Answer:
<em>0.85 Euros have the same amount as 1 U.S dollar.</em>
Step-by-step explanation:
The given equation is: , where
amount of Euros and
amount of U.S dollar.
Now for 1 U.S dollar, we need to plug into the above equation. So, we will get.........
Thus, 0.85 Euros have the same amount as 1 U.S dollar.
Answer:
a. 0
b. Yes
c. The manufacturers claim is not plausible
d. 0.3446
e. A sample mean life time of 39.8hr is not unusually short.
f. The manufacturers claim us plausible
Step-by-step explanation:
Please see attachment
ANSWER
23.6 feet
EXPLANATION
The given triangle is a right triangle.
The hypotenuse is 27 ft.
The given angle I s 29°
The unknown side x, is adjacent to the given angle.
We use the cosine ratio to get,
We multiply both sides by 27 to get;
to the nearest tenth.
Answer:
x=
Step-by-step explanation:
Step 1: Multiply both sides by x,
gx=−c+x
Step 2: Add -x to both sides.
gx+−x=−c+x+−x
gx−x=−c
Step 3: Factor out variable x.
x(g−1)=−c
Step 4: Divide both sides by g-1.
x=
Hope this helps!
Answer:
The average change in rent can be determined by substituting the value of <em>X</em> as 5000 in the regression equation.
Step-by-step explanation:
A simple linear regression line is used to predict the value of the dependent variable from the independent variable.
The general form is:
Dependent variables are those variables that are under study, i.e. they are being observed for any changes when the other variables in the model are changed.
The dependent variables are also known as response variables.
In this case the dependent variable is the average change in rent for a 1-bedroom apartment.
Independent variables are the variables that are being altered to see a proportionate change in the dependent variable. In a regression model there can be one or more than one independent variables.
The independent variables are also known as the predictor variables.
In this case the independent variable is the average income in a city.
So, for an increase of $5,000 in incomes the average change in rent can be determined by substituting the value of <em>X</em> as 5000 in the regression equation.