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    the joint effect of two variables acting together is called _____.

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    Study with Quizlet and memorize flashcards terms like A model in the form of y=Bo+B1Z1+B2Z2+ .. B2Z2 ...+ BpZp+z, A test used to determine whether or not first order autocorrelation is present is:, Which model can be used to accommodate curvilinear relationships between the independent variables and the dependent variables and more.

    In class two

    A model in the form of y=Bo+B1Z1+B2Z2+ .. B2Z2 ...+ BpZp+z

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    General linear model

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    A test used to determine whether or not first order autocorrelation is present is:

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    Durben Watson

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    1/17 Created by Reality_Williams

    Terms in this set (17)

    A model in the form of y=Bo+B1Z1+B2Z2+ .. B2Z2 ...+ BpZp+z

    General linear model

    A test used to determine whether or not first order autocorrelation is present is:

    Durben Watson

    Which model can be used to accommodate curvilinear relationships between the independent variables and the dependent variables

    Quadratic Model

    The following model y+B0+B1x1+e is referred to as a

    1st order model when you want have square term

    In multiple regression analysis, the word linear in the term "general linear model" refers to the fact that

    Bo B1 ext of ex of 1

    Serial correlation is the same as

    auto correlation

    The joint effect of two variables acting together is called

    interaction

    A test to determine whether or not first order autocorrelation is present is:

    durben watson test

    Which of the following teat is used to determine whether an additional variable makes a significant contribution to a multiple regression model>

    Parsal f test

    A variable such as z whose value is z= x1x2 is added to a general linear model in order to account for potential effects of two variables x1 and x2 acting together, this type of effect is

    interaction effect

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    What statistical analysis should be used to test the joint effects of two continous factors on one continous response variable?

    Read 3 answers by scientists to the question asked by Martin Kocsis on Jul 30, 2019

    Question

    Asked 30th Jul, 2019

    Martin Kocsis

    University of Southampton

    What statistical analysis should be used to test the joint effects of two continous factors on one continous response variable?

    I have two continous explanatory variables. I assume that the interplay (joint effect) of these variables can predict a third, continous response variable.

    What statistical test can be used to test whether the two factors and the response have a statistically significant relationship?

    The actual project is aiming to find out whether the interplay of group size and cognitive abilities of members in a primate society can predict the centralisation of the social network.

    Primates Statistical Testing Social Networks Cognitive Joints

    Get help with your research

    Join ResearchGate to ask questions, get input, and advance your work.

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    All Answers (3)

    31st Jul, 2019 Stefan Schulreich

    University of Hamburg

    Dear Martin,

    Sounds like you are interested in what is commonly called an interaction effect. You can test this in a multiple regression where you input the two continuous explanatory variables and their interaction as predictors and the response variable as dependent variable.

    To do this, you need to create the interaction term:

    1. demean/center both continous explanatory variables (i.e. compute the mean for each variable and substract the mean from the individual values of each variable).

    2. compute the interaction term by simply multiplying the demeaned variables (Xdemeaned x Ydemeaned).

    3. Then use the demeaned variables and the interaction term as predictors in a multiple regression as stated in the beginning.

    If the interaction effect gets significant, you can run further analyses to understand how the interaction looks like and how it can be interpreted.

    This can be done via a so-called simple-slopes analysis - Here is a very useful tutorial on that issue (done in SPSS, but same logic in other packages):

    https://www.youtube.com/watch?v=ighASZf3eyA

    Best, Stefan Cite 1 Recommendation 1st Aug, 2019 Claude Houssemand

    University of Luxembourg

    Dear Martin,

    You have to use regression analysis. Your IV will be the two continous factors and its interaction and your DV your continous response variable. You can use SPSS or Psych R Package.

    Best Cite 1st Aug, 2019 Basim Mahmood University of Mosul

    Regression analysis can be involved in such investigations. Then, further deep analysis can be involved just to approve your analysis.

