levels in each factor analysis

Explanation

Question 4 of 97

1

What are some of the drawbacks of using a 2-factor CRD?

Select one or more of the following:

Becomes more difficult to do based on practical limitation
Uses more resources
Loses efficiency in testing single factors
Can't test for interaction

Explanation

Question 5 of 97

1

What are the assumptions of a 2-factor CRD?

Select one or more of the following:

Independence within cells
Randomly Drawn Individuals within cells
Variances of the cells are similar
Normality within the cells
Additivity within the cells
Multiplicity of the cells
Normality of measurement units
Independence of measurement units
HOV of measurement units

Explanation

Question 6 of 97

1

What are the assumptions of a 3-factor CRD?

Select one or more of the following:

Independence within cells
Randomly Drawn Individuals in cells
Variances of cells are similar
Normality within the cells
Additivity within the cells
Multiplicity within the cells
Normality of measurement units
Independence of measurement units
HOV of measurement units

Explanation

Question 7 of 97

1

Select from the dropdown lists to complete the text.

Each combination of the two factors applied at the same time is a ( cell, pillar, block ) containing ( experimental units, measurement units ).

Explanation

Question 8 of 97

1

Select from the dropdown list to complete the text.

If each cell in a two-factor CRD only contains one experimental unit, the calculations are the same as a ( Randomized Block ANOVA, Repeated Time Measure ANOVA, One-Way ANOVA ), even though the designs are different.

Explanation

Question 9 of 97

1

Select from the dropdown lists to complete the text.

This is an example of a/an ( Proportional, Disproportional ) ( Balanced, Unbalanced ) design. In this design, ( n-cells are the same in all cells, ratios of n-cells are the same, ratios of n-cells are not the same ) and tests of cells on diagonal ( are ok, fail ).

Explanation

Question 10 of 97

1

Select from the dropdown lists to complete the text.

This is an example of a/an ( Proportional, Disproportional ) ( Unbalanced, Balanced ) design. In this design, ( n-cells are the same in all cells, ratios of n-cells are the same, ratios of n-cells are not the same ) and tests of cells on diagonal ( are ok, fails ).

Explanation

Question 11 of 97

1

Select from the dropdown lists to complete the text.

This is an example of a/an ( Proportional, Disproportional ) ( Balanced, Unbalanced ) design. In this design, ( n-cells are the same in all cells, ratios of n-cells are not the same, ratios of n-cells are the same ) and tests of cells on diagonal ( are ok, fail ).

Explanation

Question 12 of 97

1

Select from the dropdown lists to complete the text.

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? ( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process, One might )
Can we interpret Main Effects? ( Yes, No, Yes, after a ln transformation )
Would Simple Effects be of interest? ( Possibly, Definitely, Likely not )

Explanation

Question 13 of 97

1

Select from the dropdown lists to complete the text.

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? ( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process )
Can we interpret Main Effects? ( Yes, No, Yes, after a ln transformation, One might )
Would Simple Effects be of interest? ( Possibly, Definitely, Likely not )

Explanation

Question 14 of 97

1

Select from the dropdown lists to complete the text.

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? ( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process )
Can we interpret Main Effects? ( Yes, No, Yes, after a ln transformation, One might )
Would Simple Effects be of interest? ( Possibly, Definitely, Likely not )

Explanation

Question 15 of 97

1

Select from the dropdown lists to complete the text.

This is the results of an example Two-Factor CRD ANOVA with Multiple Replicates.

What kind of interaction is this? ( No Interaction, Interaction due to Multiplicative Data, Interaction due to biological process )
Can we interpret Main Effects? ( Yes, No, Yes, after a ln transformation, One might )
Would Simple Effects be of interest? ( Possibly, Definitely, Likely not )

Explanation

Question 16 of 97

1

Label the interaction graphs

Drag an answer into the correct orange point.

No Interaction

Original Multiplicative Data

Multiplicative data with transformation

Some interaction

Interaction

Explanation

Question 17 of 97

1

Simple effects or cell means testing is usually done with the interaction effect in the Two-Factor ANOVA is _______, and interpreting main effects of column and row means does not make sense.

Select one of the following:

large
small

Explanation

Question 18 of 97

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Sevearal different approaches can be used to examine simple effects. The primary differences in the approaches deal with control of and considerations

Select one or more of the following:

multiplicity
power
p-value
additivity

Explanation

Question 19 of 97

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What "logical" sets of simple effects exist?

