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If the coefficient of determination for the data set is 0.75 and the SSE for the data set is 13 what is the SST for the data set?
Wiki User
∙ 6y ago
It is 52.
Wiki User
∙ 6y ago
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What is the SUMS OF SQUARES One-way ANOVA?
In a one-way ANOVA, the relate in an equation the total variation, , where i=1,2,…,a and j=1,2,…,n_i; the explained variation and the unexplained variation SST=SSA+SSE Degrees of freedom N-1 a-1 N-a
How does a linear regression allow us to better estimate trends costs and other factors in complex situations?
You question is how linear regression improves estimates of trends. Generally trends are used to estimate future costs, but they may also be used to compare one product to another. I think first you must define what linear regression is, and what the alternative forecast methods exists. Linear regression does not necessary lead to improved estimates, but it has advantages over other estimation procesures. Linear regression is a mathematical procedure that calculates a "best fit" line through the data. It is called a best fit line because the parameters of the line will minimizes the sum of the squared errors (SSE). The error is the difference between the calculated dependent variable value (usually y values) and actual their value. One can spot data trends and simply draw a line through them, and consider this a good fit of the data. If you are interested in forecasting, there are many methods available. One can use more complex forecasting methods, including time series analysis (ARIMA methods, weighted linear regression, or multivariant regression or stochastic modeling for forecasting. The advantages to linear regression are that a) it will provide a single slope or trend, b) the fit of the data should be unbiased, c) the fit minimizes error and d) it will be consistent. If in your example, the errors from regression from fitting the cost data can be considered random deviations from the trend, then the fitted line will be unbiased. Linear regression is consistent because anyone who calculates the trend from the same dataset will have the same value. Linear regression will be precise but that does not mean that they will be accurate. I hope this answers your question. If not, perhaps you can ask an additional question with more specifics.
Is any idea for java code writing linear regression?
import java.io.*;import java.util.*;public class LinearRegression {public static void main(String[] args) {int MAXN = 1000;int n = 0;double[] x = new double[MAXN];double[] y = new double[MAXN];// first pass: read in data, compute xbar and ybardouble sumx = 0.0, sumy = 0.0, sumx2 = 0.0;while(!StdIn.isEmpty()) {x[n] = StdIn.readDouble();y[n] = StdIn.readDouble();sumx += x[n];sumx2 += x[n] * x[n];sumy += y[n];n++;}double xbar = sumx / n;double ybar = sumy / n;// second pass: compute summary statisticsdouble xxbar = 0.0, yybar = 0.0, xybar = 0.0;for (int i = 0; i < n; i++) {xxbar += (x[i] - xbar) * (x[i] - xbar);yybar += (y[i] - ybar) * (y[i] - ybar);xybar += (x[i] - xbar) * (y[i] - ybar);}double beta1 = xybar / xxbar;double beta0 = ybar - beta1 * xbar;// print resultsSystem.out.println("y = " + beta1 + " * x + " + beta0);// analyze resultsint df = n - 2;double rss = 0.0; // residual sum of squaresdouble ssr = 0.0; // regression sum of squaresfor (int i = 0; i < n; i++) {double fit = beta1*x[i] + beta0;rss += (fit - y[i]) * (fit - y[i]);ssr += (fit - ybar) * (fit - ybar);}double R2 = ssr / yybar;double svar = rss / df;double svar1 = svar / xxbar;double svar0 = svar/n + xbar*xbar*svar1;System.out.println("R^2 = " + R2);System.out.println("std error of beta_1 = " + Math.sqrt(svar1));System.out.println("std error of beta_0 = " + Math.sqrt(svar0));svar0 = svar * sumx2 / (n * xxbar);System.out.println("std error of beta_0 = " + Math.sqrt(svar0));System.out.println("SSTO = " + yybar);System.out.println("SSE = " + rss);System.out.println("SSR = " + ssr);}}
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In what year did Seventy Seven Energy Inc - SSE - have its IPO?
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Which direction is Marseille from Paris?
SSE
What is the opposite of NNW on the compass?
SSE
