y. World Population (billions) x, Number of Years after 1949 1 (1950) 2.6 11 (1960) 3.0 21 (1970) 3.7 31 (1980) 4.5 41 (1990) 5.3 51 (2000) 6.1 61 (2010) 6.9 LinReg y=ax+b a=. 0739285714 b=2.293928571 r2=, 9931624328 r=. 9965753523 ExPRes y=axb^x a=2.576975283 b=1.016951926 r2=, 9934466215 r=. 9967179248
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.
The data for world population are shown in Table . Using a graphing utility’s linear regression feature and exponential regression feature, we enter the data and obtain the models shown in Figure .Because r, the
a. Use Figure to express each model in function notation, with numbers
rounded to three decimal places.
b. How well do the
c. By one projection, world population is expected to reach 8 billion in the
year 2026. Which function serves as a better model for this prediction?
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