The correction is C.
One of the major factors that determine the quality of a printer is the resolution of that printer. The resolution is also called DPI [Dots Per Inch]. The DPI refers to amount of ink that an inkjet printer can place in a one inch line of a printed document. The higher the DPI of a printer, the higher the resolution.
I think the answer is c wooden frame
Considering the computer system technology, the RAID configuration, known as byte-striped with an error check, and spreads the data across multiple disks at the byte level with one disk dedicated to parity bits is known as <u>RAID Level 5.</u>
<h3>What is the RAID Level 5?</h3>
RAID Level 5 is the Redundant Array of Inexpensive (or Independent) Disks Level 5. RAID Level 5 works on strips to transfer data over multiple disks in an array.
RAID Level 5 is also known to record information, with the ability to withstand numerous failures.
<h3>
Other types of Redundant Array of Inexpensive (or Independent) Disks</h3><h3 />
- RAID level 0
- RAID level 1
- RAID level 2
- RAID level 3
- RAID level 4
Hence, in this case, it is concluded that the correct answer is RAID Level 5.
Learn more about RAID configuration here: brainly.com/question/9305378
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:
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Explanation:
