4/15/25, 11:07
AM
BUAL 5380 EXAM 2 QUESTIONS AND ANSWERS WITH
COMPLETE SOLUTIONS VERIFIED
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Terms in this set (62)
In regression analysis, the variables used to help explain/predict the
Independent variables:
response variable are called the:
Response variable explained by the The percentage of variation R square can be interpreted as the fraction
regression line: (%) of variation of the:
Outliers are observations that: Lie outside the typical pattern of points on a scarterplot
Scatterplots Especially helpful in identifying outliers:
In linear regression, we can have an interaction variable. Algebraically,
Product
the interaction variable is the other variables in the regression
equation:
The correlation values range from: -1 to +1
The percentage of variation R 0 to +1
square ranges from:
Multiple regression: If they're several explanatory variables, it's called:
In multiple regression, the Y when the associated X value increases by one unit
coefficients reflect the expected
change in:
Forward, backward and stepwise Three types of equation building procedures:
Which best describes parsimony Explaining the most with the least
Another term for constant error Homoscedasticity
variance:
The test statistic in an ANOVA analysis The F statistic
is:
Which isn't assumption of regression? The response variable isn't normally distributed
Which of the following is not one of The standard deviation of the response variable increases as the
the assumptions of regression? explanatory variables increase
Regression Analysis asks: how a single variable depends on other relevant variables
In regression analysis, the variable we dependent variable
are trying to explain or predict are:
In regression analysis, which of the X causes Y to vary, Y causes X to vary, other variables cause both X & Y to vary
following casual relationships are
possible?
A "fan" shape in a scatterplot indicates: unequal variance
A scatterplot that appears as a No relationship among the variables
shapeless mass of data points
indicates:
Correlation is a summary measure that The strength of the linear relationship between pairs of variables
indicates:
All optimization problems have: an objective function and decision variables
1/
3
AM
BUAL 5380 EXAM 2 QUESTIONS AND ANSWERS WITH
COMPLETE SOLUTIONS VERIFIED
Leave the first rating
Save
Terms in this set (62)
In regression analysis, the variables used to help explain/predict the
Independent variables:
response variable are called the:
Response variable explained by the The percentage of variation R square can be interpreted as the fraction
regression line: (%) of variation of the:
Outliers are observations that: Lie outside the typical pattern of points on a scarterplot
Scatterplots Especially helpful in identifying outliers:
In linear regression, we can have an interaction variable. Algebraically,
Product
the interaction variable is the other variables in the regression
equation:
The correlation values range from: -1 to +1
The percentage of variation R 0 to +1
square ranges from:
Multiple regression: If they're several explanatory variables, it's called:
In multiple regression, the Y when the associated X value increases by one unit
coefficients reflect the expected
change in:
Forward, backward and stepwise Three types of equation building procedures:
Which best describes parsimony Explaining the most with the least
Another term for constant error Homoscedasticity
variance:
The test statistic in an ANOVA analysis The F statistic
is:
Which isn't assumption of regression? The response variable isn't normally distributed
Which of the following is not one of The standard deviation of the response variable increases as the
the assumptions of regression? explanatory variables increase
Regression Analysis asks: how a single variable depends on other relevant variables
In regression analysis, the variable we dependent variable
are trying to explain or predict are:
In regression analysis, which of the X causes Y to vary, Y causes X to vary, other variables cause both X & Y to vary
following casual relationships are
possible?
A "fan" shape in a scatterplot indicates: unequal variance
A scatterplot that appears as a No relationship among the variables
shapeless mass of data points
indicates:
Correlation is a summary measure that The strength of the linear relationship between pairs of variables
indicates:
All optimization problems have: an objective function and decision variables
1/
3
