Question

In the multiple linear regression model with estimation by ordinary least squares, why should we make an analysis of the scatter plot between each covariable xij, j = 1, 2,. . . ,p with the residues ei?

Answer #1

In the multiple linear regression model with estimation by
ordinary least squares, is it really necessary to perform the
normality analysis of the residues? What if the errors are not
normal? How to proceed with the tests if the errors have a
t-Student distribution with 5 degrees of freedom? (Do not confuse
model errors with waste!)

Multiple Linear Regression
We consider the misspecification problem in multiple linear
regression. Suppose that the following model is adopted y = X1β1 +
ε while the true model is y = X1β1 + X2β2 + ε. For both models, we
assume E(ε) = 0 and V (ε) = σ^2I. Figure out conditions under which
the least squares estimate we obtained is unbiased.

True or False: In the simple regression model, both ordinary
least squares (OLS) and Method of Moments estimators produce
identical estimates. Explain.

Showing that residuals, , from the least squares fit of the
simple linear regression model sum to zero

In linear regression, the independent variable is called the
a. Response Variable
b. The explanatory variable
c. The extrapolted variable
d. an outlier
A graph that will help to one to see what type of curve might
best fit the bivariate data
a. Pie chart
b. stem-leaf plot
c. dot plot
d. scatter plot
The technique of extending a regression line beyond the region
of the actual data
a. Least Squares Regression
b. Variability
c. Extrapolation
d. Residual analysis
The...

If we have a multiple linear regression model:
lm(life ~ male + birth + divo + beds + educ + inco, data =
DATA)
(1) What R command should we use to plot it standardized residuals
against the FITTED values?
(2) What R command should we use to compute and plot the leverage
of each point and identify the points that have a leverage larger
than 0.5?
(3) What R command should we use to compute the Cook's distance for...

why should we remove non-significant variables from multiple
linear regression models? What problems may arise if we keep them
in the model?

Do you think Ordinary Least Squares and logistic regression
yield equally valid results? Why or why not?

how do we get error sum of squares in a multiple
linear regression with two independent variables x1 and x2

What are the requirements of the least-squares regression
model?
What is a correlation matrix?
How can you use technology to find a multiple regression
equation?
Why is it important to perform graphical as well as analytical
analyses when analyzing relations between two quantitative
variables?

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