Select one or more of the following:

within columns
within rows
within columns and rows
diagonal

Explanation

Question 20 of 97

1

In a Two-Factor ANOVA, if interaction is large and it has been decided that the main effects cannoth be interpreted, can we ignore the two-factor design and analyze each column/row with separate One-Factor Completely Randomized ANOVAs?

Select one of the following:

Yes
No

Explanation

Question 21 of 97

1

Is ignoring the two-factor design and running separate One-Factor ANOVAs or t-tests more or less powerfal than constructing the simple effects tests ithin the context of the Towo-Factor ANOVA? Why?

Select one of the following:

Less powerful, because MS-Error and df-Error from all groups are used in the calculation for each pair.
Less powerful, because df increases with each One-Factor test
More powerful, because MS-Error and df-Error from all groups are used in the calculation for each pair.
More powerful, because df increases with each One-Factor test

Explanation

Question 22 of 97

1

Model I: ❌
Model II: random - ❌
Model III: mixed - ❌

Drag and drop to complete the text.

fixed - 2 fixed factors

2 random factors

1 fixed factor, 1 random factor

Explanation

Question 23 of 97

1

Testing for interaction is...

Select one of the following:

similar change in magnitude and direction acoss treatment factor combinations
similar p-values in separate One-Factor ANOVA analyses

Explanation

Question 24 of 97

1

What are the null hypotheses of a Two-Factor Completely Randomized ANOVA with Multiple Replicates? (hint: main effects)

Select one or more of the following:

Column means are the same
Row means are the same
Interaction is not present
Cell means are the same
Interaction is present

Explanation

Question 25 of 97

1

What are the assumptions of a Two-Way Completely Randomized ANOVA (Model I) with Multiple Replicates?

Select one or more of the following:

data points within each cell are from randomly drawn individuals, normally distributed, and independent of one another
the variances of the cells are similar
data is additive
data points within the experiment are from randomly drawn individuals, normally distributed, and independent of one another
HOV within the experiment
data is multiplicative

Explanation

Question 26 of 97

1

What steps should be taken after the main Two-Way ANOVA?

Select one or more of the following:

Multiple Comparisons of main effects: testing for pair-wise differences between column/row means
Simple effects: testing for pair-wise differences in cell means within each column/row
Multiple Comparisons of main effects: testing for pair-wise differences within each column/row means
Simple effects: testing for pair-wise differences in cell means between columns/rows

Explanation

Question 27 of 97

1

Select all that apply to Type I SS

Select one or more of the following:

also known as "sequential SS"
valid only for balanced replication
Designed by Fisher
First SS developed
Can be used for balanced and disproportionate replication
takes interaction into account
often defaulted to because it works with almost every replication type
Yate's "unadjusted method of fitting constants"
Yate's "adjusted method of fitting constants"
Yate's "weighted squares of means"

Explanation

Question 28 of 97

1

Select all that apply to Type II SS

Select one or more of the following:

also known as "sequential SS"
valid only for balanced replication
Designed by Fisher
Can be used for balanced and disproportionate replication
more powerful than Type III with no interaction
takes interaction into account
often defaulted to because it works with almost every replication type
Yate's "unadjusted method of fitting constants"
Yate's "adjusted method of fitting constants"
Yate's "weighted squares of means"

Explanation

Question 29 of 97

1

Select all that apply to Type III SS

Select one or more of the following:

also known as "sequential SS"
valid only for balanced replication
Designed by Fisher
First SS developed
Can be used for balanced and disproportionate replication
takes interaction into account
often defaulted to because it works with almost every replication type
Yate's "weighted squares of means"
Yate's "unadjusted method of fitting constants"
Yate's "adjusted method of fitting constants"

Explanation

Question 30 of 97

1

With balanced data, all SS methods (Type I, II, III) will yield the same results.

Select one of the following:

True
False

Explanation

Question 31 of 97

1

SS will be the same for two entries in all computation methods (Type I, II, III). Which two?

Select one or more of the following:

error (residual)
interaction
Factor A
Factor B
total

Explanation

Question 32 of 97

1

In the presence of significant interaction, the chosen method of SS is _________.

Select one of the following:

irrelevant
crucial
somewhat important

Explanation

Question 33 of 97

1

Which of these are ways to DIRECTLY interpret interaction in a Two-Factor ANOVA?

