Answer:
The current through each lamp is 0.273 Amperes
Power dissipated in each lamp is 0.082W
Explanation:
Battery v = 1.5 V
Each lamp has resistance, r = 1.1 Ohms
The 5 lamps in series will therefore have total resistance, R = 5 * 1.1 = 5.5 Ohms
The current through each lamp, I = v/R = 1.5 / 5.5 = 0.273 Amperes
Power dissipated in each lamp = I² * r = 0.273² * 1.1 = 0.082W
I don’t know how to speak the laungue or know this language
Answer:
#WeirdestQuestionOfAllTime
Explanation:
Answer:
<em>the % recovery of aluminum product is 80.5%</em>
<em>the % purity of the aluminum product is 54.7%</em>
<em></em>
Explanation:
feed rate to separator = 2500 kg/hr
in one hour, there will be 2500 kg/hr x 1 hr = 2500 kg of material is fed into the machine
of this 2500 kg, the feed is known to contain 174 kg of aluminium and 2326 kg of rejects.
After the separation, 256 kg is collected in the product stream.
of this 256 kg, 140 kg is aluminium.
% recovery of aluminium will be = mass of aluminium in material collected in the product stream ÷ mass of aluminium contained in the feed material
% recovery of aluminium = 140kg/174kg x 100% = <em>80.5%</em>
% purity of the aluminium product = mass of aluminium in final product ÷ total mass of product collected in product stream
% purity of the aluminium product = 140kg/256kg
x 100% = <em>54.7%</em>
Answer:
By running multiple regression with dummy variables
Explanation:
A dummy variable is a variable that takes on the value 1 or 0. Dummy variables are also called binary
variables. Multiple regression expresses a dependent, or response, variable as a linear
function of two or more independent variables. The slope is the change in the response variable. Therefore, we have to run a multiple regression analysis when the variables are measured in the same measurement.The number of dummy variables you will need to capture a categorical variable
will be one less than the number of categories. When there is no obvious order to the categories or when there are three or more categories and differences between them are not all assumed to be equal, such variables need to be coded as dummy variables for inclusion into a regression model.
