Experimental Design Test 3

Pregunta 1 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 2 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Yes
No

Explicación

Pregunta 3 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 4 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 5 de 97

1

What are the assumptions of a 2-factor CRD?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 6 de 97

1

What are the assumptions of a 3-factor CRD?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 7 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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

Explicación

Pregunta 8 de 97

1

Selecciona la opción correcta del menú desplegable para completar el texto.

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.

Explicación

Pregunta 9 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 ).

Explicación

Pregunta 10 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 ).

Explicación

Pregunta 11 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 ).

Explicación

Pregunta 12 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 )

Explicación

Pregunta 13 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 )

Explicación

Pregunta 14 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 )

Explicación

Pregunta 15 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 )

Explicación

Pregunta 16 de 97

1

Label the interaction graphs

Arrastra la opción correcta a cada uno de los puntos naranjas

No Interaction

Original Multiplicative Data

Multiplicative data with transformation

Some interaction

Interaction

Explicación

Pregunta 17 de 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.

Selecciona una de las siguientes respuestas posibles:

large
small

Explicación

Pregunta 18 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

multiplicity
power
p-value
additivity

Explicación

Pregunta 19 de 97

1

What "logical" sets of simple effects exist?

Selecciona una o más de las siguientes respuestas posibles:

within columns
within rows
within columns and rows
diagonal

Explicación

Pregunta 20 de 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?

Selecciona una de las siguientes respuestas posibles:

Yes
No

Explicación

Pregunta 21 de 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?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 22 de 97

1

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

Arrastra y suelta para completar el texto.

fixed - 2 fixed factors

2 random factors

1 fixed factor, 1 random factor

Explicación

Pregunta 23 de 97

1

Testing for interaction is...

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 24 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 25 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 26 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 27 de 97

1

Select all that apply to Type I SS

Selecciona una o más de las siguientes respuestas posibles:

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"

Explicación

Pregunta 28 de 97

1

Select all that apply to Type II SS

Selecciona una o más de las siguientes respuestas posibles:

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"

Explicación

Pregunta 29 de 97

1

Select all that apply to Type III SS

Selecciona una o más de las siguientes respuestas posibles:

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"

Explicación

Pregunta 30 de 97

1

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

Selecciona uno de los siguientes:

VERDADERO
FALSO

Explicación

Pregunta 31 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

error (residual)
interaction
Factor A
Factor B
total

Explicación

Pregunta 32 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

irrelevant
crucial
somewhat important

Explicación

Pregunta 33 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

interaction contrasts
simple effects
correlation
regression
sum of squares

Explicación

Pregunta 34 de 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.

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 35 de 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.

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 36 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 37 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 38 de 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.

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 39 de 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.

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 40 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Type I SS
Type II SS
Type III SS

Explicación

Pregunta 41 de 97

1

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

Selecciona uno de los siguientes:

VERDADERO
FALSO

Explicación

Pregunta 42 de 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.

Selecciona una de las siguientes respuestas posibles:

Type I
Type III

Explicación

Pregunta 43 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

Type I
Type II
Type III

Explicación

Pregunta 44 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Type I
Type II
Type III

Explicación

Pregunta 45 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

structure of the imbalance
sample size
model type

Explicación

Pregunta 46 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 )

Explicación

Pregunta 47 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Split-Plot design
Nested design
Repeated Measures design
Blocked design

Explicación

Pregunta 48 de 97

1

Match the design types with the correct image.

Arrastra la opción correcta a cada uno de los puntos naranjas

Two-Way CRD

Split-Plot design

One-Factor Nested design

Repeated Measures

Explicación

Pregunta 49 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 )

Explicación

Pregunta 50 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 51 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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

Explicación

Pregunta 52 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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)

Explicación

Pregunta 53 de 97

1

In which situations can OLS be used?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 54 de 97

1

OLS assumes error in...

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 55 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 56 de 97

1

Selecciona la opción correcta del menú desplegable para completar el texto.

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.

Explicación

Pregunta 57 de 97

1

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

Selecciona uno de los siguientes:

VERDADERO
FALSO

Explicación

Pregunta 58 de 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

Arrastra y suelta para completar el texto.

Prediction Intervals

Prediction Bands

Confidence Intervals

Confidence Bands

Explicación

Pregunta 59 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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

Explicación

Pregunta 60 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

( 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 ))

Explicación

Pregunta 61 de 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.

Arrastra y suelta para completar el texto.

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

Explicación

Pregunta 62 de 97

1

Rellena los espacios en blanco para completar el texto.

r^2 = indicates a perfect line

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

Explicación

Pregunta 63 de 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

Arrastra y suelta para completar el texto.

distribution of Y-values

relation between X and Y

HOV

non-normal distribution of Y values

variance

imposed by transformations

Explicación

Pregunta 64 de 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 ❌

Arrastra y suelta para completar el texto.

