Split cells.
This answer needs to be twenty characters long to qualify so here is this useless sentence.
Answer:
D. ARPANET was developed
Explanation:
ARPANET is an acronym for Advanced Research Projects Agency Network and it was established in 1969 by the Advanced Research Projects Agency (ARPA) of the United States Department of Defense.
ARPANET was the first wide area packet switching network for transmitting electronic data and communications between computers on a single network with a distribution functionality. The TCP/IP protocol was first implemented on the Advanced Research Projects Agency Network (ARPANET), as well as some other standard protocols such as the NCP and the 1822 protocol.
The development of ARPANET happened first in the evolution of the internet.
E-mail is an acronym for electronic mail and it was invented by Ray Tomlinson in 1971; World Wide Web was created by Tim Berners-Lee in 1990; Web 2.0 evolved in 1999.
You can use photo art to help show a product or a certain piece of technology to help give a better idea of a product or thing you're trying to demonstrate to your class or people.
In probability theory and statistics, a shape parameter is a kind of numerical parameter of a parametric family of probability distributions.[1]
Specifically, a shape parameter is any parameter of a probability distribution that is neither a location parameter nor a scale parameter (nor a function of either or both of these only, such as a rate parameter). Such a parameter must affect the shape of a distribution rather than simply shifting it (as a location parameter does) or stretching/shrinking it (as a scale parameter does).
Contents
Estimation Edit
Many estimators measure location or scale; however, estimators for shape parameters also exist. Most simply, they can be estimated in terms of the higher moments, using the method of moments, as in the skewness (3rd moment) or kurtosis (4th moment), if the higher moments are defined and finite. Estimators of shape often involve higher-order statistics (non-linear functions of the data), as in the higher moments, but linear estimators also exist, such as the L-moments. Maximum likelihood estimation can also be used.
Examples Edit
The following continuous probability distributions have a shape parameter:
Beta distribution
Burr distribution
Erlang distribution
ExGaussian distribution
Exponential power distribution
Fréchet distribution
Gamma distribution
Generalized extreme value distribution
Log-logistic distribution
Inverse-gamma distribution
Inverse Gaussian distribution
Pareto distribution
Pearson distribution
Skew normal distribution
Lognormal distribution
Student's t-distribution
Tukey lambda distribution
Weibull distribution
Mukherjee-Islam distribution
By contrast, the following continuous distributions do not have a shape parameter, so their shape is fixed and only their location or their scale or both can change. It follows that (where they exist) the skewness and kurtosis of these distribution are constants, as skewness and kurtosis are independent of location and scale parameters.
Exponential distribution
Cauchy distribution
Logistic distribution
Normal distribution
Raised cosine distribution
Uniform distribution
Wigner semicircle distribution
See also Edit
Skewness
Kurtosis
Location parameter
The correct answer is letter b
