The slope of a graph is base on the rise over run of the line connected by the points which composed of an X and Y coordinate. The rise of the line is its Y and the Run is X. Base on the assigning of variables of its x and y value, the slope is calculate by dividing the weeks by its test so the answer is 3 over 4 or 0.75
Clare would need 80 of fish, (1/3 of 240 is 80.)
Ian would need 200g of butter (If you find the amount of each ingredient needed for a one person-pie, and multiply them by the amount of people you need to feed, you will get your answer.)
We know that 80g of butter is used for 6 people, so by dividing 80 by 6, we know that for a one person-pie, you would need 13.33g of butter. Multiply 13.33 by 15 to find out how many grams of butter he would need for a pie for 15 people.
Answer:
<h3>
- 128 Superscript StartFraction 3 Over x EndFraction
</h3><h3>
- (4RootIndex 3 StartRoot 2 EndRoot)x
</h3><h3>
- (4 (2 Superscript one-third Baseline) ) Superscript x</h3><h3>
Step-by-step explanation:</h3>
Given the indicinal equation ![(\sqrt[3]{128} )^{x}\\](https://tex.z-dn.net/?f=%28%5Csqrt%5B3%5D%7B128%7D%20%29%5E%7Bx%7D%5C%5C)
According to one of the law of indices,
![(\sqrt[a]{m} )^{b}\\= (\sqrt{m})^\frac{b}{a}](https://tex.z-dn.net/?f=%28%5Csqrt%5Ba%5D%7Bm%7D%20%29%5E%7Bb%7D%5C%5C%3D%20%28%5Csqrt%7Bm%7D%29%5E%5Cfrac%7Bb%7D%7Ba%7D)
Applying this law to the question;
![(\sqrt[3]{128} )^{x}\\ = {128} ^\frac{x}{3}\\ \\= (\sqrt[3]{64*2})^{x} \\ = (4\sqrt[3]{2})^{x} \\= (4(2^{1/3} )^{x} )](https://tex.z-dn.net/?f=%28%5Csqrt%5B3%5D%7B128%7D%20%29%5E%7Bx%7D%5C%5C%20%3D%20%7B128%7D%20%5E%5Cfrac%7Bx%7D%7B3%7D%5C%5C%20%5C%5C%3D%20%28%5Csqrt%5B3%5D%7B64%2A2%7D%29%5E%7Bx%7D%20%5C%5C%20%3D%20%284%5Csqrt%5B3%5D%7B2%7D%29%5E%7Bx%7D%20%5C%5C%3D%20%284%282%5E%7B1%2F3%7D%20%29%5E%7Bx%7D%20%29)
The following are therefore true based on the following calculation
128 Superscript StartFraction 3 Over x EndFraction
(4RootIndex 3 StartRoot 2 EndRoot)x
(4 (2 Superscript one-third Baseline) ) Superscript x
Answer:
The regression equation for the winter rainy days is "Humidity = (β0 + β5) + β1Temperature".
Step-by-step explanation:
Given:
Humidity = β0 + β1Temperature + β2Spring + β3Summer + β4Fall + β5Rain + ε ...........(1)
Since there can be only one of spring, summer,fall, and winter at a point in time or in a season, we will have the following when there are winter rainy days:
Spring = 0
Summer = 0
Fall = 0
Rain = 1
Substituting all the relevant values into equation (1) and equating ε also to 0, a reduced form of equation (1) can be obtained as follows:
Humidity = β0 + β1Temperature + (β2 * 0) + (β3 * 0) + (β4 * 0) + (β5 * 1) + 0
Humidity = β0 + β1Temperature + 0 + 0 + 0 + β5 + 0
Humidity = (β0 + β5) + β1Temperature
Therefore, the regression equation for the winter rainy days is "Humidity = (β0 + β5) + β1Temperature".
Answer:
0.025
Step-by-step explanation:
-This is a conditional probability problem.
-Let L denote lens defect and C charging defect.
#We first calculate the probability of a camera having a lens defect;
#Calculate the probability of a camera having a charging defect:
The the probability that a camera has a lens defect given that it has a charging defect is calculated as:
Hence, the probability that a camera has a lens defect given that it has a charging defect is 0.025
