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: D. The histogram is centered over several data points.
Step-by-step explanation:
The Box density Estimation is used when given several values of data plotted as data points, to generate a smooth curve. The histogram in the box kernel estimate is centered over several data points. Blocks are now placed on each of the data points, and this helps to eliminate the histogram's reliance on the endpoints. It results in some sort of convergence.
The major differences between the kernel density estimation and histogram is that there are no endpoints, it is smooth, and relies largely on bandwidth.
