<span><span>x=<span><span>−4</span>+<span><span><span><span><span>√21</span></span> or </span></span>x</span></span></span>=<span><span>−4</span>−<span>√<span>21</span></span></span></span>
Answer: 19 birds were at the shelter on Wednesday.
Step-by-step explanation:
Let x represent the number of birds that were at the shelter on Wednesday.
Let y represent the number of birds that were at the shelter on Wednesday.
Nicole found a record showing that there were a total of 40 birds and cats on Wednesday. This means that
x + y = 40
The animal shelter spends $5.50 per day to care for each bird and $8.50 per day to care for each cat. Nicole noticed that the shelter spent $283.00 caring for birds and cats on Wednesday. This means that
5.5x + 8.5y = 283 - - - - - - - - - - - - - -1
Substituting x = 40 - y into equation 1, it becomes
5.5(40 - y) + 8.5y = 283
220 - 5.5y + 8.5y = 283
- 5.5y + 8.5y = 283 - 220
3y = 63
y = 63/3 = 21
x = 40 - y = 40 - 21
x = 19
Answer and Step-by-step explanation:
Polynomial models are an excellent implementation for determining which input element reaction and their direction. These are also the most common models used for the scanning of designed experiments. It defines as:
Z = a0 + a1x1 + a2x2 + a11x12 + a22x22+ a12x1x2 + Є
It is a quadratic (second-order) polynomial model for two variables.
The single x terms are the main effect. The squared terms are quadratic effects. These are used to model curvature in the response surface. The product terms are used to model the interaction between explanatory variables where Є is an unobserved random error.
A polynomial term, quadratic or cubic, turns the linear regression model into a curve. Because x is squared or cubed, but the beta coefficient is a linear model.
In general, we can model the expected value of y as nth order polynomial, the general polynomial model is:
Y = B0 + B1x1 + B2x2 + B3x3 + … +
These models are all linear since the function is linear in terms of the new perimeter. Therefore least-squares analysis, polynomial regression can be addressed entirely using multiple regression
Answer:
hell naw
Step-by-step explanation:
