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68問 • 1年前
  • Geneva Pahil
    • 通報

    問題一覧

  • 1

    It is where the model predicts displacement perfectly

    Deterministic linear relationship

  • 2

    This is a graph on which NY pair is represented as a point plotted in a two dimensional coordinate system

    Scatter diagram

  • 3

    Who produced scatter diagram

    Minitab

  • 4

    It is the slope and intercept of the line

    Regression coefficient

  • 5

    It is the mean value function

    Linear model

  • 6

    E is

    Random error term

  • 7

    It has only one independent variable

    Simple linear regression model

  • 8

    In the model it has only one independent variable called

    Regressor

  • 9

    When does simple in your regression model arise

    Theoretical relationship

  • 10

    It is the line of mean values

    True regression model

  • 11

    It is interpreted as the change in the mean of Y for our unit change in X

    Slope

  • 12

    The variability of why, as a particular value of X is determined by

    Error variance two

  • 13

    He used the term regression analysis in study of the height of fathers and sons

    Sir Francis Galton

  • 14

    Sir Francis Galton first use the term regression analysis in the study of

    Height of fathers and sons

  • 15

    This is referred to as regression line

    Least squares line

  • 16

    It may even a beer to provide a good fit to the data, but the relationship is an unreasonable one on which to rely

    Straight line

  • 17

    The only way to determine cause-and-effect relationships

    Designed experiments

  • 18

    Regression relationships are only valid only for values of this variable within the range of the original data

    Regressor

  • 19

    They are not necessarily valid for extrapolation purposes

    Regression models

  • 20

    This is an independent variable

    Regressor or predictor variable

  • 21

    Dependent variable

    Response variable

  • 22

    E equals Y minus the mean of Y is called

    Who is

  • 23

    It describes the error in the fifth of the model to the end observation Y

    Residuals

  • 24

    The unknown parameter in regression model

    Variance

  • 25

    It is the sum of the squares of the residuals

    Error sum of squares

  • 26

    It is the important part of assessing the adequacy of a linear regression model about the model parameters, and constructing certain confidence interval

    Hypothesis test

  • 27

    Hypothesis related to

    Significance of regression

  • 28

    Equivalence to the concluding that there is no linear relationship between X and Y

    Reject null hypothesis

  • 29

    Rejecting non-hypothesis could mean that the model is adequate

    Straight line

  • 30

    It is used to test for significance of regression

    Analysis of variance

  • 31

    It is where the procedure partitions the total variability in response variable into many meaningful components as the basis for the test

    Analysis of variance

  • 32

    It is an important application of a regression model corresponding to specified level of the aggressor available

    Prediction of new observations

  • 33

    These are frequently helpful in tracking assumption that the errors are approximately normally distributed with constant variance, and in determining whether additional terms in the model would be useful

    Residuals

  • 34

    It will always increase if we add variable to the model, but it does not necessarily imply that the new model is superior to the old one

    Coefficient of determination

  • 35

    It does not measure the magnitude of the slope of the regression line

    Coefficient of determination

  • 36

    Experiment in which only a single factor varies while all others are kept constant

    Single factor experiment

  • 37

    This is where small number of treatment characterized by blocks, each of which contains at least one complete set of treatments

    Complete block design

  • 38

    What are the three complete block design?

    CRD are completely randomized design, Randomize complete block design, Latin Square design

  • 39

    Are large number of treatments characterized by blocks each of which contains only a fraction of the treatments to be tested

    Incomplete block design

  • 40

    What are the two incomplete block design

    Lattice design, Group balanced block design

  • 41

    Are design used or appropriate experiments with homogenous units such as laboratory experiments

    Completely randomize design

  • 42

    So, for field experiments were the number of treatment is not large and area has predictable, productivity gradient blocks of equalize each contains all the treatments

    Randomized complete block design

  • 43

    It is applied separately and independently to each of the blocks

    Randomization

  • 44

    It is used to show the relationship between two variables

    Scatterplot or scatter diagram

  • 45

    Used to measure strength of the association between two variables

    Correlation analysis

  • 46

    Only concerned with the strength of the relationship

    Correlation analysis

  • 47

    Treatments consist of all possible combination in selected levels two or more factor

    Factorial experiment

  • 48

    Treatments include all combination of the selected levels and variable factors

    Complete factorial experiment

  • 49

    It is simply ignoring the factor composition of the factorial treatments and a competition. Discuss.

    RCBD factorial

  • 50

    It is a sign which allows the level of one factor to be applied to large plots well, the levels of another factor applied to small plots

    Split plot design

  • 51

    are whole plots or main plots

    Large plots

  • 52

    This are split plots or subplot

    Smaller plots

  • 53

    It is suited for two factor experiment that is more treatments that can be accumulated by CBD

    Split plot design

  • 54

    What are the two factors of split plot design?

    Main plot factor, Sub plot factor

  • 55

    Randomly assigned to the main plots using different randomization for each block

    Levels of the whole plot

  • 56

    Randomly assigned within each main plot, using a separate randomization for each main plot

    Levels of subplots

  • 57

    Guidelines for assignment of a particular factor to either the main plot or subplot blood

    Degree of precision, Relative size of the main effects, Management practices

  • 58

    It is estimated with less position, so larger differences are required for significance. It may be difficult obtain adequate DF for the main blood factor.

    Main plot factor

  • 59

    It’s more complex because different standard errors are required for different comparisons

    Statistical analysis

  • 60

    It estimate and test the appropriate main effects and interactions

    Data analysis

  • 61

    Sometimes called split block sign

    Strip plot design

  • 62

    These are experiments involving factors that are difficult to apply to small plots

    Strip plot design

  • 63

    This is measured with greater precision than the main effects

    Intersection

  • 64

    Interaction between the two factor

    Intersection plot

  • 65

    Vertical factor

    Vertical strip plot

  • 66

    Horizontal factor

    Horizontal strip plot

  • 67

    Permits, efficient, application of doctors that would be difficult to apply to small plots

    Strip plot design

  • 68

    ANOVA Stands for

    Analysis of variance

    • GEL

    GEL

    Geneva Pahil · 63問 · 1年前

    GEL

    GEL

    63問 • 1年前
    Geneva Pahil

    visayas

    visayas

    Geneva Pahil · 100問 · 1年前

    visayas

    visayas

    100問 • 1年前
    Geneva Pahil

    Visayas Inday 2

    Visayas Inday 2

    Geneva Pahil · 70問 · 1年前

    Visayas Inday 2

    Visayas Inday 2

    70問 • 1年前
    Geneva Pahil

    Visayas Aklan

    Visayas Aklan

    Geneva Pahil · 100問 · 1年前

    Visayas Aklan

    Visayas Aklan

    100問 • 1年前
    Geneva Pahil

    Subanon

    Subanon

    Geneva Pahil · 30問 · 1年前

    Subanon

    Subanon

    30問 • 1年前
    Geneva Pahil

    Matigsalug

    Matigsalug

    Geneva Pahil · 7問 · 1年前

    Matigsalug

    Matigsalug

    7問 • 1年前
    Geneva Pahil

    Ata Manobo

    Ata Manobo

    Geneva Pahil · 23問 · 1年前

    Ata Manobo

    Ata Manobo

    23問 • 1年前
    Geneva Pahil

    Bagobo Kiata

    Bagobo Kiata

    Geneva Pahil · 8問 · 1年前

    Bagobo Kiata

    Bagobo Kiata

    8問 • 1年前
    Geneva Pahil

    Bagobo Tagabawa

    Bagobo Tagabawa

    Geneva Pahil · 6問 · 1年前

    Bagobo Tagabawa

    Bagobo Tagabawa

    6問 • 1年前
    Geneva Pahil

    Iranun

    Iranun

    Geneva Pahil · 7問 · 1年前

    Iranun

    Iranun

    7問 • 1年前
    Geneva Pahil

    Kagan

    Kagan

    Geneva Pahil · 6問 · 1年前

    Kagan

    Kagan

    6問 • 1年前
    Geneva Pahil

    OBU-MANUVU

    OBU-MANUVU

    Geneva Pahil · 12問 · 1年前

    OBU-MANUVU

    OBU-MANUVU

    12問 • 1年前
    Geneva Pahil

    TAOSUG

    TAOSUG

    Geneva Pahil · 6問 · 1年前

    TAOSUG

    TAOSUG

    6問 • 1年前
    Geneva Pahil

    SAMA

    SAMA

    Geneva Pahil · 9問 · 1年前

    SAMA

    SAMA

    9問 • 1年前
    Geneva Pahil

    MARANAO

    MARANAO

    Geneva Pahil · 7問 · 1年前

    MARANAO

    MARANAO

    7問 • 1年前
    Geneva Pahil

    BLAAN

    BLAAN

    Geneva Pahil · 18問 · 1年前

    BLAAN

    BLAAN

    18問 • 1年前
    Geneva Pahil

    Tagakaulo

    Tagakaulo

    Geneva Pahil · 12問 · 1年前

    Tagakaulo

    Tagakaulo

    12問 • 1年前
    Geneva Pahil

    Manobo

    Manobo

    Geneva Pahil · 19問 · 1年前

    Manobo

    Manobo

    19問 • 1年前
    Geneva Pahil

    T’boli

    T’boli

    Geneva Pahil · 15問 · 1年前

    T’boli

    T’boli

    15問 • 1年前
    Geneva Pahil

    Tiruray

    Tiruray

    Geneva Pahil · 13問 · 1年前

    Tiruray

    Tiruray

    13問 • 1年前
    Geneva Pahil

    問題一覧

  • 1

    It is where the model predicts displacement perfectly

    Deterministic linear relationship

  • 2

    This is a graph on which NY pair is represented as a point plotted in a two dimensional coordinate system

    Scatter diagram

  • 3

    Who produced scatter diagram

    Minitab

  • 4

    It is the slope and intercept of the line

    Regression coefficient

  • 5

    It is the mean value function

    Linear model

  • 6

    E is

    Random error term

  • 7

    It has only one independent variable

    Simple linear regression model

  • 8

    In the model it has only one independent variable called

    Regressor

  • 9

    When does simple in your regression model arise

    Theoretical relationship

  • 10

    It is the line of mean values

    True regression model

  • 11

    It is interpreted as the change in the mean of Y for our unit change in X

    Slope

  • 12

    The variability of why, as a particular value of X is determined by

    Error variance two

  • 13

    He used the term regression analysis in study of the height of fathers and sons

    Sir Francis Galton

  • 14

    Sir Francis Galton first use the term regression analysis in the study of

    Height of fathers and sons

  • 15

    This is referred to as regression line

    Least squares line

  • 16

    It may even a beer to provide a good fit to the data, but the relationship is an unreasonable one on which to rely

    Straight line

  • 17

    The only way to determine cause-and-effect relationships

    Designed experiments

  • 18

    Regression relationships are only valid only for values of this variable within the range of the original data

    Regressor

  • 19

    They are not necessarily valid for extrapolation purposes

    Regression models

  • 20

    This is an independent variable

    Regressor or predictor variable

  • 21

    Dependent variable

    Response variable

  • 22

    E equals Y minus the mean of Y is called

    Who is

  • 23

    It describes the error in the fifth of the model to the end observation Y

    Residuals

  • 24

    The unknown parameter in regression model

    Variance

  • 25

    It is the sum of the squares of the residuals

    Error sum of squares

  • 26

    It is the important part of assessing the adequacy of a linear regression model about the model parameters, and constructing certain confidence interval

    Hypothesis test

  • 27

    Hypothesis related to

    Significance of regression

  • 28

    Equivalence to the concluding that there is no linear relationship between X and Y

