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3 years ago

The skyway express train travels about 800 miles a day how far dose it travel in 10 days?

2 answers:

5 0

To find the answer you will have to multiply 800 by 10. 800 multiplied by 10 equals 8,000
The final answer is : The skyway express train travels about 8,000 miles in 10 days

stich3 [128]3 years ago

3 0

800 miles a day is the rate.

800*10 = 8000

Answer: It travels 8,000 miles in 10 days

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What does a residual value of 1. 3 mean when referring to the line of best fit of a data set? A data point is 1. 3 units above t

Darina [25.2K]

You can use the fact that a residual value is obtained by subtracting the prediction by the line of best fit from the actual data value we have.

The residual value 1.3 when referring to the line of best fit of a data set means:

Option B: A data point is 1.3 units below the line of best fit

<h3>What is a residual value and how is it calculated?</h3>

First of all, this whole story starts with data. We get data and we try to fit a line which can best imitate the way data is lying on the coordinate plane.

Remember that on a 2d coordinate cartesian plane, we have (x,y) called as point on plane and x is abscissa and y is called ordinate.

Let the best fit line be denoted by y = mx + c (assuming we're working with 2d data) with slope m and y-intercept c.

Now, this line is used to predict where can the next data point may lie.

When this best fit line is used to predict already present data point, we get the error that best fit line made when predicting the real data.

This is measured by "residual value"

For data point with ordinate b, we suppose get prediction as y

Then we have the residual value as y - b.

Remember, the prediction is before the real data point's ordinate.

<h3>How to know what does 1.3 residual value mean?</h3>

Let the real value be b and the predicted value be y from which this residual was calculated.

Then we have:

y - b = 1.3

y = 1.3 + b

Thus, we see that prediction is bigger than the real data point's y-ordinate. Since y axis has increasing value as we go higher and higher vertically, thus this prediction value's ordinate is higher than that of real value's ordinate.

The prediction, since shows the height of best fit line on that input point, thus we have:

Option B: A data point is 1.3 units below the line of best fit.

Learn more about line of best fit here:

brainly.com/question/2396661

8 0

2 years ago

Simplify the expression 2x - 7 + 4x

bija089 [108]

Answer:

6x−7

Step-by-step explanation:

2x−7+4x

=2x+−7+4x

Combine Like Terms:

=2x+−7+4x

=(2x+4x)+(−7)

=6x+−7

3 0

3 years ago

Read 2 more answers

A doctor released the results of clinical trials for a vaccine to prevent a particular disease. In these clinical trials, 200,0

CaHeK987 [17]

Answer:

1) No, there is not enough evidence to support the claim that the vaccine was effective (P-value=0.104) .

The null and alternative hypothesis are:

$H_0: \pi_1-\pi_2=0\\\\H_a:\pi_1-\pi_2< 0$

being the subindex 1 for the experimental group and subindex 2 for the control group.

2) Test statistic z=-1.26

Step-by-step explanation:

This is a hypothesis test for the difference between proportions.

The claim is that the vaccine was effective.

Then, the null and alternative hypothesis are:

$H_0: \pi_1-\pi_2=0\\\\H_a:\pi_1-\pi_2< 0$

The significance level is 0.01.

The sample 1 (experimental group), of size n1=100000 has a proportion of p1=0.0002.

$p_1=X_1/n_1=21/100000=0.0002$

The sample 2 (control group), of size n2=100000 has a proportion of p2=0.0003.

$p_2=X_2/n_2=30/100000=0.0003$

The difference between proportions is (p1-p2)=-0.0001.

$p_d=p_1-p_2=0.0002-0.0003=-0.0001$

The pooled proportion, needed to calculate the standard error, is:

$p=\dfrac{X_1+X_2}{n_1+n_2}=\dfrac{21+30}{100000+100000}=\dfrac{51}{200000}=0.00026$

The estimated standard error of the difference between means is computed using the formula:

$s_{p1-p2}=\sqrt{\dfrac{p(1-p)}{n_1}+\dfrac{p(1-p)}{n_2}}=\sqrt{\dfrac{0.00026*0.99974}{100000}+\dfrac{0.00026*0.99974}{100000}}\\\\\\s_{p1-p2}=\sqrt{0+0.0000000025}=\sqrt{0.0000000051}=0.000071$