- Questions & Answers
- Accounting
- Computer Science
- Automata or Computationing
- Computer Architecture
- Computer Graphics and Multimedia Applications
- Computer Network Security
- Data Structures
- Database Management System
- Design and Analysis of Algorithms
- Information Technology
- Linux Environment
- Networking
- Operating System
- Software Engineering
- Big Data
- Android
- iOS
- Matlab

- Economics
- Engineering
- Finance
- Thesis
- Management
- Science/Math
- Statistics
- Writing
- Dissertations
- Essays
- Programming
- Healthcare
- Law

- Log in | Sign up

Sheet1

Car MPG Weight Cylinders Horsepower Country

Buick Skylark 28.4 2670 4 90 U.S.

Dodge Omni 30.9 2230 4 75 U.S.

Mercury Zephyr 20.8 3070 6 85 U.S.

Fiat Strada 37.3 2130 4 69 Italy

Peugeot 694 SL 17.8 3410 6 133 France

VW Ra

it 31.9 1925 4 71 Germany

Plymouth Horizon 34.2 2200 4 70 U.S.

Mazda GLC 34.1 1975 4 65 Japan

Buick Estate Wagon 16.9 4360 8 155 U.S.

Audi 5000 22.5 2830 5 103 Germany

Chevy Malibu Wagon 19.2 3605 8 125 U.S.

Dodge Aspen 18.6 3620 6 110 U.S.

VW Dasher 30.5 2190 4 78 Germany

Ford Mustang 4 26.5 2585 4 88 U.S.

Dodge Colt 35.1 1915 4 80 Japan

Datsun 810 22 2815 6 97 Japan

VW Scirocco 31.5 1990 4 71 Germany

Chevy Citation 28.8 2595 6 115 U.S.

Olds Omega 26.8 2700 6 115 U.S.

Chrysler LeBaron Wagon 18.5 3940 8 150 U.S.

Datsun 510 27.2 2300 4 97 Japan

AMC Concord D/L 18.1 3410 6 120 U.S.

Buick Century Special 20.6 3380 6 105 U.S.

Saab 99 GLE 21.6 2795 4 115 Sweden

Datsun 210 31.8 2020 4 65 Japan

Ford LTD 17.6 3725 8 129 U.S.

Volvo 240 GL 19 3140 6 125 Sweden

Dodge St Regis 18.2 3830 8 135 U.S.

Toyota Corona 27.5 2560 4 95 Japan

Chevette 30 2155 4 68 U.S.

Ford Mustang Ghia 21.9 2910 6 109 U.S.

AMC Spirit 27.4 2670 4 80 U.S.

Ford Country Squire Wagon 28 4054 8 142 U.S.

BMW 320i 23.1 2600 4 110 Germany

Pontiac Phoenix 33.5 2556 4 90 U.S.

Honda Accord LX 29.5 2135 4 68 Japan

Mercury Grand Marquis 16.5 3955 8 138 U.S.

Chevy Caprice Classic 17 3840 8 130 U.S.

Engineering Department, UMass Boston XXXXXXXXXXSpring 2022

ENGIN 322: Probability and Random Processes

Project #2: Sampling Theory and Linear Regression

Project Description

In this project, you will assume the part of an automotive manufacturer. In part I, you will provide

ackground analysis for the development of a new vehicle. In part II, you will develop a method for

sampling manufactured parts in order to determine the likelihood of failure. (Note: Data for Part I of the

project is slightly modified from: https:

www.statcrunch.com/5.0/viewresult.php?resid=1878105).

Part I

The spreadsheet ‘Vehicle_Info.xlsx’ provides detailed information about vehicles on the market. In Part I,

you should load the data into Matlab and compare vehicle properties to average miles per gallon (mpg).

a) Load the data set into Matlab with the readtable() function

) Determine the data set co

elation (i.e., Pearson’s r) for average mpg versus weight, number of

cylinders, and horsepower.

c) Use the scatter() and polyfit() functions to display the data points and linear regression curves

for average mpg versus weight, number of cylinders, and horsepower. For each of the 3 plots,

show average mpg on your Y axis. Label the resulting plots and include them in your report.

Question 1: Would you say that a vehicles horsepower impacts the average mpg? What about the impact

of weight and number and cylinders on average mpg? Explain your reasoning.

Question 2: Your company’s newest vehicle model will be a 6 cylinder that weighs 3,250lbs and has 100

horsepower. If your goal is to be above the regression line for every category, what average mpg should

you strive for?

Part II

Assume that you have received reports of faulty headlights. The data values in the file

‘population_data.mat’ represent the population of headlights on the production line (0 represents a good

headlight, 1 represents a faulty headlight). You are unable to test all headlights; but you are able to

andomly sample ?? of the headlights as they come off the production line. In Part II, you should simulate

the following in Matlab for values of ?? ranging from 1 to 2000.

a) For each value of ??, randomly sample n headlights from the population and determine the sample

mean and sample variance (i.e., the percentage of faulty headlights in your sample and the

associated variance of the sample).

) Plot the sample mean and sample variance versus the sample size.

Question 1: What do you notice about the sample mean and sample variance as you increase sample size?

Question 2: Based on your plots, what can you predict about the percentage of headlights in the

population that are faulty? Explain your reasoning. (Hint: Zoom in on the values from n=1000 to n=2000

for a better visualization. You can compare your prediction to the true mean by determining the mean

value of the full set of values from ‘population_data.mat’)

https:

www.statcrunch.com/5.0/viewresult.php?resid=1878105

Grading Metrics

• Coding and Results: 30%

• Theoretical Analysis: 40%

• Written Report: 30%

Coding and Results: This portion of the project will be graded based on the implementation of code as

described for Part I and II above. Results should include a description of the observed outcomes and some

depiction of the results from your code.

Theoretical Analysis: This portion of the project will be graded based on your answers to the questions

above. Be sure to clearly indicate the answers AND REASONING for each of the questions within your

written report.

Written Report: The written report should be submitted on blackboard by midnight on April 8. The report

should be 3-5 pages including an overview of the project, expected outcomes, your analysis method,

esults, and observations. You may include any code as an appendix.

Project Description

Part I

Part II

Grading Metrics

Part II - Old

Answered 2 days AfterApr 05, 2022

Engineering Department, UMass Boston Spring 2022

ENGIN 322: Probability and Random Processes

Project #2: Sampling Theory and Linear Regression

Name:……………………………………………… ID:………………………..

Name:……………………………………………… ID:……………………….

Table of Contents

Title Page

1

Objective

3

Introduction

3

Apparatus

3

Procedure

3

Results and Discussion

4-11

Conclusion

References

11

11

Objective: In this project, you will assume the part of an automotive manufacturer. In part I,

you will provide background analysis for the development of a new vehicle. In part II, you will

develop a method for sampling manufactured parts in order to determine the likelihood of

failure.

Introduction:

Regression analysis is an important statistical method for the analysis of data. By applying

egression analysis, we are able to examine the relationship between a dependent variable and

one or more independent variables. In this article, I am going to introduce the most common

form of regression analysis, which is the linear regression. As the name suggests, this type of

egression is a linear approach to modeling the relationship between the variables of interest.

Method

Linear regression is used to study the linear relationship between a dependent variable (y) and

one or more independent variables (X). The linearity of the relationship between the

dependent and independent variables is an assumption of the model. The relationship is modeled

through a random distu

ance term (or, e

or variable) ε. The distu

ance is primarily

important because we are not able to capture every possible influential factor on the dependent

variable of the model. To capture all the other factors, not included as independent variable, that

affect the dependent variable, the distu

ance term is added to the linear regression model.

In this way, the linear regression model takes the following form:

where

are the regression coefficients of the model (which we want to estimate!), and K is the number

of independent variables included. The equation is called the regression equation.

Simple linear regression

Let’s take a step back for now. Instead of including multiple independent variables, we start

considering the simple linear regression, which includes only one independent variable. Here,

we start modeling the dependent variable yi with one independent variable xi:

where the subscript i refers to a particular observation (there are n data points in total). Here, β0

and β1 are the coefficients (or parameters) that need to be estimated from the data. β0 is the

intercept (a constant term) and β1 is the gradient.

In simple linear regression, we essentially predict the value of the dependent variable yi using

the score of the independent variable xi, for observation i.

Apparatus/Tools:

• Personal Computer or Laptop.

• MATLAB etc

Procedure:

Results and Discussion:

Part 1 –

a) Load the data set into Matlab with the readtable() function

) Determine the data set co

elation (i.e., Pearson’s r) for average mpg versus weight, number

of cylinders, and horsepower.

c) Use the scatter() and polyfit() functions to display the data points and linear regression curves

for average mpg versus weight, number of cylinders, and horsepower. For each of the 3 plots,

show average mpg on your Y axis. Label the resulting plots and include them in your report.

Part 2 –

Assume that you have received reports of faulty headlights. The data values in the file...

ENGIN 322: Probability and Random Processes

Project #2: Sampling Theory and Linear Regression

Name:……………………………………………… ID:………………………..

Name:……………………………………………… ID:……………………….

