Answer:
Time = 6 years
Step-by-step explanation:
Data;
Simple interest = $507.50
Principal = $290
Rate = 12.5% = 12.5/100 = 0.125
Time = ?
To solve this question, we'll need to use the formula for simple interest
S.I = PRT / 100
Or
S.I = P(1 + RT)
S.I = simple interest
P = principal
R = rate
T = time
S.I = P(1 + RT)
507.50 = 290(1 + 0.125*T)
507.50 = (290 × 1) + (290 * 0.125T)
507.50 = 290 + 36.25T
Solve for T
36.25T = 507.50 - 290
36.25T = 217.50
T = 217.50 / 36.25
T = 6
The time tenure on the loan was 6 years
The answer to number 1 = 40
Answer:
30 cm^2
Step-by-step explanation:
the triangle is 36
the rectangle is 6
36-6=30
Hey there!
Let's take a look at this problem... Okay so for these questions there is formula known as the point-slope formula that is used...
(y - y₁) = m(x - x₁)
From here on out you substitute in your given values...
(y + 3) = -6(x + 2)
y + 3 = -6x -12
<u>y = -6x -15</u><u /> <----- That's your answer!
I hope this helped you!
<u>Please vote me for Brainliest if there is a second answer!</u><u /> ^__^
Explanation:
Marginal distribution: This distribution gives the probability for each possible value of the Random variable ignoring other random variables. Basically, the values of other variables is not considered in the marginal distribution, they can be any value possible. For example, if you have two variables X and Y, the probability of X being equal to a value, lets say, 4, contemplates every possible scenario where X is equal to 4, independently of the value Y has taken. If you want the probability of a dice being a multiple of 3, you are interested that the dice is either 3 or 6, but you dont care if the dice is even or odd.
Conditional distribution: This distribution contrasts from the previous one in the sense that we are restricting the universe of events to specific condition for other variable, making a modification of our marginal results. If we know that throwing a dice will give us a result higher than 2, then to in order to calculate the probability of the dice being a multiple of 3 using that condition, we have two favourable cases (3 and 6) from 4 total possible results (3,4,5 and 6) discarding the impossible values (1 and 2) from this universe since they dont match the condition given (note that the restrictions given can also reduce the total of favourable cases).
The joint distribution calculates the probabilities for two different events (related to two different random variables) occuring simultaneously. If we want to calculate the joint probability of a dice being multiple of 3 and greater than 2 at the same time, our possible cases in this case are 3 and 6 from 6 possible results. We are not discarding 1 or 2 as possible results because we are not assuming, that the dice is greater than 2, that is another condition that we should met in the combination of events.