Statistical Analysis - Revision history

GapAdmin: /* Gridding */

2018-08-07T15:17:58Z

‎Gridding

2018-08-07T15:13:10Z

‎Gridding

2018-08-07T15:11:38Z

‎Gridding

2018-08-07T15:10:06Z

‎Gridding

2018-08-07T15:07:11Z

‎Gridding

2018-08-07T15:02:33Z

‎Gridding

2018-08-07T14:30:14Z

‎Gridding

2018-08-07T14:28:22Z

‎Gridding

2018-08-07T14:25:06Z

‎Descriptive Statistics

2018-08-07T14:23:43Z

‎Gridding

@@ Line 168: / Line 168: @@
 [[File:gridding2_70.png|frame|left|<b>Figure 8:</b> Gridding]]
-The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [https://www.gdal.org/grid_tutorial.html $wgExternalLinkTarget = '_blank' <b>GDAL Grid Tutorial</b>].
+The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [https://www.gdal.org/grid_tutorial.html <b>GDAL Grid Tutorial</b>].
 <b>Inverse Distance</b> The Inverse Distance method performs weighted average interpolation to a power (default 2). Optional parameters include:

@@ Line 168: / Line 168: @@
 [[File:gridding2_70.png|frame|left|<b>Figure 8:</b> Gridding]]
-The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [https://www.gdal.org/grid_tutorial.html <b>GDAL Grid Tutorial</b>].
+The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [https://www.gdal.org/grid_tutorial.html $wgExternalLinkTarget = '_blank' <b>GDAL Grid Tutorial</b>].
 <b>Inverse Distance</b> The Inverse Distance method performs weighted average interpolation to a power (default 2). Optional parameters include:

@@ Line 168: / Line 168: @@
 [[File:gridding2_70.png|frame|left|<b>Figure 8:</b> Gridding]]
-The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [[https://www.gdal.org/grid_tutorial.html|<b>GDAL Grid Tutorial</b>]].
+The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [https://www.gdal.org/grid_tutorial.html <b>GDAL Grid Tutorial</b>].
 <b>Inverse Distance</b> The Inverse Distance method performs weighted average interpolation to a power (default 2). Optional parameters include:

@@ Line 183: / Line 183: @@
 * <b>Angle</b> - Angle of search ellipse rotation in degrees (counter clockwise, default 0.0).
-Both types of grids require the specification of a data table, target variable and layer name.
+Both types of grids require the specification of a <b>data table</b>, <b>target variable</b> and <b>layer name</b>.
 <br clear=all>

@@ Line 166: / Line 166: @@
 ==Gridding==
-[[File:gridding_70.png|frame|left|<b>Figure 8:</b> Gridding]]
+[[File:gridding2_70.png|frame|left|<b>Figure 8:</b> Gridding]]
 The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis#Models|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases. The two main options to choose between are between the inverse distance and nearest neighbor algorithms. For more information, see the [[https://www.gdal.org/grid_tutorial.html|<b>GDAL Grid Tutorial</b>]].

@@ Line 2: / Line 2: @@
 ==Descriptive Statistics==
-To view basic model statistics, select the model and go to <b>'Descriptive Statistics'</b>. You can view basic statistics for each variable either as a graph (<b>Select an attribute</b>) or as a table (Figure 1).
+To view basic model statistics, select the model and go to <b>Descriptive Statistics</b>. You can view basic statistics for each variable either as a graph (<b>Select an attribute</b>) or as a table (Figure 1).
 The cumulative distribution function (CDF) shows the number of datapoints below a respective threshold value. By clicking on the legend, you can visualize the dataset's <b>mean</b>, <b>median</b> and <b>IQR</b> (interquartile range), which indicates the middle 50% of the dataset.

← Older revision		Revision as of 14:23, 7 August 2018
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	==Gridding==		==Gridding==

−	[[File:gridding_80.png]]	+	[[File:gridding_80.png\|frame\|left\|<b>Figure 8:</b> Gridding]]
		+
		+	The <b>Gridding</b> function allows you to grid your point data using interpolation, which can produce a reasonably accurate map given a sufficient density of data points. Although not as sophisticated as logistic regression, found in the [[Statistical Analysis$Models\|<b>Models</b>]] section, is simpler and easier to use and can be sufficient in some cases.
		+
		+	<br clear=all>