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A. Using the data provided: i. Find the SE for the model A: y = 0.015 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number ii. Find the SSE for the model B: y = 025 + 0.01 c using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number i. State which model (A or B) is better and why? Click for List

A. Using the data provided: i. Find the SE for the model A: y = 0.015 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number ii. Find the SSE for the model B: y = 025 + 0.01 c using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number i. State which model (A or B) is better and why? Click for List

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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A. Using the data provided: i. Find the SSE for the model A: y = 0.015 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number %3D ii. Find the SSE for the model B: y = 025 + 0.01 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number iii. State which model (A or B) is better and why? Click for List B. Now, using Linear Regression, i. Which of the given is the line y = mx + b that has the smallest SSE. y = 0.25 x + 0.884862 y= 0.0201743 x + 0.884862 O y= 0.0201743 x – 0.884862 O y=0.28 x + 0.884862 ii. State the r-value (correlation coefficient) for the data. Explain what it means. Click for List C. Use your model from part B to predict the world population in 2040. Round to three decimal places. Number
Transcribed Image Text:A. Using the data provided: i. Find the SSE for the model A: y = 0.015 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number %3D ii. Find the SSE for the model B: y = 025 + 0.01 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number iii. State which model (A or B) is better and why? Click for List B. Now, using Linear Regression, i. Which of the given is the line y = mx + b that has the smallest SSE. y = 0.25 x + 0.884862 y= 0.0201743 x + 0.884862 O y= 0.0201743 x – 0.884862 O y=0.28 x + 0.884862 ii. State the r-value (correlation coefficient) for the data. Explain what it means. Click for List C. Use your model from part B to predict the world population in 2040. Round to three decimal places. Number
Consider the data for the world population (in billions) over the last 300 years. Global Population Year (in billions) 1700 0.6 1760 0.8 1820 1.0 1870 1.3 1910 1.8 1950 2.5 1970 3.7 1990 5.3 2010 6.9 2020 7.6 A. Using the data provided: i. Find the SSE for the model A: y = 0.015 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number ii. Find the SSE for the model B: y = 0.25 + 0.01 x using the data where x is the number of years since 1700. Round to three decimal places. %3D SSE = Number i. State which model (A or B) is better and why? Click for List B. Now, using Linear Regression, i. Which of the given is the line y= mx + 6 that has the smallest SSE. O y= 0.25 x +0.884862 O y= 0.0201743 x +0.884862 O y= 0.0201743 x – 0.884862 O y=0.28 x + 0.884862
Transcribed Image Text:Consider the data for the world population (in billions) over the last 300 years. Global Population Year (in billions) 1700 0.6 1760 0.8 1820 1.0 1870 1.3 1910 1.8 1950 2.5 1970 3.7 1990 5.3 2010 6.9 2020 7.6 A. Using the data provided: i. Find the SSE for the model A: y = 0.015 x using the data where x is the number of years since 1700. Round to three decimal places. SSE = Number ii. Find the SSE for the model B: y = 0.25 + 0.01 x using the data where x is the number of years since 1700. Round to three decimal places. %3D SSE = Number i. State which model (A or B) is better and why? Click for List B. Now, using Linear Regression, i. Which of the given is the line y= mx + 6 that has the smallest SSE. O y= 0.25 x +0.884862 O y= 0.0201743 x +0.884862 O y= 0.0201743 x – 0.884862 O y=0.28 x + 0.884862
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