The area sampled in squared centimeters goes on the x-axis because it is the independent variable. Whereas species richness goes on the y-axis because it is the dependent variable. Based on this sample the prairie habitat seems to be more species rich. This approach does not provide a definitive answer to the question because this is a single sample. In order to provide a more accurate answer to the question of which habitat is richer, more samples must be included and analyzed in order to minimize sampling error.
Sample size G, of 40,000 centimeters squared, provides the most information about species richness. Due to the fact that it is the largest representation of a habitat. A t-test is a statistical analysis that compares the means of two populations, wherein the variances of the data determine whether or not the means differ significantly. From a t-test a null hypothesis can be determined. The null hypothesis for this study is the mean number of species found within the lawn and the prairie are the same, while the alternative hypothesis states that the mean number of species found within the lawn and the prairie are not equal. As determined by the p-value of 0.007434, for a two-tail t-test, one should reject the null hypothesis and accept …show more content…
The power trendline creates smaller distances between the data points, therefore creating smaller residuals. Consequently the value for β is 0.4421, which is relatively greater than the β value for the linear model. The power curve works well for this application because the rate of change in the species richness increases and then levels out. The power model works well for ecological data because the expected species richness in a fixed spaced area will eventually level out as space for organisms decreases. Linear models assume that the data is fixed and will always increase by a predicatable increment which is unrealistic for ecological
Sample size G, of 40,000 centimeters squared, provides the most information about species richness. Due to the fact that it is the largest representation of a habitat. A t-test is a statistical analysis that compares the means of two populations, wherein the variances of the data determine whether or not the means differ significantly. From a t-test a null hypothesis can be determined. The null hypothesis for this study is the mean number of species found within the lawn and the prairie are the same, while the alternative hypothesis states that the mean number of species found within the lawn and the prairie are not equal. As determined by the p-value of 0.007434, for a two-tail t-test, one should reject the null hypothesis and accept …show more content…
The power trendline creates smaller distances between the data points, therefore creating smaller residuals. Consequently the value for β is 0.4421, which is relatively greater than the β value for the linear model. The power curve works well for this application because the rate of change in the species richness increases and then levels out. The power model works well for ecological data because the expected species richness in a fixed spaced area will eventually level out as space for organisms decreases. Linear models assume that the data is fixed and will always increase by a predicatable increment which is unrealistic for ecological