The number of cars that would possibly be sold in the next two years with the existing business model is modeled with the polynomial c(x)=−3x4+x5+2.16x3+1, where x represents the number of years after the current year. prince digit states that the degree of this polynomial is 4, because it is the exponent in the first term of the polynomial. is he correct? a) no, he is not correct. the degree of a polynomial in one variable is identified by the largest exponent. therefore, the degree of this polynomial is 5. b) no, he is not correct. the degree of a polynomial in one variable is the sum of the exponents in the polynomial. therefore, the degree of this polynomial is 12. c) no, he is not correct. the degree of a polynomial in one variable is the exponent of the term with the greatest coefficient. therefore, the degree of this polynomial is 3. d) yes, he is correct. the degree of a polynomial in one variable is the exponent in the first term of the given polynomial.

D)

Explanation: The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. Random variables can appear in random sequences. A random process is a sequence of random variables whose outcomes do not follow a deterministic pattern, but follow an evolution described by probability distributions. These and other constructs are extremely useful in probability theory and the various applications of randomness.

Randomness is most often used in statistics to signify well-defined statistical properties. Monte Carlo methods, which rely on random input (such as from random number generators or pseudorandom number generators), are important techniques in science, as, for instance, in computational science.[2] By analogy, quasi-Monte Carlo methods use quasirandom number generators.

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