Regression model building by using excel (transferring to minitab17)

Anonymous
account_balance_wallet \$9.99

Question Description

The question/requirements are as follows on the attached items. I am very confused with this, mostly with the wording, I would appreciate anything if someone could help me. Thank you. And please no plagiarism. This is for a statistics course, I am doing good in class but have no idea where to begin with this assignment/project, I am not even being lazy, I have been trying to figure it out for about two weeks now, so that is why I am posting this on studypool.

Unformatted Attachment Preview

Purchase answer to see full attachment

Ace_Tutor
School: Cornell University

attached is my work

Comparison between Simple Linear Regression and “final best” Multiple Linear Regression

Introduction:
The objectives of this report are to apply both simple linear regression and multiple linear
regression model to predict the response variable ‘5-Year Return’ and then to compare both
methods using different approaches. In the simple linear model, the predictor variable is ‘3-Year
Return’ and in the multiple linear model, the predictor variables are Expense Ratio, 1-Year
Return, 3-Year Return, Assets, and ObjectiveCODE. The residual plots for both models are
Simple Linear Regression:
The simple linear regression model demonstrates the relationship between 3-year-return and 5year-return can be described as:
Y X  

where X = 3-Year Return and Y = 5-Year Return.
A. The scatter plot and the Minitab printout for this simple linear regression model are shown
below:
Regression Analysis: 5-Year Return versus 3-Year Return
The regression equation is
5-Year Return = - 3.258 + 0.9987 3-Year Return
S = 2.65307

R-Sq = 70.1%

Analysis of Variance
Source
Regression
Error
Total

DF
1
866
867

SS
14294.9
6095.6
20390.5

MS
14294.9
7.0

F
2030.89

P
0.000

Fitted Line Plot

5-Year Return = - 3.258 + 0.9987 3-Year Return
30

S
R-Sq

2.65307
70.1%
70.1%

5-Year Return

20

10

0

-10

-20
-10

0

10

20

30

3-Year Return

B. The sample regression equation is

5  Year Return   3.258  0.9987 * 3  Year Return
C. For our fitted model, Y intercept = -3.258 means that the 5-Year Return is equal to -3.258%
when the 3-Year Return is 0. In addition, the slope of 0.9987 means that for each increment of 3Year Return by 1%, the 5-Year Return will increase by the amount of 0.9987%.
D. The coefficient of determination r2 = 70.1% implies that there is 70.1% of the variance in 5Year Return that is predictable from 3-Year Return.
E. The standard error of the estimate SYX = 2.65307 means that the variability of predictions in
the regression model is 2.65307.
F. The residual plots of the given model can be generated as follow:

According to the above residual plots, it appears that the data is pretty symmetrically distributed,
as it tends to cluster around the middle of the plot. The plots are evenly distributed vertically;
however, there may be a few outliers at both ends of the plot.
G. The assumptions are met and the fitted model is appropriate and statistically significant. A
value for our independent variable 3-Year Return can be selected as x  23.6. Therefore,
(1) Y can be predicted as

Y  3.258  0.9987 x
 3.258  0.9987  23.6 
 20.3
(2) The 95% confidence interval estimate of the average value of Y is calculated by

95%CI     t1 /2,nk 1  SE ,   t1 /2,n k 1  SE 
  0.9987  1.963  2.65307, 0.9987  1.963  2.65307 
  4.2085, 6.2059 
(3) The 95% prediction interval estimate of the average value of Y is calculated by

95% PI  y  t1 /2,n 2 SE , y  t1 /2, n 2 SE

  20.3  1.963  2.65307, 20.3  1.963  2.65307 
 15.092, 25.508 

“Final best” Multiple Linear Regression:
A. The scatterplot matrix that demonstrates the possible relationships of the numerical dependent
variable Y with each potential predictor variable can be developed as follow:

Matrix Plot of 5-Year Retur, Expense Rati, 1-Year Retur, ...
0.0

1.5

0

3.0

15

0.0

30

0.5

1.0
20

5-Year Return

0
-20

3.0
1.5

Expense Ratio

0.0

40
20

1-Year Return

0

30
15

3-Year...

flag Report DMCA
Review

Anonymous
Tutor went the extra mile to help me with this essay. Citations were a bit shaky but I appreciated how well he handled APA styles and how ok he was to change them even though I didnt specify. Got a B+ which is believable and acceptable.

Brown University

1271 Tutors

California Institute of Technology

2131 Tutors

Carnegie Mellon University

982 Tutors

Columbia University

1256 Tutors

Dartmouth University

2113 Tutors

Emory University

2279 Tutors

Harvard University

599 Tutors

Massachusetts Institute of Technology

2319 Tutors

New York University

1645 Tutors

Notre Dam University

1911 Tutors

Oklahoma University

2122 Tutors

Pennsylvania State University

932 Tutors

Princeton University

1211 Tutors

Stanford University

983 Tutors

University of California

1282 Tutors

Oxford University

123 Tutors

Yale University

2325 Tutors