Select one or more of the following:

interaction contrasts
simple effects
correlation
regression
sum of squares

Explanation

Question 34 of 97

1

Main effects for Factor A by this method can be interpreted as the main effect of Factor a controling or adjusting for Factor B and the interaction of Factor A x Factor B.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 35 of 97

1

A major criticisim of this method is that the model does not respect marginality, and that it is generally wrong to interpretmain effects in the presence of an interaction.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 36 of 97

1

In this method, SS for main effects are computed adjusting fo other main effects in the model, but omitting interaction terms.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 37 of 97

1

In this method of SS computation, effects are adjusted only for the terms that appear "above" them in the ANOVA table.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 38 of 97

1

A criticism of this SS computation method is that it produces different values for SS if we swap the ordering of the factors.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 39 of 97

1

The null hypothesses associated with this method can be interpreted as the equcivalenc eof unweighted means without making further assumptions regarding the presence of interaction.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 40 of 97

1

This method simplifies the testing of equality of equally weighted means if the interaction term is assumed to be zero.

Select one of the following:

Type I SS
Type II SS
Type III SS

Explanation

Question 41 of 97

1

If we can assume there's no interaction, the hypotheses for the Type I method become equivalent to Type III

Select one of the following:

True
False

Explanation

Question 42 of 97

1

This method tests for equivalence of fully weighted means for the first variable entered into the model, and the null hypothesis for the second factor is the same as the Type II method.

Select one of the following:

Type I
Type III

Explanation

Question 43 of 97

1

A criticisim for these methods of SS computation is that the null hypotheses are a function of sample size.

Select one or more of the following:

Type I
Type II
Type III

Explanation

Question 44 of 97

1

If ther is no interaction, this SS computation mehtod is the most powerful for an unbalanced design.

Select one of the following:

Type I
Type II
Type III

Explanation

Question 45 of 97

1

For most situations, power of the method of SS computation depends primarily on what?

Select one of the following:

structure of the imbalance
sample size
model type

Explanation

Question 46 of 97

1

Select from the dropdown lists to complete the text.

Procedure for Aligned Rank Transform for a Two-Factor ANOVA

1. ( Align, Rank, Average ) data ( seperately, al together ) producing ( three, five, seven, one ) different data sets
2. ( Rank aligned, Align ranked, Average ranked, Average aligned, Align averaged, Rank averaged ) data ( all together, seperately ) ( within, between ) each of the ( three, one, five, seven ) data sets
3. Replace original data with ( ranks, average, alignments ) ( within, between ) each of the ( three, one, five, seven ) data sets
4. Run ( three, one, five, seven ) ( separate, combined ) ( Two-Factor, One-Factor, Three-Factor ) ANOVAs on ( ranks, averages, alignments )

Explanation

Question 47 of 97

1

A _______ is another version of a two-factor experiment, with one factor nesed within the second factor.

Select one of the following:

Split-Plot design
Nested design
Repeated Measures design
Blocked design

Explanation

Question 48 of 97

1

Match the design types with the correct image.

Drag an answer into the correct orange point.

Two-Way CRD

Split-Plot design

One-Factor Nested design

Repeated Measures

Explanation

Question 49 of 97

1

Select from the dropdown lists to complete the text.

Procedure for Aligned Rank Transform for a Three-Factor ANOVA

1. ( Align, Rank, Average ) data ( seperately, all together ) producing ( three, one, five, seven ) different data sets
2. ( Rank aligned, Align ranked, Rank averaged, Align averaged, Average ranked, Average aligned ) data ( all together, separately ) ( within, between ) each of the ( three, one, five, seven ) data sets
3. Replace original data with ( ranks, averages, alignments ) ( within, between ) each of the ( three, seven, five, one ) data sets
4. Run ( three, one, five, seven ) ( separate, combined ) ( Two-Factor, One-Factor, Three-Factor ) ANOVAs on ( ranks, averages, alignments )

Explanation

Question 50 of 97

1

_____________ is a common technique for estimating coefficients of linear regression equations.

Select one of the following:

Ordinary Least Squares (OLS)
Geometric Mean Axis (GMA)
Pearson Correlation Analysis
ANVOA
ANCOVA

Explanation

Question 51 of 97

1

Select from the dropdown lists to complete the text.

Linear regrassion equations describe the relationship beetween one or more ( independent, dependent ) ( quantitative, qualitative ) variables and a ( dependent, independent ) variable.

Explanation

Question 52 of 97

1

What are the purposes of OLS?
(One-Factor ANOVA with x as independent variable and y as dependent variable)

Select one or more of the following:

Quatify rate of change in y as x changes
Quantify value of y at x=0 (y-intercept)
Predict a y-value given x
Quantify rate of change in x as y changes
Predict an x-value given y
Quantify a value of x at y=0 (x-intercept)

Explanation

Question 53 of 97

1

In which situations can OLS be used?

Select one or more of the following:

straight lines
curved lines that can be transformed into straight lines
quadratic lines
cubic lines
s-shaped lines

Explanation

Question 54 of 97

1

OLS assumes error in...

Select one of the following:

only the y-direction
only the x-direction
in both the x and y direction

Explanation

Question 55 of 97

1

Which line is the middle line of the data (Error in both the x and y direction)?

Select one or more of the following:

Central Axis Line
Geometric Mean Axis (GMA)
Reduced Major Axis
Ordinary Least Squares (OLS) Line

Explanation

Question 56 of 97

1

Select from the dropdown list to complete the text.

The OLS method aims to minimize the ( sum of sqaure differences, sum of differences, average difference, average error, sum of error terms ) between the observed and predicted values.

Explanation

Question 57 of 97

1

The main question that OLS aims to answer is whether or not there is a treatment effect (presence or absence of change).

Select one of the following:

True
False

Explanation

Question 58 of 97

1

❌: related to individual points in a dataset that was predicted mathematically.
❌: related to the entire OLS line from the new dataset that was predicted mathematically
❌: related to individual points in the actual dataset
❌: related to the entire OLS line from the new dataset that was predicted mathematically

Drag and drop to complete the text.

Prediction Intervals

Prediction Bands

Confidence Intervals

Confidence Bands

Explanation

Question 59 of 97

1

Select from the dropdown lists to complete the text.

Steps of inverse prediction:

1. Measure the ( dependent, independent ) variable (( y, x )) at known values of the ( independent, dependent ) variable (( x, y )).
2. Use these values to create a ( standard curve, line of correlation, central axis ) and find the regression equation
3. Rearrange the regression, isolating ( x, y ) on one side
4. Measure the unknown, finding the ( y-value, x-value )
5. Plug the measurement into the rearranged equation to find the unknown value

Explanation

Question 60 of 97

1

Select from the dropdown lists to complete the text.

( Interpolation, Extrapolation ): using the section of the OLS line bounded by the dataset for data prediction (( good use of equation, not recommended ))

( Extrapolation, Interpolation ): using sections of the OLS line not bounded by the dataset in order to complete data prediction (( not recommended, good use of equation ))

Explanation

Question 61 of 97

1

Common Diagnostic Tests for OLS

❌
-initial visual diagnostic
-may indicate ❌

r^2
-some information on the ❌
-as a general rule, r^2 > ❌ indicates a strong linear relationship

❌
-indication of ❌ or non-random error terms
-ranges from 0-4
- 2=❌, near 1 or 4 = ❌

❌
-indication of X outlier

❌
-examines patterns on scatter plot of Studentized Residual vs. X
-any pattern other than random indicates potential issues with ❌

❌
-large ❌ indicate possible Y-outliers
-absolute SDR value in 2-3 range or greater indicate possible outlier

❌
-large values indicate ❌ on linear equation
-percentiles greater than ❌% indicate overly influential data point. ❌% would be extreme.

Drag and drop to complete the text.

Scatter Plot of Y vs X

non-linear patterns

linear relationship between X and Y

0.95

0.50

Durbin-Watson Test

autocorrelation

low autocorrelation

high autocorrelation

Diagonal Elements of the hat matrix

Studentized Residual

Studentized Deletion Residual

Cook's Distance

linearity or HOV

absolue values

percentiles

averages

influential Y data point

50

95

Explanation

Question 62 of 97

1

Fill the blank spaces to complete the text.

r^2 = indicates a perfect line

r= indicates perfect positive correlation
r= indicates perfect negative correlation
r= indicates no correlation

Explanation

Question 63 of 97

1

Transformation of X
--corrects non-linearity without changing ❌ and ❌

Transformation of Y
--corrects non-linearity of ❌
--correct ❌ and ❌

Transformation of both X and Y
--correct non-linearity ❌ to fix other issues

Drag and drop to complete the text.

distribution of Y-values

relation between X and Y

HOV

non-normal distribution of Y values

variance

imposed by transformations

Explanation

Question 64 of 97

1

❌
--regression taking ❌ into account
--usually based on measuring things over time

❌
--Regression of discrete categorical data (❌)

❌
--fitting polynomial curves (❌

❌
--fitting ❌
--commonly used for growth curves

❌
--using ❌ to fit separate sections of complex patterns
--good for fit, not great for prediction

❌
--similar to linear regression in many aspects, but more than one X variable
--uses ❌

Drag and drop to complete the text.

