3003PSY EOT EXAM - Survey Design and Analysis. || with A+ Guaranteed Solutions.
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3003PSY EOT
Bivariate regression is one predictor and one outcome, and multiple regression is one outcome with multiple predictors correct answers What is the difference between Bivariate and multiple regression?
Tells us that residuals are normal and most likely centered around 0. correct answers in regres...
3003PSY EOT EXAM - Survey Design and Analysis. || with
A+ Guaranteed Solutions.
Bivariate regression is one predictor and one outcome, and multiple regression is one outcome
with multiple predictors correct answers What is the difference between Bivariate and multiple
regression?
Tells us that residuals are normal and most likely centered around 0. correct answers in
regression what does normality tell us?
ÿ is the mean.
Y' is the predicted value of Y
Y is the outcome variable correct answers What is ÿ, Y' and Y?
margin of error is how often we are wrong in predicting scores. correct answers What is margin
of error?
it is the mean of the outcome variable. This mean is the baseline of predictions.
*note: Y is the outcome variable and x is the predictor variable* correct answers what is Ÿ?
A dummy Variable is a numerical value used in regression to represent sub groups of the sample.
Usually used with dichotomous variables.
e.g. Men = 0 and women = 1. correct answers What is a dummy variable?
A dichotomous variable is a variable that takes on one of only 2 possible values when measured.
e.g. you are either a man or a women, or you're either over 65 or under 65.
When using dichotomous variables as a predictor, we are measuring if for example, being male
or female predicts scores. correct answers What is a dichotomous variable?
slope of the line of best fit correct answers The unstandardized regression coefficient
corresponds by default to what part of a
regression? (bo)
the expected score when all predictors are 0 correct answers The intercept corresponds by default
to what part of a regression? (b1)
Beta weights is the standardised version of b weights. correct answers what is the difference
between beta weights and b weights?
, It tells us the partial slopes. Tells us how much the predicted score on the outcome (DV) will
increase for every scale point on the predictor, when all other predictors are held constant.
e.g. For each point on the interpersonal growth scale (PREDICTOR), psychological adjustment
(OUTCOME) decreases by .211 points. correct answers what does each 'b' weight tell us in
multiple regression?
Y = b0 + b1X1 + b2X2.... + e correct answers what is the unstandardised regression equation
the total variance in data set in regression. difference between mean and actual scores. correct
answers What is sums of square total?
SSreg is how much of total variation can be accounted for by knowing the relationship between
variables. How much the predictor scores lies from the mean.
(Y' - Ÿ) correct answers What is sums of squares regression?
SSres is how much actual scores vary from predicted scores. its the residuals between scores.
(Y-Y') correct answers What is sums of square residuals?
To work out variance explained we use R2 in the model summary. We can also work this out by
dividing ssreg (Y' - ÿ) by sstotal (Y - ÿ). correct answers How do we work out the amount of
variance in Y that can be explained by X? (variance explained)
Least squared is the means regression equation. The least squared approach is most common
approach to estimate coefficients. correct answers What is least squared?
1. Model Specification (where we select variables that we think are important in predicting the
outcome)
2. Parameter estimation (where stat analysis provides us with output)
3. Model checking/fit assessment
4. prediction correct answers what are the stages of linear modeling?
The significance in the anova model table tell us if predictors are significantly different from 0
under the null hypothesis.
If the predictors are significantly different from 0, then we can reject our null hypotheses that
there will be no significant difference. correct answers how do we tell if predictors are
significantly different from 0? and why do we need to tell this?
describes the linear relationship between 2 variables in a sample.
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