Hp 35S

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HP 35s Linear Regression

Linear Regression Practice solving linear regression problems

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HP 35s Linear Regression Linear Regression Linear regressioncalculates the equation for a line that "best fits" a set of ordered pairs by minimizing the sum of the squared residuals between the actual data points and the predicted data points using the estimatedline’s slope and intercept. The equation of the line produced by linear regression is in the form Y = mX + b, where m is the slope of the line and b is the Y-intercept. Once the slope and intercept have beencalculated, it is fairly easy to substitute other values for X and predict a corresponding value for Y, or to substitute a value for Y and predict a value for X. When the X value is a measure of time(months or years, for example), the equation is specifically referred to as a trend line. On the HP 35s, values are entered into the statistical / summation registers by keying in the number (or pairof numbers) desired and pressing /. This process is repeated for all numbers or pair of numbers. When entering a pair of numbers in RPN or algebraic mode, key the Y value, press Ï, then key the Xvalue and press /. To view the linear regression results, press ¹%. The HP 35s displays a menu of relevant values. Items on this menu are viewed by pressing the Ö or Õ cursor keys of the HP 35s. Thismenu allows you to predict a value for X given a Y value, or predict a value for Y given an X value. It also displays the linear regression line's correlation, slope, and y-intercept. The correlationwill always be between –1 and +1, where values closer to –1 and +1 indicating a good “fit” of the line to the data. Values nearer to zero indicate little to no “fit.” Little reliance should be placedupon predictions made where the correlation is not near –1 or +1. Exactly how far away from these values the correlation can be and the equation still be considered a good predictor is a matter of...