Effects of Environment Using Discussion Board Data Sets

Effects of Environment Using Discussion Board Data Sets In this Discussion you will use one of the Discussion Board data sets to identify two variables that are correlated and then create the best prediction equation for those variables.

Effects of Environment Using Discussion Board Data Sets
Effects of Environment Using Discussion Board Data Sets

Effects of Environment Using Discussion Board Data Sets

Finally, you will use the prediction equation you created to make a prediction for one variable (the Y variable) using a value for the second variable (the X variable). Correlated Variables: Use one of the Discussion Board data sets to identify two variables that are correlated. Use SPSS to document the correlation. For example, you may think that height and weight are correlated. From the Female Health data set there is a correlation of 0.364 between height and weight. Best Prediction equation: Use the Linear Regression procedure to generate the best prediction equation (regression equation) using one variable as the dependent or predicted variable (Y).

Effects of Environment Using Discussion Board Data Sets

Differential SAR interferometry, a popular technique for measuring displacements of the Earth’s surface, is potentially influenced by changes in soil moisture. Different mechanisms for this impact have been proposed, but its magnitude, sign, and even presence remain poorly understood. In this study the dependence of the phase, the coherence magnitude as well as the phase triplets on soil moisture was inferred empirically with regression techniques: this was done for two airborne data sets at L-band.

Effects of Environment Using Discussion Board Data Sets

The phase dependence was significant (at a significance level of 0.05) for more than 70% of the fields at HH polarization, its sign corresponding to an increase in optical path upon wetting, and the magnitude of the associated deformation commonly exceeding 2 cm for a change in soil moisture of 20%. This trend was similar in both campaigns, whereas the prevalence of soil moisture-related decorrelation differs. These results are only consistent with a dielectric origin of the soil moisture effects, and not with soil swelling or the penetration depth hypothesis.

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