Autoregressing Models

Logistic Regression

autocorelation

Curvilinear Regression

cubic, quadratic, etc.

S-shaped curves

Nonlinear Regression

age, presence/absence

Spline Regression

splines and knots

Multiple Regression

dummy X matrix

Explanation

Question 65 of 97

1

ANCOVA is a full design.

Select one of the following:

True
False

Explanation

Question 66 of 97

1

Select from the dropdown lists to complete the text.

In ANCOVA, for each individual ( EU/MU, block, factor ) ( a covariate, an additional factor, an additional level, a block ) is measured to account for variation other than the treatment effect.

Explanation

Question 67 of 97

1

ANCOVA can be applied to any design, including designs that also have blocking

Select one of the following:

True
False

Explanation

Question 68 of 97

1

ANCOVA is primarily utilized for what purpose?

Select one of the following:

remove background variation
add an additional level of analysis
order factors by importance

Explanation

Question 69 of 97

1

What is the null hypothesis of ANCOVA?

Select one of the following:

The adjusted means of columns are equal
The means of columns are equal
The observed frequency is equal to the expected frequency
The means of columns and rows are equal
There is no correlation between X and Y

Explanation

Question 70 of 97

1

What is the null hypothesis of Goodness of Fit?

Select one of the following:

The adjusted means of columns are equal
The means of columns are equal
The observed frequency is equal to the expected frequency
The means of columns and rows are equal
There is no correlation between X and Y

Explanation

Question 71 of 97

1

What is the null hypothesis of linear correlation?

Select one of the following:

The adjusted means of columns are equal
The means of columns are equal
The observed frequency is equal to the expected frequency
The means of columns and rows are equal
There is no correlation between X and Y

Explanation

Question 72 of 97

1

What are the assumptions for Y (data) for using a covariate in a One-Factor CRD?

Select one or more of the following:

Data points within each column are from randomly drawn individuals and are normaly distributed
Data points are independent of one another within and between columns
Variances of columns are similar
independent of treatment effect
no error in measuring
X and Y form a linear relationship for all treatment groups
slopes of regression lines are similar for all treatment groups

Explanation

Question 73 of 97

1

What are the assumptions for X (covariate) for using a covariate in a One-Factor CRD?

Select one or more of the following:

Data points within each column are from randomly drawn individuals and are normaly distributed
Data points are independent of one another within and between columns
Variances of columns are similar
independent of treatment effect
no error in measuring
X and Y form a linear relationship for all treatment groups
slopes of regression lines are similar for all treatment groups

Explanation

Question 74 of 97

1

What are the assumptions for X and Y for using a covariate in a One-Factor CRD?

Select one or more of the following:

Data points within each column are from randomly drawn individuals and are normaly distributed
Data points are independent of one another within and between columns
Variances of columns are similar
independent of treatment effect
no error in measuring
X and Y form a linear relationship for all treatment groups
slopes of regression lines are similar for all treatment groups

Explanation

Question 75 of 97

1

What is the main purpose for linear correlation?

Select one of the following:

Determine correlation of two measures.
Determine linear relationship between two measures
Remove background variation

Explanation

Question 76 of 97

1

What are the assumptions of Pearson's Correlation?

Select one or more of the following:

The X and Y pairs of data points are from randomly drawn individuals that are independent of one another
X and Y are normally distributed
X and Y form a linear relationship
HOV between data pairs

Explanation

Question 77 of 97

1

What are the assumptions of Spearman Non-Parametric Correlation?

Select one or more of the following:

The X and Y pairs of data points are from randomly drawn individuals that are independent of one another
X and Y are normally distributed
X and Y form a linear relationship
HOV between data pairs

Explanation

Question 78 of 97

1

❌ = Pearson correlation coefficient
❌ = coefficient of determination

Drag and drop to complete the text.

r^2

r

Explanation

Question 79 of 97

1

❌ lines are used for prediction
❌ lines are used for function

Drag and drop to complete the text.

OLS

Central Axis

Explanation

Question 80 of 97

1

Select from the dropdown lists to complete the text.

In a t-test of the correlation coefficient, the null hypothesis is ( r = rho = 0, r^2 = rho = 0, r = rho <> 0, r^2 = rho <> 0, r1 = r2 ). For a two-tailed test, t follows the ( t, Durbin-Watson, r, x^2, G )-distribution with v degrees of freedom.

Explanation

Question 81 of 97

1

z and z* transforms and the z-test of the correlation coefficient is used for testing which null hypotheses?

Select one or more of the following:

r = rho <> 0
r^2 = rho <> 0
r = rho = 0
r^2 = rho = 0
r1 = r2
basically, when the null hypothesis is that r is not equal to 0
basically, when the null hypothesis is that r is equal to 0

Explanation

Question 82 of 97

1

Which is one of the best non-parametric procedures for testing correlation?

Select one of the following:

Spearman
Pearson's
Durbin-Watson
Cook's Distance

Explanation

Question 83 of 97

1

The Spearman Non-Parametric procedures of determining correlation is often used by default because it doesn't rely on normality or linearity, but still gives reliable conclusions.

Select one of the following:

True
False

Explanation

Question 84 of 97

1

Analysis of Frequency/Count data relies on the testing of _ and __.

Select one or more of the following:

observed frequencies
expected frequencies
correlation coefficients
t-values
assigned values

Explanation

Question 85 of 97

1

What count-data design(s) can be analyzed with Goodness of Fit?

Select one or more of the following:

One-way
Two-way
Three-way
Four-way

Explanation

Question 86 of 97

1

What count-data design(s) can be analyzed with Contingency Tables?

Select one or more of the following:

One-way
Two-way
Three-way
Four-way

Explanation

Question 87 of 97

1

What is the null hypothesis for Goodness of Fit?

Select one of the following:

observed frequency = expected frequency
two-way classification factors are independent of each other
r = rho = 0
homogeneity across all tests

Explanation

Question 88 of 97

1

What is the null hypothesis for Contingency Tables?

Select one of the following:

observed frequency = expected frequency
two-way classification factors are independent of each other
r = rho = 0
homogeneity across all tests

Explanation

Question 89 of 97

1

What is the null hypothesis for Homogeneity Log-likelihood tests??

Select one of the following:

observed frequency = expected frequency
two-way classification factors are independent of each other
r = rho = 0
homogeneity across all tests

Explanation

Question 90 of 97

1

What are the assumptions for Goodness of Fit?

Select one or more of the following:

Counts are independent of each other
Expected frequencies are postulated before counts are made
Desireable to have a total count > 25
Desireable to have expected frequencies > 5
Desireable to have a total count > 6x the number of cells
Data is additive
Data is multiplicative
Counts are normally distributed
HOV

Explanation

Question 91 of 97

1

What are the assumptions for Contingency Tables?

Select one or more of the following:

Counts are independent of each other
Expected frequencies are postulated before counts are made
Desireable to have a total count > 25
Desireable to have expected frequencies > 5
Desireable to have a total count > 6x the number of cells
Data is additive
Data is multiplicative
Counts are normally distributed
HOV

Explanation

Question 92 of 97

1

Select from the dropdown lists to complete the text.

Chi-squared test uses ( x^2, t, G, D, z ) statistic, which follows the ( x^2, t, G, D, z ) distribution table with v degrees of freedom.

Log-likelihood test uses the ( G, x^2, t, D, z ) statistic, which follows the ( x^2, G, D, t, z ) distribution table with v degrees of freedom.

Explanation

Question 93 of 97

1

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Heterogeneitiy Log-Likelihood Tests analyze ( correlation, linear regression ) by combining ( Goodness of Fit, Contingency Table, ANOVA, ANCOVA ) tests.

Explanation

Question 94 of 97

1

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If the heterogeneity G test does not reject the null hypothesis of homogeneity across all tests, the ( Pooled G, Chi-squared, Log-likelihood, Durbin-Watson ) test is a legitimate test of adding the counts of all tests together for a combined test with a larger number of counts.

