The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is(A) Draw a scatter plot of the data and a graph of the model on the same axes.y = - 0.33x + 95.0O A.О В.where x represents years after 1960 and y represents the corresponding percentage of oil imports.1004100-Fossil Fuel ProductionProduction (%)96Year19600-0-1970916060198088Years after 1960Years after 1960199084OC.OD.200083100,100-0-0-60Years after 196060Years after 190(B) Interpret the slope of the model.The rate of change of the percentage of oil imports with respect to time isper year.(C) Use the model to predict fossil fuel production in 2010.In 2010 fossil fuel production as a percentage of total production will be about %.(Round to one decimal place as needed.)(D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production.In the year fossil fuel production is expected to fall below 70% of total energy production.(Round up to the nearest year.)

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The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is (A) Draw a scatter plot of the data and a graph of the model on the same axes. y= -0.33x + 95.0 OA. OB. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100 100 Fossil Fuel Production Production (%) 96 91 88 84 83 Year 1960 1970 1980 1990 2000 Years after 1900 Years after 1900 OC. OD. OD. 100 100 Years after 1000 Years after 1000 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about %. (Round to one decimal place as needed.) (D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production. In the year fossil fuel production is expected to fall below 70% of total energy production. (Round up to the nearest year.)

The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is (A) Draw a scatter plot of the data and a graph of the model on the same axes. y= -0.33x + 95.0 OA. OB. where x represents years after 1960 and y represents the corresponding percentage of oil imports. 100 100 Fossil Fuel Production Production (%) 96 91 88 84 83 Year 1960 1970 1980 1990 2000 Years after 1900 Years after 1900 OC. OD. OD. 100 100 Years after 1000 Years after 1000 (B) Interpret the slope of the model. The rate of change of the percentage of oil imports with respect to time is per year. (C) Use the model to predict fossil fuel production in 2010. In 2010 fossil fuel production as a percentage of total production will be about %. (Round to one decimal place as needed.) (D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production. In the year fossil fuel production is expected to fall below 70% of total energy production. (Round up to the nearest year.)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

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Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

The table lists fossil fuel production as a percentage of total energy production for selected years. A linear regression model for this data is
(A) Draw a scatter plot of the data and a graph of the model on the same axes.
y = - 0.33x + 95.0
O A.
О В.
where x represents years after 1960 and y represents the corresponding percentage of oil imports.
1004
100-
Fossil Fuel Production
Production (%)
96
Year
1960
0-
0-
1970
91
60
60
1980
88
Years after 1960
Years after 1960
1990
84
OC.
OD.
2000
83
100,
100-
0-
0-
60
Years after 1960
60
Years after 190
(B) Interpret the slope of the model.
The rate of change of the percentage of oil imports with respect to time is
per year.
(C) Use the model to predict fossil fuel production in 2010.
In 2010 fossil fuel production as a percentage of total production will be about %.
(Round to one decimal place as needed.)
(D) Use the model to estimate the year in which fossil fuel production will fall below 70% of total energy production.
In the year fossil fuel production is expected to fall below 70% of total energy production.
(Round up to the nearest year.)

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