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.
<span>A) The constant is 7.5. FALSE. 7.5 is a coefficient, not a constant.
</span><span>B) The coefficients are 7.5 and -9. FALSE. The coefficients are 7.5, -1/9 and 2. 50, being a constant, could be regarded as a coefficient in this case.
</span><span>C) The variables are x and y. FALSE. There are three variables: x, y and z.
</span><span>D) The like terms are 7.5y and 2y. TRUE. The variable (y) is the same in each term.</span><span>
</span>
Answer:
6x-108
Step-by-step explanation:
-3 · 2(7 · 2 - x + 4)
= ((−3)(2))((7)(2) +− x + 4)
= −84 + 6x − 24
= 6x − 108
The answer is 6x - 108
Answer:
y=24
Step-by-step explanation:
