Forest fire prediction and detection. A data set of 122 instances of forest fires from two regions of Algeria was collected from the Microelectronic & Nanotechnology Division, Center for Development of Advanced Technologies (CDTA), for forest fire prediction and detection. A wireless sensor network (WSN) based on the fire weather index (FWI) system components was used to detect forest fires. The FWI components consisting of fine fuel moisture code (FFMC) index (x1), duff moisture code (DMC) index (x2), drought code (DC) index (x3), initial spread index (ISI) (x4), and buildup index (BUI) (x5) were used in a first-order model for the FWI (y) and saved in a file. The results are shown in the accompanying output printout. Regression Statistics Multiple R 0.997 R Square 0.993 Adjusted R square 0.993 Standard Error 0.523 Observations 122 ANOVA df SS MS F Significance F Regression 5 4836.576 967.315 3531.466 0.000 Residual 116 31.774 0.274 Total 121 4868.350 Standard Error t-Stat P-value Lower 95% Upper 95% Intercept 2.753 0.309 8.921 0.000 2.142 3.364 FFMC -0.081 0.005 -16.053 0.000 -0.091 -0.071 DMC -0.066 0.111 -0.591 0.556 -0.286 0.155 DC -0.026 0.012 -2.125 0.036 -0.050 -0.002 ISI 1.600 0.031 51.124 0.000 1.538 1.662 BUI 0.340 0.127 2.676 0.009 0.088 0.592

a. Write the least squares prediction equation for y = fire weather index (FWI).

b. Give practical interpretations of the beta estimates.

c. Conduct a test of H0: b2 = 0 against Ha: b2 6 0 at a = .05. Interpret the results.

d. Locate a 95% confidence interval for b5 on the printout. Interpret the interval.

e. How well is the model able to predict the value of the FWI? Applying the Concepts—Intermediate