Answer:
Ram and the hard drive
Explanation:
A page of RAM is written to the harddisk and read back when it is needed again. That way it may seem you have more RAM than you actually have, at the cost of performance of course.
Answer:
Explanation:
The following code is written in Java. It creates a program that asks the user how many cookies they want to make. Then it adjusts the original recipe based on the ratio of the original amount of cookies it makes to the amount the user wants. Finally, passing the output of the new adjusted recipe to the user. Due to technical difficulties, I have added the code as a txt file attached below.
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
Answer:
Explanation: The first thing to calculate is what IP addresses are used by the largest LAN. Because the LAN has 100 hosts, 7 bits must be left for host bits.
Explanation:
Explanation: The first thing to calculate is what IP addresses are used by the largest LAN. Because the LAN has 100 hosts, 7 bits must be left for host bits.