Table of Contents

Title Page

1

Objective

3

Introduction

3

Apparatus

3

Procedure

3

Results and Discussion

4-11

Conclusion

References

11

11

Objective: In this project, you will assume the part of an automotive manufacturer. In part I,

you will provide background analysis for the development of a new vehicle. In part II, you will

develop a method for sampling manufactured parts in order to determine the likelihood of

failure.

Introduction:

Regression analysis is an important statistical method for the analysis of data. By applying

egression analysis, we are able to examine the relationship between a dependent variable and

one or more independent variables. In this article, I am going to introduce the most common

form of regression analysis, which is the linear regression. As the name suggests, this type of

egression is a linear approach to modeling the relationship between the variables of interest.

Method

Linear regression is used to study the linear relationship between a dependent variable (y) and

one or more independent variables (X). The linearity of the relationship between the

dependent and independent variables is an assumption of the model. The relationship is modeled

through a random distu

ance term (or, e

or variable) ε. The distu

ance is primarily

important because we are not able to capture every possible influential factor on the dependent

variable of the model. To capture all the other factors, not included as independent variable, that

affect the dependent variable, the distu

ance term is added to the linear regression model.

In this way, the linear regression model takes the following form:

where

are the regression coefficients of the model (which we want to estimate!), and K is the number

of independent variables included. The equation is called the regression equation.

Simple linear regression

Let’s take a step back for now. Instead of including multiple independent variables, we start

considering the simple linear regression, which includes only one independent variable. Here,

we start modeling the dependent variable yi with one independent variable xi:

where the subscript i refers to a particular observation (there are n data points in total). Here, β0

and β1 are the coefficients (or parameters) that need to be estimated from the data. β0 is the

intercept (a constant term) and β1 is the gradient.

In simple linear regression, we essentially predict the value of the dependent variable yi using

the score of the independent variable xi, for observation i.

Apparatus/Tools:

• Personal Computer or Laptop.

• MATLAB etc

Procedure:

Results and Discussion:

Part 1 –

a) Load the data set into Matlab with the readtable() function

) Determine the data set co

elation (i.e., Pearson’s r) for average mpg versus weight, number

of cylinders, and horsepower.

c) Use the scatter() and polyfit() functions to display the data points and linear regression curves

for average mpg versus weight, number of cylinders, and horsepower. For each of the 3 plots,

show average mpg on your Y axis. Label the resulting plots and include them in your report.

Part 2 –

Assume that you have received reports of faulty headlights. The data values in the file...

SOLUTION.PDF## Answer To This Question Is Available To Download

- The question and required file are attachedSolvedMay 11, 2022
- Q1: In the proposed methodology, it is used chi-square as the distance estimate. Is it any advantage of this? Q3: Can you please explain what exactly the developed model architecture which is used to...SolvedMay 09, 2022
- Name: 1 1. See the 2nd order linear circuit shown in Figure 1 which consists of one current source (Iin), three resistors (R1 to R3), one capacitor (C), and one inductor (L). Figure 1 – 2nd order...SolvedMay 01, 2022
- A4 Motor PWM Design a system using a matrix keypad as the input to control a PWM output signal to an electric motor (12 VDC). Show the speed % (0 to 100) on a 2x16 LCD display (Must be I2C connected)....SolvedApr 26, 2022
- a would be nice, but I desperately need parts b and c. AND DO NOT COPY THE ANSWER FROM CHEGG. I KNOW THERE IS ONE FOR THIS QUESTION BUT IT IS INCOMPLETE AND WRONGSolvedApr 21, 2022
- Please go through the attached file.SolvedApr 18, 2022
- The question is attached as a jpegSolvedApr 17, 2022
- Question 1 (15 Points) Concepts and Terminology: Answer the questions below in 1-2 sentences for each question. a) Provide two reasons why we would want to sample a subset of values from a population...SolvedApr 13, 2022
- Below are the measured resistors and capacitor 1K → measured: 0.996k 2.2k → measured: 2.177k 100 → measured: 100 43 → measured: 43.7 Capacitor: 10uf → 9.744uf RUBRICSolvedApr 11, 2022
- EGRE 303 SEMICONDUCTOR ELECTRONIC DEVICES – SPRING 2022 A COMPREHENSIVE PN JUNCTION COMPUTER AIDED (CAD) PROJECT Due Date: Midnight April 14th, 2022 This project is designed for students for the...SolvedApr 09, 2022

Copy and Paste Your Assignment Here

About Us | Contact Us | Help | Privacy Policy | Revision and Refund Policy | Terms & Conditions | Honor Code

Copyright © 2022. All rights reserved.