Java Program help

 

Show transcribed image text Linear Regression : Linear regression draws a straight line through a group of data points such that the position and slope of the line minimizes the square of the vertical distance between the data points and the straight line. It fits the data in an intuitively satisfying and yet mathematically reproducible way. For linear regression to be valid, all data points should vary in exactly the same random way, and that variation should have a normal or ''Gaussian'' distribution - the familiar bell-shaped distribution. To illustrate the application of linear regression, this project Uses it to generate a trend line for the effect of nitrogen fertilizer on the yield of a crop of corn (maise). To guarantee that the required assumptions are met, we have created the data artificially, by adding a normally distributed random variable to a sloping straight line, with the same variance for all data points. Specifically, we added normal random number having a standard deviation of 25 to the straight line. Here's the equation: y = 50 + 100 * x + randomNumber The following plot shows one set of 10 data points, and the linear-regression fit to those data points The first sample session prints all of the data points used in the figure. The second sample session prints just the first 10 of 10,000 points used as the basis of the regression. Of course, your random number generator will not generate the same data values as those shown above, but the four values at the bottom of your output should be close to the four values we generated - which are close to the parameters used to generate the random data. Your job is to write the program that produces these results. To generate the first sample session above, initialize a two-dimensional array with the 10 sets of output values shown. To generate the second sample session above, import the java.util. Random package, use the zero-parameter constructor to instantiate a random-number generator, and have that generator call its nextGaussian method to generate a random variable with a Gaussian distribution whose mean value is zero and whose standard deviation is 1.0. (See Section 5.8 for more information.)