Question

A simple linear regression model
relating a bank lending interest rate and investment in physical
capital by companies is stated as:

- Which variable (lending interest rate or investment in physical
capital) do you think should be the dependent variable in this
regression model? Please justify your
answer.
[
**2 points**]

- What sign would you expect for the slope of this regression
model for interest rate and investment in physical capita? Please
justify your answer.
**[2 points]** - What is the role of the error term in the simple linear
regression model like the one stated above? Please state at least
three examples of the factors that you think belong in the error
term of the simple linear regression model stated above briefly
justifying each example.
**[3 points]**

Answer #1

The data file Demographics was used in a simple linear
regression model where Unemployment Rate is the response variable
and Cost of Living is the explanatory variable. You may refer to
the previous two questions for the regression model if you wish.
The anova function in R was used to obtain the breakdown of the
sums of squares for the regression model. This is shown below: >
anova(myreg)Analysis of Variance Table Response: Unemployment Df
Sum Sq Mean Sq F value...

A regression model was developed relating amount charge to a
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Size and Income (in thousands) of the customer. Part of the
regression results are shown below
1. What is the value of the F test statistic for
testing whether the regression model overall is significant?
(2 Points)
2. What is the rejection rule for testing whether
the regression model is significant at the 0.05 level of
significance? Use the critical...

Following is a simple linear regression model:
The following results were obtained from some statistical
software.
R2 = 0.523
syx (regression standard error) = 3.028
n (total observations) = 41
Significance level = 0.05 = 5%
Variable Parameter Estimate Std. Error of
Parameter Est.
Intercept 0.519 0.132
Slope of X -0.707 0.239
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Consider the simple linear regression model y=10+30x+e where the
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Do NOT use software!
(a) Fit the linear regression model by least squares and find
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(b) Find the estimate of ?^2 .
(c) Find...

In the simple linear regression model estimate Y =
b0 + b1X
A. Y - estimated average predicted value, X –
predictor, Y-intercept (b1), slope
(b0)
B. Y - estimated average predicted value, X –
predictor, Y-intercept (b0), slope
(b1)
C. X - estimated average predicted value, Y –
predictor, Y-intercept (b1), slope
(b0)
D. X - estimated average predicted value, Y –
predictor, Y-intercept (b0), slope
(b1)
The slope (b1)
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You wish to estimate as precisely as possible the slope β1 in
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. , 4.
Each pair of observations (xi , yi) costs $1.00 and your budget
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(a) Make two y-observations at x = 1 and a further two at x =
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(b) Make one y-observation...

Applying Simple Linear Regression to Your favorite Data (Please
confirm with the instructor the dataset you find before you work on
this question) Many dependent variables in business serve as the
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Question 1:
In the normal error simple linear regression model, suppose all
assumptions hold except constancy of error variance with respect to
X. Suppose E(Y) = V(Y). What transformation of y works to stabilize
the variance?
Question 2:
Let Y1,Y2,...Yn be a random sample for a normal distribution with
mean 10 and variance 4. Let Y(k) denote the Kth order statistics of
this random sample. Approximate the probability:
P(Y(k) <= 8.5) , if K =15 and n = 50.
Please...

Please choose the most appropriate description of the concepts
related to the linear model.
The linear relation between two continuous variables
Pearson's r asks the opposite question of this
Type of plot that depicts the relation between X and Y
Person's r has no error term in its formula
Pearson's r can compare two sets of numbers when n1 does not
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Prediction using the linear model
The point on Y when X=0
The rate of change as one...

1.
General features of economic time series: trends, cycles,
seasonality.
2.
Simple linear regression model and multiple regression model:
dependent variable, regressor, error term; fitted value, residuals;
interpretation.
3.
Population VS sample: a sample is a subset of a population.
4.
Estimator VS estimate.
5.
For what kind of models can we use OLS?
6.
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7.
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variance; AIC, BIC.
8.
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hypothesis , significance...

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