Answer:
b. The histogram is decentralized over several data points.
Step-by-step explanation:
Kernel density estimators can be classified as non-parametric density estimators. The Kernel density estimators first smooth each data point into a density bump, then sum them up to obtain the final density estimated curve. A good histogram analysis skill is reqired to understand kernel density estimators.
Answer:
0.875
Step-by-step explanation:
P(H=0) = 0.125
P(H=1) = 0.375
P(H=2) = 0.375
P(H=3) = 0.125
P(H<3) = P(H=0) + P(H=1) + P(H=2)
P(H<3) = 0.125 + 0.375 + 0.375
P(H<3) = 0.875
Answer:
y=10
Step-by-step explanation:
1.7y+37 is 14 less than the value of 9.3y–25
- 1.7y + 37 = 9.3y - 25 - 14
- 1.7y + 37 = 9.3y - 39
- 9.3y - 1.7y = 37 + 39
- 7.6y = 76
- y = 76/7.6
- y = 10
Answer is 10
<span><span>u2</span> – 11u + 24 = 0 where u = (x2 – 1) the first awnser, a.</span>