Briefly discuss the fundamental differences between a multiple
regression model, an analysis of variance model and an analysis of
covariance model. Be sure to provide concrete examples of problems
that represent the three types of models.
Distinguish between the following:
Heteroskedasticity and autocorrelation
specified regression model vs estimated regression
equation
data type vs level of measurement
ANOVA and Multiple Regression
Outliers vs Influencers
Distinguish between the following:
Heteroskedasticity and autocorrelation
specified regression model vs estimated regression
equation
data type vs level of measurement
ANOVA and Multiple Regression
Outliers vs Influencers
Based on question 1e above, do you think the following scatter
plots contain any outliers or any influential data points? Justify
your answers on each plot.
(iii)
(iv)
(i)
(ii)
Describe how to use a simple (bivariate) regression model to
carry out a difference in the means test, to estimate a descriptive
statistic, and to estimate an unbiased (or less biased) causal
effect.
1. A multiple linear regression model should not be used
if:
A The variables are all statistically significant.
B The coefficient of determination R2 is large.
C Both of the above.
D Neither of the above.
2. Consider a multiple linear regression model where the output
variable is a company's revenue for
different months, and the purpose is to investigate how the revenue
depends upon the company's advertising budget. The input variables
can be time-lagged so that the first input...
The following simple bivariate linear regression model was
estimated explaining a firm's sales revenue to the income of its
customer’s (INC) using annual data over a nine-year period:Sales Revenue = 81.38 + .23(INC),(0.018) p-vale = 0.001where the standard error of the slope estimate is reported in
parentheses below the coefficient estimatea) Interpret the value of the intercept term and the slope term
in the fitted regression equation.b) Is the coefficient on the income variable statistically
significant?c) What level of sales...
QUESTION 17
The process of creating a linear model of bivariate data.
a.
Least Squares Regression
b.
Variability
c.
Extrapolation
d.
Residual analysis
QUESTION 18
The "Portion of Variability" is also known as the
a.
Correlation coefficient
b.
Regression line
c.
Fitted Value
d.
Coefficient of determination
QUESTION 19
Linear regression models may not always acccurately reflect the
pattern of data from which they are made
a.
TRUE
b.
FALSE
QUESTION 20
The following data relates the time a student...