So the rule with multiplying exponents of the same base is 
. Apply this rule here:
Next, the rule with converting negative exponents into positive ones is 
. Apply this rule here:
<u>Your final answer is 1/49.</u>
<h2>------------------------------------------------</h2>
So an additional rule when it comes to exponents is ![x^{\frac{m}{n}}=\sqrt[n]{x^m}](https://tex.z-dn.net/?f=%20x%5E%7B%5Cfrac%7Bm%7D%7Bn%7D%7D%3D%5Csqrt%5Bn%5D%7Bx%5Em%7D%20)
In this case, your fractional exponent, x^9/7, would be converted to ![\sqrt[7]{x^9}](https://tex.z-dn.net/?f=%20%5Csqrt%5B7%5D%7Bx%5E9%7D%20)
. However, I had just realized you can further expand this.
Remember the rule I had mentioned earlier about multiplying exponents of the same base? Well, you can apply it here:
![\sqrt[7]{x^9}=\sqrt[7]{x^7*x^2}=x\sqrt[7]{x^2}](https://tex.z-dn.net/?f=%20%5Csqrt%5B7%5D%7Bx%5E9%7D%3D%5Csqrt%5B7%5D%7Bx%5E7%2Ax%5E2%7D%3Dx%5Csqrt%5B7%5D%7Bx%5E2%7D%20)
Your final answer would be ![x\sqrt[7]{x^2}](https://tex.z-dn.net/?f=%20x%5Csqrt%5B7%5D%7Bx%5E2%7D%20)
Answer:
17
Step-by-step explanation:
I would just solve them individually for 3 and then add them together. f(x)=6(3)+3 = 21 and g(x)= 3-7= -4
(f+g)(3) = 21-4= 17
Answer:
y= x/zw
Step-by-step explanation:
Isolate the variable by dividing each side by the factors that dont contain the variable
x = zyw /zw
y=x/zw
Answer:
n=19
d=12
$225n+$250d=
$225(19)+$250(12)=
4275+3000
=7275
Step-by-step explanation:
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.
