FOR 3456 – Forest Watershed Management
Lab Session #2 (February 5th and 7th, 2014) – Soil Moisture Statistics
Regression Analysis, Statistical Regression, Regression – “Methods of establishing an equation to explain
or predict the variability of a dependent variable using information about one or more independent
variables. The equation is often represented by a regression line, which is the straight line that comes
closest to approximating a distribution of points in a scatter plot. When "regression" is used without any
qualification it refers to "linear" regression.”
Statistical Analysis (Multiple Linear Regression) on the soil moisture and permeability
results compiled data, in relation to soil texture, iron and organic matter, bulk density,
porosity, and liquid limits to determine regression equations.
Procedure:
1. Locate and open up the statistical analysis program Statview (as provided).
2. Import the compiled dataset (Permdata_2014.txt) using Statview. Keep all of
your working data stored on your personal network drive or somewhere “safe” –
under a “For_3456_2014” folder. Using the statistical analysis package Statview,
analyze the soil moisture content data you collected from Lab 1, using multiple
liner regression analyses.
3. Using multiple liner regression analysis, determine which combination of soil
properties (texture – sand, silt, or clay, OM, Db, and Db_Tube, Porosity) have the
greatest overall effect on soil moisture retention. Your choice of independent
predictor variables should be logical for each moisture content value (i.e. SAT is
best predicted by the amount of pore space in the soil…because the soil is
considered saturated when all of the pores have been completely filled with
water).
4. Use each of the seven different soil moisture variables (SAT, FC, PWP, HP, PL, LL,
& LOG_K) as your dependant variable (in each regression analysis), texture (sand,
silt clay), bulk density (Db_Tube), porosity, and organic matter (OM) as your
independent variables. Identify the “best” statistical relationship for each
dependant variable, and report
the seven different
regression equations (with associated graphs and background
information).
After you have tried using the “physical” characteristics as independent
variables, try using the other soil moisture dependant variables as independent
predictors.
5. Once you have derived the “best” regression relationships for each of the seven
dependant variables, save a new column of data in your data spreadsheet for
each (predicted values) based on the following step.
6. For each of your final chosen regression analyses, save the “fitted values”…so
that you can plot the actual data vs. the predicted values. When you are working
in the analysis window, the fitted / predicted values will appear as a new column
of data on your working spreadsheet (far right column).
7. Determine which combination of these soil physical properties had the greatest
influence on soil moisture retention using all the samples analyzed.
8. Use scatter plots of your regression data to view trends.
Refer to the following page for an example of the scatterplot and regression
equation design.
Deadline for the first report will be one week from today
(Wednesday, February 12th, 2014 – 5:30pm).
For 3456 – Lab Session #2
Statistical Regression Work – Multiple Linear Regression Analysis
Dataset being used – Permdata_2014_Working.xlsx / Permdata_2014_Working.txt
Purpose:
To analyze the compiled dataset using multiple linear regression, and derive the “best”
(relating to the minimum # of independent variables used, highest r² value gained…etc.)
regression equations for all seven (7) dependant variables (SAT, FC, PWP, HP, PL, LL, Log
K).
Produce regression scatterplots of the analyzed data for each by saving the
“unstandardized predicted values” (i.e. predicted dependant values generated from
regression analysis), and then graphing the “Actual” (Y axis) versus “Predicted” (X Axis)
data.
EXAMPLE
Regression Coefficients
SAT vs. 1 Independents
Coefficient Std. Error
Std. Coeff.
t-Value
P-Value
Intercept
7.131
1.373
7.131
5.194
<.0001
FC
1.168
.040
.888
28.861
<.0001
For 3456 – Soil Moisture Statistics – Statview Software