Identifying type of urban land cover. Refer to the Geographical Analysis (Oct. 2006) study...

Identifying type of urban land cover. Refer to the Geographical Analysis (Oct. 2006) study of a new method for analyzing remote-sensing data from satellite pixels, presented in Exercise. Recall that the method uses a numerical measure of the distribution of gaps, or the sizes of holes, in the pixel, called lacunarity. Summary statistics for the lacunarity measurements in a sample of 100 grassland pixels are   and s = 20. As stated in Exercise 6.32, it is known that the mean lacunarity measurement for all grassland pixels is 220. The method will be effective in identifying land cover if the standard deviation of the measurements is 10% (or less) of the true mean (i.e., if the standard deviation is less than 22).

a. Give the null and alternative hypothesis for a test to determine whether, in fact, the standard deviation of all grassland pixels is less than 22.

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b. A MINITAB analysis of the data is provided below. Locate and interpret the p -value of the test. Use α =.10.

Identifying type of urban land cover. Geographers use remote-sensing data from satellite pictures to identify urban land cover as either grassland, commercial, or residential. In Geographical Analysis (Oct. 2006), researchers from Arizona State, Florida State, and Louisiana State Universities collaborated on a new method for analyzing remote-sensing data. A satellite photograph of an urban area was divided into 4 × 4-meter areas (called pixels). Of interest is a numerical measure of the distribution of the sizes of gaps, or holes, in the pixel, a property called lacunarity. The mean and standard deviation of the lacunarity measurements for a sample of 100 pixels randomly selected from a specific urban area are 225 and 20, respectively. It is known that the mean lacunarity measurement for all grassland pixels is 220. Do the data suggest that the area sampled is grassland? Test at α = .01.