Answer:
The length of rectangular panel is 6.4 cm.
Step-by-step explanation:
We are given the following information in the question:
Area of rectangular solar panel = 128 square cm
Width of panel = 20 cm
We have to find the length of the panel
Formula:
Hence, the length of rectangular panel is 6.4 cm.
This is an example of direct variation as greater would be length and breadth, the greater would be the area.
Answer:
the answer is 12 cm
Step-by-step explanation:
Because of 8 cm on the small one, the 16 is double that, which means that 6 on the small would be 12 on the large
9514 1404 393
Answer:
4mn/(3m+12)
Step-by-step explanation:
It is often helpful to factor expressions so that common factors can cancel.
The missing angle of the given triangle is 59°.
What is vertically opposite angles?
Angles that are vertically opposed to one another are always equal to one another. A vertical angle and the angle to which it is next are also supplementary angles; that is, they sum up to 180 degrees. As an illustration, if two lines connect to form an angle, say X=45°, then the angle's opposite angle is also 45°.
Here consider angle with 31° and 49° triangle is 1. We know that sum of all angles in triangle is add upto 180°. Then,
⇒ 49°+31°+x = 180°
⇒ 80° + x= 180°
⇒x= 180°-80°=100°
Then unknown angle in triangle 1 ia 100°.
Now according to the vertically opposite angle theorem , opposite angles are equal to each other. Then ,
In triangle 2 angle is 100° and 21°. Then missing angle is,
⇒ 100°+21°+missing angle =180°
⇒Missing angle = 180°-100°-21°
⇒Missing angle = 59°
Therefore the answer is 59°.
To learn more about Vertical angle ,
brainly.com/question/18450499
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Answer:
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.
