To answer your question it is false. This is because excels default orientation is portrait
Answer:
D. at the intersection of at least two constraints.
Explanation:
Linear programming is an optimization technique which is fine for the purpose of getting the best solution such as maximizing profit or certain o 4th era quantities. It is fine by modelling real life problems into mathematical models that have linear relationships or constraints such as in the form of objective functions. In linear programming, an objective function defines the formula for quantity optimization and the goal from this is to determine variable values that maximize or minimize the objective function depending on the problem robbery solved.
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
<span>True - it's important to always adjust mirrors after adjusting the position of your seat in the car when driving. This is essential to ensure you have a good view of the road and are able to spot possible hazards and park correctly.</span>
Answer:
I do not play but i would say that the best character would be Diluc
Explanation:
