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a) perform a best subsets regression and choose the most appropriate model for these data A) y= (__) + (__)x1 + (__)x3 + (__)4  B) y= (__) + (__)x1 + (__)x2 + (__)x3 + (__)x4  C) y= (__) + (__)x1 + (__)x3  D) y= (__) + (__)x1 + (__)x2 + (__)x3  b) perform a residual analysis to determine if the conditions for the model are met  -Is there an obvious pattern in this plot?

a) perform a best subsets regression and choose the most appropriate model for these data A) y= (__) + (__)x1 + (__)x3 + (__)4  B) y= (__) + (__)x1 + (__)x2 + (__)x3 + (__)x4  C) y= (__) + (__)x1 + (__)x3  D) y= (__) + (__)x1 + (__)x2 + (__)x3  b) perform a residual analysis to determine if the conditions for the model are met  -Is there an obvious pattern in this plot?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Consider the accompanying date set of dependent and independent variables. 

y x1 x2 x3 x4
64 74 22 24 17
43 63 29 15 30
51 78 20 9 25
49 52 17 38 29
39 43 12 19 39
42 47 17 17 30
23 35 8 5 33
37 17 15 40 39
30 15 10 27 44
27 20 10 30 41
20 17 7 33 49

a) perform a best subsets regression and choose the most appropriate model for these data

A) y= (__) + (__)x1 + (__)x3 + (__)4 

B) y= (__) + (__)x1 + (__)x2 + (__)x3 + (__)x4 

C) y= (__) + (__)x1 + (__)x3 

D) y= (__) + (__)x1 + (__)x2 + (__)x3 

b) perform a residual analysis to determine if the conditions for the model are met 

-Is there an obvious pattern in this plot? 

(I am stuck on this question, someone please help! Thank you! ) 

**Title: Analyzing Best Subsets Regression and Residual Analysis** **Introduction:** Consider the accompanying dataset of dependent and independent variables. The goal is to apply statistical techniques to determine the most suitable model for the data and to evaluate the model fit. **Steps:** a. **Perform a best subsets regression** and choose the most appropriate model for these data. b. **Perform a residual analysis** to determine if the conditions for the model are met. To view the data, click the icon provided. **Model Selection:** a. Select the correct choice below, and fill in the answer boxes to complete your choice (Round the constant to one decimal place. Round all other values to three decimal places as needed). - **Option A:** \[ \hat{y} = \text{[ ]} + \text{[ ]} x_1 + \text{[ ]} x_3 + \text{[ ]} x_4 \] - **Option B:** \[ \hat{y} = \text{[ ]} + \text{[ ]} x_1 + \text{[ ]} x_2 + \text{[ ]} x_3 + \text{[ ]} x_4 \] - **Option C:** \[ \hat{y} = \text{[ ]} + \text{[ ]} x_1 + \text{[ ]} x_3 \] **Instructions:** Click to select and enter your answer(s) and then click "Check Answer."
Transcribed Image Text:**Title: Analyzing Best Subsets Regression and Residual Analysis** **Introduction:** Consider the accompanying dataset of dependent and independent variables. The goal is to apply statistical techniques to determine the most suitable model for the data and to evaluate the model fit. **Steps:** a. **Perform a best subsets regression** and choose the most appropriate model for these data. b. **Perform a residual analysis** to determine if the conditions for the model are met. To view the data, click the icon provided. **Model Selection:** a. Select the correct choice below, and fill in the answer boxes to complete your choice (Round the constant to one decimal place. Round all other values to three decimal places as needed). - **Option A:** \[ \hat{y} = \text{[ ]} + \text{[ ]} x_1 + \text{[ ]} x_3 + \text{[ ]} x_4 \] - **Option B:** \[ \hat{y} = \text{[ ]} + \text{[ ]} x_1 + \text{[ ]} x_2 + \text{[ ]} x_3 + \text{[ ]} x_4 \] - **Option C:** \[ \hat{y} = \text{[ ]} + \text{[ ]} x_1 + \text{[ ]} x_3 \] **Instructions:** Click to select and enter your answer(s) and then click "Check Answer."
## Data Table This table presents a dataset consisting of one dependent variable \( y \) and four independent variables \( x_1, x_2, x_3, \) and \( x_4 \). Each row represents a distinct observation or data point across the five variables. ### Data Points: - **Observation 1:** \( y = 64, x_1 = 74, x_2 = 22, x_3 = 24, x_4 = 17 \) - **Observation 2:** \( y = 43, x_1 = 63, x_2 = 29, x_3 = 15, x_4 = 30 \) - **Observation 3:** \( y = 51, x_1 = 78, x_2 = 20, x_3 = 9, x_4 = 25 \) - **Observation 4:** \( y = 49, x_1 = 52, x_2 = 17, x_3 = 38, x_4 = 29 \) - **Observation 5:** \( y = 39, x_1 = 43, x_2 = 12, x_3 = 19, x_4 = 39 \) - **Observation 6:** \( y = 42, x_1 = 47, x_2 = 17, x_3 = 17, x_4 = 30 \) - **Observation 7:** \( y = 23, x_1 = 35, x_2 = 8, x_3 = 5, x_4 = 33 \) - **Observation 8:** \( y = 37, x_1 = 17, x_2 = 15, x_3 = 40, x_4 = 39 \) - **Observation 9:** \( y = 30, x_1 = 15, x_2 = 10, x_3 = 27, x_4 = 44 \) - **Observation 10:** \( y = 27, x_1 = 20, x_2 = 10, x_3 = 30, x_4 = 41 \) - **Observation 11:** \( y = 20, x_1 = 17, x_2 = 7,
Transcribed Image Text:## Data Table This table presents a dataset consisting of one dependent variable \( y \) and four independent variables \( x_1, x_2, x_3, \) and \( x_4 \). Each row represents a distinct observation or data point across the five variables. ### Data Points: - **Observation 1:** \( y = 64, x_1 = 74, x_2 = 22, x_3 = 24, x_4 = 17 \) - **Observation 2:** \( y = 43, x_1 = 63, x_2 = 29, x_3 = 15, x_4 = 30 \) - **Observation 3:** \( y = 51, x_1 = 78, x_2 = 20, x_3 = 9, x_4 = 25 \) - **Observation 4:** \( y = 49, x_1 = 52, x_2 = 17, x_3 = 38, x_4 = 29 \) - **Observation 5:** \( y = 39, x_1 = 43, x_2 = 12, x_3 = 19, x_4 = 39 \) - **Observation 6:** \( y = 42, x_1 = 47, x_2 = 17, x_3 = 17, x_4 = 30 \) - **Observation 7:** \( y = 23, x_1 = 35, x_2 = 8, x_3 = 5, x_4 = 33 \) - **Observation 8:** \( y = 37, x_1 = 17, x_2 = 15, x_3 = 40, x_4 = 39 \) - **Observation 9:** \( y = 30, x_1 = 15, x_2 = 10, x_3 = 27, x_4 = 44 \) - **Observation 10:** \( y = 27, x_1 = 20, x_2 = 10, x_3 = 30, x_4 = 41 \) - **Observation 11:** \( y = 20, x_1 = 17, x_2 = 7,
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