From the figure, we can conclude that the little triangle is an isosceles triangle, the greatest angle is 90 because ∠A = 90 and they are supplementary, therefore, using the triangle sum theorem:
Answer:

General Formulas and Concepts:
<u>Calculus</u>
Limits
Limit Rule [Variable Direct Substitution]: 
Limit Rule [Variable Direct Substitution Exponential]: 
Limit Property [Multiplied Constant]: 
Step-by-step explanation:
<u>Step 1: Define</u>
<em>Identify</em>

<u>Step 2: Solve</u>
- Rewrite [Limit Property - Multiplied Constant]:
![\displaystyle \lim_{x \to 0} \frac{1}{4}[f(x)]^4 = \frac{1}{4} \lim_{x \to 0} [f(x)]^4](https://tex.z-dn.net/?f=%5Cdisplaystyle%20%5Clim_%7Bx%20%5Cto%200%7D%20%5Cfrac%7B1%7D%7B4%7D%5Bf%28x%29%5D%5E4%20%3D%20%5Cfrac%7B1%7D%7B4%7D%20%5Clim_%7Bx%20%5Cto%200%7D%20%5Bf%28x%29%5D%5E4)
- Evaluate limit [Limit Rule - Variable Direct Substitution Exponential]:
![\displaystyle \lim_{x \to 0} \frac{1}{4}[f(x)]^4 = \frac{1}{4}(4^4)](https://tex.z-dn.net/?f=%5Cdisplaystyle%20%5Clim_%7Bx%20%5Cto%200%7D%20%5Cfrac%7B1%7D%7B4%7D%5Bf%28x%29%5D%5E4%20%3D%20%5Cfrac%7B1%7D%7B4%7D%284%5E4%29)
- Simplify:
![\displaystyle \lim_{x \to 0} \frac{1}{4}[f(x)]^4 = 64](https://tex.z-dn.net/?f=%5Cdisplaystyle%20%5Clim_%7Bx%20%5Cto%200%7D%20%5Cfrac%7B1%7D%7B4%7D%5Bf%28x%29%5D%5E4%20%3D%2064)
Topic: AP Calculus AB/BC (Calculus I/I + II)
Unit: Limits
Book: College Calculus 10e
Answer:
Lashawn
Step-by-step explanation:
333/222=1 1/2part salsa per egg - Lashawn
777/555= 1 2/5 part salsa per egg - Gilberta
1 1/2 is greater than 1 2/5
Answer:
Pvalue = 0.1505
y = 0.550x1 + 3.600x2 + 7.300
Step-by-step explanation:
Given the data :
Study Hours GPA ACT Score
5 4 27
5 2 18
5 3 18
1 3 20
2 4 21
Using technology, the Pvalue obtained using the Fratio :
F = MSregression / MSresidual = 30.228571/ 8.190476 = 3.69
The Pvalue for the regression equation is:
Using the Pvalue from Fratio calculator :
F(1, 3), 3.69 = 0.1505
Using the Pvalue approach :
At α = 0.01
Pvalue > α ; Hence, we fail to reject H0 and conclude that ; There is not enough evidence to show that the relationship is statistically significant.
The regression equation :
y = A1x1 + A2x2 +... AnXn
y = 0.550x1 + 3.600x2 + 7.300
x1 and x2 are the predictor variables ;
y = predicted variable