    Reject null hypothesis

  • 29

    Rejecting non-hypothesis could mean that the model is adequate

    Straight line

  • 30

    It is used to test for significance of regression

    Analysis of variance

  • 31

    It is where the procedure partitions the total variability in response variable into many meaningful components as the basis for the test

    Analysis of variance

  • 32

    It is an important application of a regression model corresponding to specified level of the aggressor available

    Prediction of new observations

  • 33

    These are frequently helpful in tracking assumption that the errors are approximately normally distributed with constant variance, and in determining whether additional terms in the model would be useful

    Residuals

  • 34

    It will always increase if we add variable to the model, but it does not necessarily imply that the new model is superior to the old one

    Coefficient of determination

  • 35

    It does not measure the magnitude of the slope of the regression line

    Coefficient of determination

  • 36

    Experiment in which only a single factor varies while all others are kept constant

    Single factor experiment

  • 37

    This is where small number of treatment characterized by blocks, each of which contains at least one complete set of treatments

    Complete block design

  • 38

    What are the three complete block design?

    CRD are completely randomized design, Randomize complete block design, Latin Square design

  • 39

    Are large number of treatments characterized by blocks each of which contains only a fraction of the treatments to be tested

    Incomplete block design

  • 40

    What are the two incomplete block design

    Lattice design, Group balanced block design

  • 41

    Are design used or appropriate experiments with homogenous units such as laboratory experiments

    Completely randomize design

  • 42

    So, for field experiments were the number of treatment is not large and area has predictable, productivity gradient blocks of equalize each contains all the treatments

    Randomized complete block design

  • 43

    It is applied separately and independently to each of the blocks

    Randomization

  • 44

    It is used to show the relationship between two variables

    Scatterplot or scatter diagram

  • 45

    Used to measure strength of the association between two variables

    Correlation analysis

  • 46

    Only concerned with the strength of the relationship

    Correlation analysis

  • 47

    Treatments consist of all possible combination in selected levels two or more factor

    Factorial experiment

  • 48

    Treatments include all combination of the selected levels and variable factors

    Complete factorial experiment

  • 49

    It is simply ignoring the factor composition of the factorial treatments and a competition. Discuss.

    RCBD factorial

  • 50

    It is a sign which allows the level of one factor to be applied to large plots well, the levels of another factor applied to small plots

    Split plot design

  • 51

    are whole plots or main plots

    Large plots

  • 52

    This are split plots or subplot

    Smaller plots

  • 53

    It is suited for two factor experiment that is more treatments that can be accumulated by CBD

    Split plot design

  • 54

    What are the two factors of split plot design?

    Main plot factor, Sub plot factor

  • 55

    Randomly assigned to the main plots using different randomization for each block

    Levels of the whole plot

  • 56

    Randomly assigned within each main plot, using a separate randomization for each main plot

    Levels of subplots

  • 57

    Guidelines for assignment of a particular factor to either the main plot or subplot blood

    Degree of precision, Relative size of the main effects, Management practices

  • 58

    It is estimated with less position, so larger differences are required for significance. It may be difficult obtain adequate DF for the main blood factor.

    Main plot factor

  • 59

    It’s more complex because different standard errors are required for different comparisons

    Statistical analysis

  • 60

    It estimate and test the appropriate main effects and interactions

    Data analysis

  • 61

    Sometimes called split block sign

    Strip plot design

  • 62

    These are experiments involving factors that are difficult to apply to small plots

    Strip plot design

  • 63

    This is measured with greater precision than the main effects

    Intersection

  • 64

    Interaction between the two factor

    Intersection plot

  • 65

    Vertical factor

    Vertical strip plot

  • 66

    Horizontal factor

    Horizontal strip plot

  • 67

    Permits, efficient, application of doctors that would be difficult to apply to small plots

    Strip plot design

  • 68

    ANOVA Stands for

    Analysis of variance