EXSC 520 CASE STUDY T-TEST COMPARING
MEANS COMPREHENSIVE EXAM 2026 TWO
SETS DATA STUDY GUIDE Q&A UPDATED
⩥ Curvilinear. Answer: Plot of bivariate data where the best fit line is
not straight. Is a measure of linear relationship. The curved line
represents the relationship between assumed by pearson's coefficient.
The true relationship is curvilinear and strong.
⩥ Bivariate Regression background. Answer: Regression analysis
applied to one independent variable and one dependent variable.(simple
linear progression).
⩥ Standard deviation residuals. Answer: The vertical distance from any
point in a scatter diagram to the line of the best fit.
⩥ Bivariate regression residuals,. Answer: The vertical distance to any
point to the line.
⩥ Standard error of the estimate. Answer: a numerical value that
indicates the amount of error in prediction of a Y value in a bivariate or
multivariate regression. Interpreted as the standard deviation of the
errors, or residuals, made when predicting Y from X.
, ⩥ homoscedasticity. Answer: an assumption of parametric inferential
statistics is that the data are normally distributed. a further assumption of
regression analysis is called
⩥ Multiple regression background. Answer: Regression analysis applied
to more than one independent variables(X1,2,3) and one dependent
variable(Y).
⩥ Muticolinearity. Answer: A condition in which two or more
independent variables in multiple regression are highly correlated with
each other. It leads to 2 correlated problems.
⩥ Variance inflation factor. Answer: (VIF), an index of extent of
multicollinearity in a data set . Indices that help us to calculate quantify
the amount of multicollinearity in the data.
⩥ Tolerance. Answer: The denominator of the variance inflation
factor(VIF).
⩥ T TEST,. Answer: we compare a single sample mean against the mean
from a known population value. It is the technique by which we perform
this analysis. The T test is useful for conducting experimental research.
The t test may be modified to make comparison between observed
proportions (pag 172).
MEANS COMPREHENSIVE EXAM 2026 TWO
SETS DATA STUDY GUIDE Q&A UPDATED
⩥ Curvilinear. Answer: Plot of bivariate data where the best fit line is
not straight. Is a measure of linear relationship. The curved line
represents the relationship between assumed by pearson's coefficient.
The true relationship is curvilinear and strong.
⩥ Bivariate Regression background. Answer: Regression analysis
applied to one independent variable and one dependent variable.(simple
linear progression).
⩥ Standard deviation residuals. Answer: The vertical distance from any
point in a scatter diagram to the line of the best fit.
⩥ Bivariate regression residuals,. Answer: The vertical distance to any
point to the line.
⩥ Standard error of the estimate. Answer: a numerical value that
indicates the amount of error in prediction of a Y value in a bivariate or
multivariate regression. Interpreted as the standard deviation of the
errors, or residuals, made when predicting Y from X.
, ⩥ homoscedasticity. Answer: an assumption of parametric inferential
statistics is that the data are normally distributed. a further assumption of
regression analysis is called
⩥ Multiple regression background. Answer: Regression analysis applied
to more than one independent variables(X1,2,3) and one dependent
variable(Y).
⩥ Muticolinearity. Answer: A condition in which two or more
independent variables in multiple regression are highly correlated with
each other. It leads to 2 correlated problems.
⩥ Variance inflation factor. Answer: (VIF), an index of extent of
multicollinearity in a data set . Indices that help us to calculate quantify
the amount of multicollinearity in the data.
⩥ Tolerance. Answer: The denominator of the variance inflation
factor(VIF).
⩥ T TEST,. Answer: we compare a single sample mean against the mean
from a known population value. It is the technique by which we perform
this analysis. The T test is useful for conducting experimental research.
The t test may be modified to make comparison between observed
proportions (pag 172).
