Answer:
2
Step-by-step explanation:
we know the line goes through the origin, so we have point (0,0). next, find another point the line goes through. for example, point (2,4)
the slope formula for points (x1, y1) and (x2, y2) is (y2 - y1) / (x2 - x1). plug in the 2 points to get (4-0)/(2-0) = 4/2 = 2
350 calories lost per day and there are 3,500 calories in a pound
350x10=3,500
it will take Judy 100 days to lose 10 pounds.
We will start off working on the right hand side.
<span>cot x - tan x </span>
<span>= [cos x / sin x] - [sin x / cos x] </span>
<span>= [(cos x)^ 2 - (sin x)^2] / [sin x cos x] </span>
<span>This is where it gets a bit tougher if you do not have your formula list with you. </span>
<span>(cos x)^ 2 - (sin x)^2 = cos(2x) </span>
<span>sin 2x = 2 sin x cos x </span>
<span>Note that by arranging the second formula, we will have sin x cos x = (1/2) sin 2x </span>
<span>Hence, we will get: </span>
<span>[(cos x)^ 2 - (sin x)^2] / [sin x cos x] </span>
<span>= [cos 2x] / (1/2)[sin 2x] </span>
<span>= 2[cos 2x] / [sin 2x] </span>
<span>= 2cot 2x </span>
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Hope my answer would be a great help for you. If you have more questions feel free to ask here at Brainly.
Answer: D) the significance level of the test
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Explanation:
The significance level of the test, also known as "alpha", is the probability of making a type 1 error. A type 1 error is where you reject the null hypothesis but it was true all along.
The null hypothesis is where we test a certain probability distribution (eg: normal distribution). Specifically we gather a sample of values and compute the test statistic. If the probability of getting that test statistic or more extreme is smaller than alpha, then we reject the null. This probability value is known as the p-value.
If you lower the alpha value, then that will make it more likely you do not reject the null. Consider an example where alpha = 0.10 to start with. If you get a p-value of 0.02, then you would reject the null. The same would apply for alpha = 0.05; however, with alpha = 0.01, the p-value is no longer smaller than alpha. At this point we do not reject the null. Your textbook may use the phrasing "fail to reject the null".
Going in the opposite direction, increasing the alpha value will make it more likely to reject the null. Each time you adjust the alpha value, keep the p-value to some fixed number (between 0 and 1).
