You'll need 2 more lines to complete this two column proof.
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Line 4
For the "statement" portion, you'll say something like
The reason for this statement is "transitive property"
We're basically combining lines 1 and 3 to form this new line.
The transitive property is the idea that if A = B and B = C, then A = C. We connect the statements like a chain.
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Line 5
The statement is what you want to prove since this is the last line.
So the statement is
The reason is "converse of corresponding angles theorem"
As you can probably guess, this theorem says "If two corresponding angles are congruent, then the lines are parallel".
To prove that lines P and Q are parallel based on congruent angles, utilize the geometric understanding that if a transversal intersects two parallel lines, the corresponding angles are congruent. Given that angle 1 is congruent to angle 2, we can infer that the lines forming these angles (P and Q) are parallel.
To prove that the lines P and Q are parallel using the fact that angle 1 is congruent to angle 2, you will utilize the concept of congruent angles. When two parallel lines are intersected by a transversal, the corresponding angles are congruent. Therefore, if we know that angle 1 is congruent to angle 2, we can say the lines forming these angles are parallel.
Here are the steps:
This process uses reasoning of the geometry and reviews the core concept of parallel lines and transversals in proving that P is parallel to Q.
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Answer:
False
Step-by-step explanation:because it is.
Answer:
20 grams
Step-by-step explanation:
10 grams is 50%. 50% x 2= 100%. so you do 50/100=10/20, and you will get 2. Apply that to the 10, 10 x 2=20.
Answer:
Step-by-step explanation:
Sir the graph pls
Answer:it would be d
Step-by-step explanation:
An ordinary Regression model that treats the response Y is (a) True False, (w) True
What is Regression?
A statistical method called regression links a dependent variable to one or more independent (explanatory) variables.
A regression model can demonstrate whether changes in one or more of the explanatory variables are related to changes in the dependent variable.
A) Models for numerical response variable, like ANOVA and linear regression are special cases of GLMs
for these model the following holds
1. Random component has a normal distribution
2. Systematic component α+β₁x₁+β₂x₂+...........βₓxₓ
3. link function = identity (g(µ)=µ)
GLMs can generalise these models with response Y as normally distributed, hence the statement is True
B) With a GLM. Y does not need to have a normal distribution and one can model a function of the mean of Y instead of just the mean itself. but in order to get ML estimates the variance of Y must be small. This small variance of Y is the reason for ML estimator to be the best one. hence the statement is false.
An ordinary Regression model that treats the response Y is (a) True False, (w) True
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