Consider the following Year Population Cin millions ) 1790 1800 3,१2१॥ 5.308 7,240 1810 1820 1830 (९५6. 1850 1860 1870 1880 (৪१० h 12.866 17, 069 23, 192 31.433 38.558 50. 156 62. १५३ 75. ११८ 91.972 (05, 711 122.775 131.669 ISO 6९7 1900 1910 1920 1930 1940 1950 a) (ensus data for the Unitedl States betueen 1790 and 1950 are given in the table above. Construct a lagistic populatio model using the data from 1790 1860 and ', l920, CAssume that t is years Since Round all (790 and P is popu lation in millions. coesdicents to 'four decimal placese). P(t)=
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.
Population of Country U in 1790 is equal to 3.929 millions and the population of Country U in 1860 is equal to 31.433 million. Use logistic population model to find a general equation to predict the population of Country U.
The general equation of a logistical population model is given by,
, where P(t) is the predicted population at year t, K is the carrying capacity, A is a constant where , where P0 is the population of a Country at the 0th year or the start year
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