Explanation

Question 95 of 97

1

The purpose of this test is to add counts of multiple Godness-of-fit tests for a combined test with a larger number of counts.

Select one of the following:

Pooled G
Heterogeneity G
Goodness of Fit
Contingency Table

Explanation

Question 96 of 97

1

The purpose of this test is to test for homogeneity of goodness-of-fit tests (log-likelihood), particularly before a Pooled G test.

Select one of the following:

Pooled G
Heterogeneity G
Goodness of Fit
Contingency Table

Explanation

Question 97 of 97

1

Contingency tables can have marginal totals that are either fixed or not fixed.

❌ (❌)
Control totals of both factors, but not counts of each cell
In other words: control ratio between rows/columns, but not ratios of counts within each row/column

❌ (common)
Don't control totals of each row/column, only the total N
In other words: don't control any ratios

❌ (❌)
Control totals of one facor (rows OR columns), but not the other.
In other words: control the ratio of either rows OR columns, but not the other.

Drag and drop to complete the text.

One margin fixed, one margin not

Both margins not fixed

Both fixed

very rare

common

	Created by Jo O'Bar over 1 year ago

Master's Statistics Quiz on Experimental Design Test 3, created by Jo O'Bar on 31/10/2022.

Experimental Design Test 3

Question 1 of 97

What is the difference between a 2-factor CRD and a 1-way CRD?

Select one of the following:

Explanation

Question 2 of 97

Can you add nesting or blocking to a 2-factor CRD?

Select one of the following:

Explanation

Question 3 of 97

What are some of the advantages to using a 2-way CRD?

Select one or more of the following:

Explanation

Question 4 of 97

What are some of the drawbacks of using a 2-factor CRD?

Select one or more of the following:

Explanation

Question 5 of 97

What are the assumptions of a 2-factor CRD?

Select one or more of the following:

Explanation

Question 6 of 97

What are the assumptions of a 3-factor CRD?

Select one or more of the following:

Explanation

Question 7 of 97

Select from the dropdown lists to complete the text.

Each combination of the two factors applied at the same time is a cell pillar block( cell, pillar, block ) containing experimental units measurement units( experimental units, measurement units ).

Explanation

Question 8 of 97

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If each cell in a two-factor CRD only contains one experimental unit, the calculations are the same as a Randomized Block ANOVA Repeated Time Measure ANOVA One-Way ANOVA( Randomized Block ANOVA, Repeated Time Measure ANOVA, One-Way ANOVA ), even though the designs are different.

Explanation

Question 9 of 97

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Explanation

Question 10 of 97

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Explanation

Question 11 of 97

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Explanation

Question 12 of 97

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Explanation

Question 13 of 97

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Explanation

Question 14 of 97

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Explanation

Question 15 of 97

Select from the dropdown lists to complete the text.

Explanation

Question 16 of 97

Label the interaction graphs

Drag an answer into the correct orange point.

Explanation

Question 17 of 97

Simple effects or cell means testing is usually done with the interaction effect in the Two-Factor ANOVA is _______, and interpreting main effects of column and row means does not make sense.

Select one of the following:

Explanation

Question 18 of 97

Sevearal different approaches can be used to examine simple effects. The primary differences in the approaches deal with control of ____________ and ____________ considerations

Select one or more of the following:

Explanation

Question 19 of 97

What "logical" sets of simple effects exist?

Select one or more of the following:

Explanation

Question 20 of 97

In a Two-Factor ANOVA, if interaction is large and it has been decided that the main effects cannoth be interpreted, can we ignore the two-factor design and analyze each column/row with separate One-Factor Completely Randomized ANOVAs?

Select one of the following:

Explanation

Question 21 of 97

Is ignoring the two-factor design and running separate One-Factor ANOVAs or t-tests more or less powerfal than constructing the simple effects tests ithin the context of the Towo-Factor ANOVA? Why?

Select one of the following:

Explanation

Question 22 of 97

Each combination of the two factors applied at the same time is a ( cell, pillar, block ) containing ( experimental units, measurement units ).

If each cell in a two-factor CRD only contains one experimental unit, the calculations are the same as a ( Randomized Block ANOVA, Repeated Time Measure ANOVA, One-Way ANOVA ), even though the designs are different.

Sevearal different approaches can be used to examine simple effects. The primary differences in the approaches deal with control of and considerations

Model I: ❌
Model II: random - ❌
Model III: mixed - ❌