all measurements associated with trees not analysed in accordance with this section must be discarded; destructive sampling for the equivalent number of sample trees discarded in accordance with paragraph (a) must be repeated in accordance with subsection 3.27(1); and analysis of the sample trees must be completed in accordance with this section. If the coefficient of variation calculated in accordance with subsection (3) is equal to or less than 15%, the dry weight of any sample trees not analysed in accordance with this section must be estimated by multiplying the tree wet weight by the average tree dry to wet weight ratio. Step 10—Data exploration and analysis The whole tree dry weight data obtained in section 3.28 must be compiled into a database or spreadsheet suitable for statistical analysis or importation into a statistical analysis software package. If an existing allometric equation is to be validated, Step 10.1 in this section must be skipped and Step 11 in section 3.30 must be completed. If a new allometric equation is to be validated, Step 10.1 in this section must be completed. Step 10.1—Allometric development An allometric equation being developed must take the form of a statistical model fitted using: simple linear regression; multiple regression; polynomial regression; or non-linear regression. Each allometric equation developed must satisfy the assumptions that: (a) the means of the response variables change in a systematic way with variation in the explanatory variable; and errors are: independent; and normally distributed. To satisfy the assumptions in subsection (5), data may be transformed. If data is transformed as permitted by subsection (6), power transformations may be used. If a logarithmic transformation is applied to the response variable, the proportional bias must be estimated and applied the ratio of the arithmetic sample mean to the mean of the back-transformed predicted variables. Each explanatory variable used in an allometric equation must be statistically significant. For the purposes of subsection (9), a variable is statistically significant if the outcome of an or a two-tailed t-test has a probability value of less than 5%.
all measurements associated with trees not analysed in accordance with this section must be discarded; destructive sampling for the equivalent number of sample trees discarded in accordance with paragraph (a) must be repeated in accordance with subsection 3.27(1); and analysis of the sample trees must be completed in accordance with this section. If the coefficient of variation calculated in accordance with subsection (3) is equal to or less than 15%, the dry weight of any sample trees not analysed in accordance with this section must be estimated by multiplying the tree wet weight by the average tree dry to wet weight ratio. Step 10—Data exploration and analysis The whole tree dry weight data obtained in section 3.28 must be compiled into a database or spreadsheet suitable for statistical analysis or importation into a statistical analysis software package. If an existing allometric equation is to be validated, Step 10.1 in this section must be skipped and Step 11 in section 3.30 must be completed. If a new allometric equation is to be validated, Step 10.1 in this section must be completed. Step 10.1—Allometric development An allometric equation being developed must take the form of a statistical model fitted using: simple linear regression; multiple regression; polynomial regression; or non-linear regression. Each allometric equation developed must satisfy the assumptions that: (a) the means of the response variables change in a systematic way with variation in the explanatory variable; and errors are: independent; and normally distributed. To satisfy the assumptions in subsection (5), data may be transformed. If data is transformed as permitted by subsection (6), power transformations may be used. If a logarithmic transformation is applied to the response variable, the proportional bias must be estimated and applied the ratio of the arithmetic sample mean to the mean of the back-transformed predicted variables. Each explanatory variable used in an allometric equation must be statistically significant. For the purposes of subsection (9), a variable is statistically significant if the outcome of an or a two-tailed t-test has a probability value of less than 5%.
avoided-land-clearing-draft-determination.docx