Answer:
y = 0
Step-by-step explanation:
It is always a good idea to look at the question and make some observations about it. Here, you might observe ...
- all of the bases are powers of 3: 243 = 3^5; 9 = 3^2
- y is a factor of every exponent
The latter observation is important, because it means that when y=0, every exponential expression has a value of 1. Hence y = 0 is a solution.
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To solve the equation, you can write it in terms of powers of 3.
(3^5)^(-y) = (3^-5)^(3y)·(3^2)^(-2y)
3^(-5y) = 3^(-15y)·3^(-4y)
3^(-5y) = 3^(-19y)
-5y = -19y . . . . . . . . equating exponents; equivalent to taking log base 3
14y = 0 . . . . . . . . . . add 19y
y = 0 . . . . . . . . . . . one solution
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The rules of exponents we used are ...
(a^b)(a^c) = a^(b+c)
(a^b)^c = a^(bc)
1/a^b = a^-b
Answer:
63 calories
Step-by-step explanation:
Divide the number of calories by the number of servings
calories/servings
252/4
63
So, there are 63 calories per serving
Hope this helps!
Answer:
y = 0
Step-by-step explanation:
y =1/2(4)-2
y= 2-2
y=0
f(x) = 
note the range is {1, 5, 25, 125 } which can be expressed as
{ 
, 
, 
, 
}
the exponents being the domain { 0, 1, 2, 3 }
thus f(x) = an exponential function
Answer:
d. Both I and II are false
Step-by-step explanation:
When there is a high degree of linear correlation between the predictors the errors are found.
The basic objective of the regression model is to separate the dependent and independent variables. So if the variables have high degree of linear correlation then the multi collinearity causes problems or has errors. It is not necessary that multi collinearity must be present with high degree of linear correlation.
For example we have 3 variable of heat length and time. And all of them have a high degree of correlation. With increase in heat and time the length increases . But for multi collinearity with the increase of time and decrease of heat length does not increase. So this causes errors.
y-hat = 135 + 6x + errors
The linear relationship between height and weight is inexact. The deterministic relation in such cases is then modified to allow the inexact relationship between variables and a non deterministic or probabilistic model is obtained which has error which are unknown random errors.
y- hat= a + bXi + ei (i=1,2,3...)
ei are the unknown random errors.
<u><em>So both statements are false.</em></u>
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