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

Explicación

Pregunta 65 de 97

1

ANCOVA is a full design.

Selecciona uno de los siguientes:

VERDADERO
FALSO

Explicación

Pregunta 66 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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.

Explicación

Pregunta 67 de 97

1

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

Selecciona uno de los siguientes:

VERDADERO
FALSO

Explicación

Pregunta 68 de 97

1

ANCOVA is primarily utilized for what purpose?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 69 de 97

1

What is the null hypothesis of ANCOVA?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 70 de 97

1

What is the null hypothesis of Goodness of Fit?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 71 de 97

1

What is the null hypothesis of linear correlation?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 72 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 73 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 74 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 75 de 97

1

What is the main purpose for linear correlation?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 76 de 97

1

What are the assumptions of Pearson's Correlation?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 77 de 97

1

What are the assumptions of Spearman Non-Parametric Correlation?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 78 de 97

1

❌ = Pearson correlation coefficient
❌ = coefficient of determination

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r^2

r

Explicación

Pregunta 79 de 97

1

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

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OLS

Central Axis

Explicación

Pregunta 80 de 97

1

Selecciona la opción correcta de los menús desplegables para completar el texto.

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.

Explicación

Pregunta 81 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 82 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

Spearman
Pearson's
Durbin-Watson
Cook's Distance

Explicación

Pregunta 83 de 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.

Selecciona uno de los siguientes:

VERDADERO
FALSO

Explicación

Pregunta 84 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

observed frequencies
expected frequencies
correlation coefficients
t-values
assigned values

Explicación

Pregunta 85 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 86 de 97

1

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

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 87 de 97

1

What is the null hypothesis for Goodness of Fit?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 88 de 97

1

What is the null hypothesis for Contingency Tables?

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 89 de 97

1

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

Selecciona una de las siguientes respuestas posibles:

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

Explicación

Pregunta 90 de 97

1

What are the assumptions for Goodness of Fit?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 91 de 97

1

What are the assumptions for Contingency Tables?

Selecciona una o más de las siguientes respuestas posibles:

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

Explicación

Pregunta 92 de 97

1

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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.

Explicación

Pregunta 93 de 97

1

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

Explicación

Pregunta 94 de 97

1

Selecciona la opción correcta del menú desplegable para completar el texto.

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.

Explicación

Pregunta 95 de 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.

Selecciona una de las siguientes respuestas posibles:

Pooled G
Heterogeneity G
Goodness of Fit
Contingency Table

Explicación

Pregunta 96 de 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.

Selecciona una de las siguientes respuestas posibles:

Pooled G
Heterogeneity G
Goodness of Fit
Contingency Table

Explicación

Pregunta 97 de 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.

Arrastra y suelta para completar el texto.

One margin fixed, one margin not

Both margins not fixed

Both fixed

very rare

common

	Creado por Jo O'Bar hace casi 2 años

Master's Statistics Test sobre Experimental Design Test 3, creado por Jo O'Bar el 31/10/2022.

Experimental Design Test 3

Experimental Design Test 3

Pregunta 1 de 97

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

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 2 de 97

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

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 3 de 97

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

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 4 de 97

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

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 5 de 97

What are the assumptions of a 2-factor CRD?

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 6 de 97

What are the assumptions of a 3-factor CRD?

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 7 de 97

Selecciona la opción correcta de los menús desplegables para completar el texto.

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 ).

Explicación

Pregunta 8 de 97

Selecciona la opción correcta del menú desplegable para completar el texto.

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.

Explicación

Pregunta 9 de 97

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Explicación

Pregunta 10 de 97

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Explicación

Pregunta 11 de 97

Selecciona la opción correcta de los menús desplegables para completar el texto.

Explicación

Pregunta 12 de 97

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Explicación

Pregunta 13 de 97

Selecciona la opción correcta de los menús desplegables para completar el texto.

Explicación

Pregunta 14 de 97

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Explicación

Pregunta 15 de 97

Selecciona la opción correcta de los menús desplegables para completar el texto.

Explicación

Pregunta 16 de 97

Label the interaction graphs

Arrastra la opción correcta a cada uno de los puntos naranjas

Explicación

Pregunta 17 de 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.

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 18 de 97

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

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 19 de 97

What "logical" sets of simple effects exist?

Selecciona una o más de las siguientes respuestas posibles:

Explicación

Pregunta 20 de 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?

Selecciona una de las siguientes respuestas posibles:

Explicación

Pregunta 21 de 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?

Selecciona una de las siguientes respuestas posibles:

Explicación

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 - ❌