Please help...If g(x) = x2 + 2, find g(3).
A: 9
B: 8
C: 11
D: 6
Answers
As the
To find the value of g(3) we have to put the value of equal to 3 everywhere in g(
)
Therefore
on solving the equation
Hence g(3) =11 is the answer
Hope this will help
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The set of ordered pairs (-1,8), (0, 3), (1, -2), and (2-7) represent a function. What is the range of the function?{xx=-1, 0, 1, 2} {y y=-7, -2, 3, 8} {xx=-7, -2, -1,0, 1, 2, 3, 8} {y y=-7, -2, -1, 0, 1, 2, 3, 8}
Answers
Answer:
Distance and area is not preserved
Step-by-step explanation:
Answer:
Distance and area is not preserved
Step-by-step explanation:
Answers
Unless you want it as
3*100000+1*10000+4*100+9
Or even
3*10^5+1*10^4+4*10^2+9
Answers
The list of the steps should be explained below:
- The calculation is as follows:
Triangle has 3 angles, A, B, C.
The total interior angle of a triangle is 180.
Now
180 = A + B + C
180 = 72 + B + 113
180 = B + 185
B = 180 - 185
B = -5
So, the above proves that the figure is not a triangle because angle B is negative
Learn more: brainly.com/question/994316?referrer=searchResults
Answer:
Step-by-step explanation:
From, the given figure, it is given that and
.
Using the exterior angle property in ΔABC, we have
⇒
⇒
⇒
Therefore, the measure of is
.
Answers
A. 2
B. 509
C. 25
D. -1
Answers
Answer:
d I think but dont quote me
Step-by-step explanation:
idk I just guessed
Answers
x=1x
if you have a cow, how many do you have? 1
if ou have an x, how many do you have? 1
1 is coeficinet
Final answer:
The adjusted multiple coefficient of determination accounts for the number of independent variables in the model.
Explanation:
The adjusted multiple coefficient of determination, also known as the adjusted R-squared, is a statistical measure used in regression analysis to assess the goodness-of-fit of a regression model. It takes into account the number of independent variables in the model and adjusts the coefficient of determination (R-squared) to account for the degrees of freedom.
The adjusted R-squared provides a more accurate measure of the model's predictive power by penalizing the inclusion of unnecessary variables that do not contribute significantly to the model's explanatory power. It helps to prevent overfitting and provides a more reliable estimate of the model's performance.
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