document.write( "Question 1204606: x 1 2 3 4 5 6
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document.write( "y 914 1186 1380 1653 1940 2347\r
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document.write( "Use regression to find an exponential equation that best fits the data above. The equation has form
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document.write( " where:\r
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document.write( "a = \r
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Algebra.Com's Answer #840963 by Theo(13342) You can put this solution on YOUR website! i used the curve fit calculator at https://stats.blue/Stats_Suite/exponential_regression_calculator.html\r \n" ); document.write( " \n" ); document.write( "\n" ); document.write( "here's what the results looked like.\r \n" ); document.write( " \n" ); document.write( "\n" ); document.write( " \r\n" ); document.write( " \n" ); document.write( "\n" ); document.write( " \r\n" ); document.write( " \n" ); document.write( "\n" ); document.write( "regression equation is y = 791.6107 * 1.1997^x\r \n" ); document.write( " \n" ); document.write( "\n" ); document.write( "r = .997 \n" ); document.write( "r^2 = .9941\r \n" ); document.write( " \n" ); document.write( "\n" ); document.write( "here's a pretty decent reference on the meaning of r and r^2. \n" ); document.write( "it applies to a linear model, but means the same in any type of curve fitting model, as far as i know.\r \n" ); document.write( " \n" ); document.write( "\n" ); document.write( "https://www.statology.org/r-vs-r-squared/\r \n" ); document.write( " \n" ); document.write( "\n" ); document.write( "since r^2 is the square of r, it stands to reason that a strong correlation leads to a high percentage of variation explained by the data. \n" ); document.write( " \n" ); document.write( " |