1
forest biometrics exam 2 Questions with
Correct Answers for Specific Exam Mail
regression analysis
Ans: To study the relationship between X and Y we perform
Regression:
Ans: the statistical or mathematical relationship between the independent
variable (X) and a dependent variable (Y) while considering certain
constants or parameters
dependent varible
Ans: The quantity being estimated through regression is called the
independent variable
Ans: is measured in order to predict the dependent variable
scatter diagrams
Ans: One way to examine the x and y relationship is through
Once the X,Y points are plotted, the diagram can be analyzed to see if a ----
-- forms.
Ans: pattern
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,2
The simplest relationship is called a ---- ---- ---- which is also called a straight
line relationship
Ans: simple linear regression
There is an assumption, for each -- value a distribution for the -- values will
occur with each observation of i, at observation -- there will be an
observation at --
Ans: X, Y ..... Yi, Xi
linear model equation
Ans: Y^=A + BX
prediction equation
Ans: an equation suggested by the points of a scatter plot that is used to
predict other points
is a way for us to get the estimates needed, in other words values for a and b
Ans: least squares
So what do we need for least squares?
Ans: 1. Estimates of A and B by using a and b
2. We use the estimates of a and b in the prediction equation Y^= a + bX
3. Get estimates of a and b variances 4. Test hypothesis 5. Set confidence
intervals 6. And on occasion, estimate Y for a given value of X
Least Squares allows us to get the best
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, 3
Ans: unbiased estimates
So for every observed Yi there is a predicted value of --, equal to (a + bXi )
which corresponds to a location on the regression line
Ans: Y^i
(yi-y^i)
Ans: is the deviation of the observed Y from the predicted Y^
sum(Yi-Y^)2
Ans: The sum of squares of all the deviations form the fitted line :
Since we know Y^ = a + bX, we can substitute for Y^ in the formula to get:
Ans: sum(Yi - a - bXi ) 2
When a and b have been found their numerical values (using the previous
two formulas and the data) for the two estimators a and b can be substituted
into the
Ans: prediction equation Y^ = a + bX
1. A and B are unknown parameters or also referred to as constants 2. X's are
known values (measured values with no associated errors) and are selected
by the user 3. For each value of X, the Yi ~ N(µ,σ 2 ) and independent 4. The
variance of Y with a given X is the same for all X (homoscedasticity)
Ans: regression assumptions:
Pretest - Stuvia US
forest biometrics exam 2 Questions with
Correct Answers for Specific Exam Mail
regression analysis
Ans: To study the relationship between X and Y we perform
Regression:
Ans: the statistical or mathematical relationship between the independent
variable (X) and a dependent variable (Y) while considering certain
constants or parameters
dependent varible
Ans: The quantity being estimated through regression is called the
independent variable
Ans: is measured in order to predict the dependent variable
scatter diagrams
Ans: One way to examine the x and y relationship is through
Once the X,Y points are plotted, the diagram can be analyzed to see if a ----
-- forms.
Ans: pattern
Pretest - Stuvia US
,2
The simplest relationship is called a ---- ---- ---- which is also called a straight
line relationship
Ans: simple linear regression
There is an assumption, for each -- value a distribution for the -- values will
occur with each observation of i, at observation -- there will be an
observation at --
Ans: X, Y ..... Yi, Xi
linear model equation
Ans: Y^=A + BX
prediction equation
Ans: an equation suggested by the points of a scatter plot that is used to
predict other points
is a way for us to get the estimates needed, in other words values for a and b
Ans: least squares
So what do we need for least squares?
Ans: 1. Estimates of A and B by using a and b
2. We use the estimates of a and b in the prediction equation Y^= a + bX
3. Get estimates of a and b variances 4. Test hypothesis 5. Set confidence
intervals 6. And on occasion, estimate Y for a given value of X
Least Squares allows us to get the best
Pretest - Stuvia US
, 3
Ans: unbiased estimates
So for every observed Yi there is a predicted value of --, equal to (a + bXi )
which corresponds to a location on the regression line
Ans: Y^i
(yi-y^i)
Ans: is the deviation of the observed Y from the predicted Y^
sum(Yi-Y^)2
Ans: The sum of squares of all the deviations form the fitted line :
Since we know Y^ = a + bX, we can substitute for Y^ in the formula to get:
Ans: sum(Yi - a - bXi ) 2
When a and b have been found their numerical values (using the previous
two formulas and the data) for the two estimators a and b can be substituted
into the
Ans: prediction equation Y^ = a + bX
1. A and B are unknown parameters or also referred to as constants 2. X's are
known values (measured values with no associated errors) and are selected
by the user 3. For each value of X, the Yi ~ N(µ,σ 2 ) and independent 4. The
variance of Y with a given X is the same for all X (homoscedasticity)
Ans: regression assumptions:
Pretest - Stuvia US
