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Business & Marketing
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Topic:

Business Analytic Tools (Coursework Sample)

Instructions:

The task required me to read notes on goodness of fit, simple linear regression, multiple linear regression, categorical regression and model building and use the knowldge from the notes to answer the questions. The sample includes the answers to the questions provided. The data was analyzed using Microsoft excel.

source..
Content:

Question 1 
In the graph of the simple linear regression equation, the parameter  is the ___________ of the regression line.
Question 1 options:

a) slope


b) end-point


c) y-intercept


d) x-intercept

Question 2 
The Least Squares method is based on calculus finding the point that minimizes the errors.
Question 2 options:

True


False

Question 3 
In the simple linear regression model, the ____________ accounts for the variability in the dependent variable that cannot be explained by the linear relationship between the variables.
Question 3 options:

a) residual


b) error term


c) constant term


d) model parameter

Question 4The F test has a value of 0.04999 therefore; you reject this one variable's null hypothesis.
Question 4 options:

True


False

Question 5 
Identify the dangers in regression analysis:
Question 5 options:

a) prediction


b) extrapolation


c) very large datasets


d) quadratic models


e) not performing L.I.N.E.

Question 6 
______________ refers to the degree of correlation among independent variables in a regression model.
Question 6 options:

a) Multicollinearity


b) Rank


c) Confidence level


d) Tolerance

Question 7 
Multicollinearity can be identified by (select all that apply):
Question 7 options:

a) degrees of freedom


b) Adjusted R2


c) correlations


d) the intercept's p-value


e) VIFs

Question 8 
Overfitting is considered bad because the VIF value is increased.
Question 8 options:

True


False

Question 9 
The proper structure for the categorical variable below is:
Home Construction:  Brick, Wood, Stone, Metal, Hybrid
Question 9 options:

a) Brick: 0, 1          Wood: 0, 1Stone: 0, 1         Metal: 0, 1Hybrid: 0, 1


b) Brick: 0, 1          Wood: 0, 1Stone: 0, 1         Metal: 0, 1


c) Construction:  0, 1, 2, 3, 4


d) Construction:  1, 2, 3, 4


e) Construction:  1, 2, 3, 4, 5

Question 10 
When a categorical variable has k values, the encoding process will transform that into k "dummy variables".
Question 10 options:

True


False

Question 11 
y=b0+b1x1+b2(x1−x(k))xk  is an example of:
Question 11 options:

a) a linear regression model with two independent variables


b) a piecewise linear regression model


c) a model that this class did not study


d) an incorrect quadratic regression model

Question 12 
Stepwise Regression:
Question 12 options:

a) uses a "knot" to identify where the independent variable's best coefficient value.


b) begins with the independent variable with the lowest SSE, then adds a variable and removes a variable so long as pre-specified criterion are met.


c) incrementally adds one independent variable at a time based on its SSE.


d) begins with a regression model for every possible combination of independent variables, then selects the model with the best goodness of fit statistics.

Using a linear regression line, what is the value of b0 if:
b1= 8.3     y= 36.1      x = 4
(Keep one decimal place)
Your Answer:
Question 13 options:

Answer

Question 14 
Question 14 options:
Download Q 2 (download) for this problem.
How much of the data's variation is explained by this simple linear regression?
(Keep four decimal places)
True / False:  Based on R2, the F test, and the p value(s),  we should not use this model to represent this data.
What is the average distance that the observed values fall from the regression line?
(Keep two decimal places)
b1 = 
(Keep four decimal places)
Question 15 
Download   HYPERLINK "https://elearn.memphis.edu/d2l/common/dialogs/quickLink/quickLink.d2l?ou=8789758&type=coursefile&fileId=Module+2%2fChapter+7b+files%2fQ+1.xlsx" \t "_blank" Q 1 (download) and generate the simple linear regression model.
Predict the annual maintenance expense if

...
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