Answer:
The amount the school pays is £32.40
Step-by-step explanation:
The cost of each pen = 15 pence
The cost of each ruler = 20 pence
The number of pens bought by the school = 150
The number of rulers bought by the school = 90
The cost reduction (discount) on the items bought = 1/5
Therefore, we have;
The total cost of the pens bought by the school = 150 × 15 = 2250 = £22.50
The total cost of the rulers bought by the school = 90 × 20 = 1800 = £18.00
The total cost of the writing materials (rulers and pens) bought by the school = £22.50 + £18.00 = £40.50
The discount = 1/5 total cost reduction = 1/5×£40.50 = $8.10
The amount the school pays = The total cost of the writing materials - The discount
The amount the school pays = £40.50 - $8.10 = £32.40
The amount the school pays = £32.40.
Answer:
I believe it depends on the equations..
Step-by-step explanation:
1.7x 10 =17
Hope this helps
Answer:
Step-by-step explanation:
When two or more quantities have the same exponents, their bases can be multiplied together.
Answer:
Step-by-step explanation:
Correlation occurs when we can observe a trend between the response/dependent variable (y) and the explanatory/independent variable (x).
When comparing two sets of data, we may observe and use correlation to determine whether or not the data presented if significant or not and whether or not it supports our hypothesis. We may want to see if this is just a fluke and whether or not the trend causes causation.
An example of this would be if we thought that the weight of female mice determines how many kids they have in a month.. Let's say that mice that weigh up to one ounce have 6 baby mice per month.
Let's say that the x variable is the weight which is between 0.25 oz and 1.25 oz and the y-variable is the amount of kids between each month. If another laboratory conducts the same study with the same type of mice with the same weights as us, we would need to determine if there is correlation and if there is causation and try to use this information to determine if both sets of data are significant to our hypothesis.