    Good luck Cite 1 Recommendation

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    Term

    regression is used to:

    Definition

    find the best fitting straight line that relates 2 dependent samples in populations

    Term

    correlation is used to :

    Definition

    describe the strength of the linear relationship

    Term

    a correlation coefficient of +/- 1 indicates:

    Definition

    a perfect correlation

    Term

    involes the linear relationship btwn one one dependent variable and multiple dependant variables

    Definition

    multiple regression

    Term

    used often in epidemiology studies:

    Definition

    multiple regression

    Term

    formula for Z score:

    Definition

    score-mean/SD: Z score of +1 = 1SD above the mean

    Term

    which test? two independent grps w subjects randomly assigned to groups

    Definition

    T test

    Term

    3 assumptions in the T test:1. scores represent normal distribution

    Definition

    2. sbj randomly selected/assgnd grps

    3. variences of the 2 grps are equal

    Term

    numerator of the T test represents:

    Definition

    difference btwn grp means

    Term

    denominator of the T test represents:

    Definition

    diff btwn grp variances

    Term

    formula for the degrees of freedom:

    Definition

    df= total # of df - # of grps

    Term

    one vs two tail tests: which is less likely to commit a Type II error?

    Definition

    less likely to commit a type II error using a one tailed test

    Term

    useful test for when the experimental design is one group that receives before/after tx:

    Definition

    T test for paired data

    Term

    Z scores are appropriate for alrge samples but the _____ is more commonly used for small samples (n<30)

    Definition

    t-distribution

    Term

    max # of grps you can compare w a T test:

    Definition

    2

    Term

    a stat method used to test for differences among three or more tx means:

    Definition

    ANOVA

    Term

    2 assumptions of NOVA

    Definition

    normally distributed data

    no sig diff btwn grp variances

    Term

    instead of a T-test the ANOVA has an:

    Definition

    F test

    Term

    F ratio formula:

    Definition

    F = btwn grp variance/within grp variance

    Term

    ANOVA used to investigate the effects of ea of several independent variables and the joint effect of these variables acting together:

    Definition

    factorial ANOVA

    Term

    the difference btwn the tx variables are called:

    Definition

    main effects

    Term

    the ______ effect is due to the joint action of the tx variables:

    Definition

    interaction effect

    Term

    multiple comparison test conducted after the primary analysis has rejected the null hypothesis:

    Definition

    Post-hoc comparisons

    Term

    multiple comparison test: comparisons of hypotheses specified before the analysis commenced; appropriate even when F test is not significant:

    Definition

    planned comparisons

    Term

    4 ex of multiple comparison tests:1. honestly significant difference

    Definition 2. newman-keuls

    3. multiple range test

    4. scheffe's method Term

    analysis used when levels of an independent variable are ordered along a continuum.

    Definition

    trend analysis

    Term

    test most used for frequency or count data; ex: (# of men,# of deaths, # women w/wo dz)

    Definition

    Chi square

    Term

    basic premise of Chi sq: number of sample observations in each category of ea variable:

    Definition

    observed frequencies

    Term

    basic premise of CHi sq: # of sample observations in ea category if the null hypothesis is true:

    Definition

    expected frequencies

    Term

    4 types of chi sq test:1. goodness of fit

    Definition

    2. test of independence

    3. test of homogeneity

    4. McNemar test Term

    proportions of a normal curve: +/- 1SD = what% of scores?

    Definition

    68.26%

    Term

    proportions of normal curve: what % is within 2sd of mean?

    Definition

    95.44%

    Term

    proportions of normal curve: what % is within 3SD from mean?

    Definition

    99.73%

    Term

    a Z score of 1 indicates:

    Definition

    the score is 1 SD above the mean

    Term

    what % of 14yo have cholesterol of >240?

    mean cholesterol @ 14 = 170+/-35

    Definition

    240-170/35 = 2SD above mean; 2.325% of 14yo have chol>240

    Term

    likelihood that any one event will occur, given all possible outcomes:

    Definition

    probability

    Term

    SD of a theoretical sampling distribution of means:

    Definition

    standard error of the mean

    Term

    null hypotheses also reffered to as:

    Definition

    statistical hypothesis

    Term

    concluding that a difference exists when none actually does

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