Answer:
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.
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Answer:
A
Step-by-step explanation:
Two facts need to guide your answer.
One
The highest power is odd: you know this because an even power would start on the left come down do it's squiggles if had any and wind up on the right going up.
This graph comes down on the left does it's squiggles and then goes further down on the right. That's the behavior of something whose highest power is odd.
Two
The leading coefficient, the number in front of the highest power must be minus. If it was positive as in y = x^3 the graph would be the mirror image of what it is.
Argument
B and D cannot be true. The highest power is even.
C is false because the leading coefficient is + 1.
So that leave A which is the answer.
The graph is included with this answer
Answer:
x= 16±√-64 over 2
Step-by-step explanation:
x= -(-16)±√(-16)²-4×1×80 over 2×1
x= 16±√256-320 over 2
x= 16±√-64 over 2
can also be represented with all real numbers.