Angie earns $9.00 an hour at her job. • She works 15 hours a week. • She receives a 5% raise. How much more money will she earn
1 answer:
First we have to find how much she earns each week before the raise. For this we multiply Angies earnings per hour by the amount of hours she works per week:
Angie earns $135 per week. Now she get's a raise of 5%. We need to find 5% of $135 and then add it to $135:
After the raise, Angie earns $6.75/week more than before the rise. So:
Angie will earn $141.75 a week after the raise
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Answer:
5
Step-by-step explanation:
Let's call the length AD y.
Hope this helps!
It is given that:
Monthly Rent = A
Total Money Paid to Landlord = Y
The landlord requires initial payment to include:
- 1st month's rent
month's rent or 50% of monthly rent for Security Deposit
- 40% of monthly rent for utility bills
- 5% of monthly rent for health club
- 13% of monthly rent per person for other services
Also, Total Amount Paid to the Landlord = $2574
Number of People Sharing the Apartment = 3
Basis the above information,
Y = (100% * A) + (50% * A) + (40% * A) + (5% * A) + (13% * A * 3)
⇒ Y = A + (0.5 * A) + (0.4 * A) + (0.05 * A) + (0.39 * A)
⇒ Y = A + (0.5 * A) + (0.4 * A) + (0.05 * A) + (0.39 * A)
⇒ Y = 2.34 * A
Substituting the value of Y to determine A
⇒ 2,574 = 2.34 * A
⇒ A = 1,100
Hence, the monthly rent is $1,100
Check the picture below, is a negative angle, thus, is going "clockwise"
Answer:
D. No, because the sample size is large enough.
Step-by-step explanation:
The central limit theorem states that "if we have a population with mean μ and standard deviation σ and take sufficiently large random samples from the population with replacement, then the distribution of the sample means will be approximately normally distributed. This will hold true regardless of whether the source population is normal or skewed, provided the sample size is sufficiently large".
Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".
If the sample size is higher than 30, on this case the answer would be:
D. No, because the sample size is large enough.
And the reason is given by The Central Limit Theorem since states if the individual distribution is normal then the sampling distribution for the sample mean is also normal.
From the central limit theorem we know that the distribution for the sample mean is given by:
If the sample size it's not large enough n<30, on that case the distribution would be not normal.