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Forest Measurements Exam 3 UPDATED Exam Questions and CORRECT Answers

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Forest Measurements Exam 3 UPDATED Exam Questions and CORRECT Answers confidence interval - CORRECT ANSWER - built around the sample mean standard error (Smeanofx) - CORRECT ANSWER distributions 1. How many samples are needed? - dispersion (spread) of mean 2. Quantifying accuracy of results to the true value with inferential statistics - CORRECT ANSWER - keys to samplin

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Forest Measurements Exam 3 UPDATED
Exam Questions and CORRECT Answers
confidence interval - CORRECT ANSWER - built around the sample mean



standard error (Smeanofx) - CORRECT ANSWER - dispersion (spread) of mean
distributions


1. How many samples are needed?
2. Quantifying accuracy of results to the true value with inferential statistics - CORRECT
ANSWER - keys to sampling


1. descriptive

2. inferential - CORRECT ANSWER - 2 main stat categories



descriptive stats - CORRECT ANSWER - characterize data (shape, center, spread)
whole population is measured


inferential stats - CORRECT ANSWER - relate a sample to a larger population



mean - CORRECT ANSWER - average



spread (Sx^2) - CORRECT ANSWER - dispersion of data
not used very much in NR


standard deviation (Sx) - CORRECT ANSWER - -square root of spread
-average difference across all observations from the mean
-often used in NR

, coefficient of variation (CV) - CORRECT ANSWER - - relative measure of dispersion
- spread as a % of the mean
- allows managers to compare different populations


sampling - CORRECT ANSWER - choosing a small population to represent a larger
population


95%

(varies 0-100) - CORRECT ANSWER - level of confidence is typically __%



(sample population) - 1 - CORRECT ANSWER - degrees of freedom =
(t table)


1 - (alpha) - CORRECT ANSWER - desired confidence level =
(t table)


nonlinear

positive - CORRECT ANSWER - this regression line is _ and _


strong

positive - CORRECT ANSWER - this regression is _ and _



regression - CORRECT ANSWER - describes relationship between x and y



yi = b0 + b1xi + ei - CORRECT ANSWER - simple linear regression equation



intercept - CORRECT ANSWER - b0 =

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