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2 years ago

The ratio 15min to an hr can be writtin in the form 1:n

Mathematics

2 answers:

goldfiish [28.3K]2 years ago

4 0

Answer:

1:4

Step-by-step explanation:

there are 60 minutes in an hour

60/15=4

15:60=1:4

Eduardwww [97]2 years ago

4 0

Answer:

1:4

Step-by-step explanation:

15/60 = 1/4 = 1:4

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Roughly 75% of students take the bus to school. If you were to ask 9 students in your class, what is the probability that at mos

GREYUIT [131]

The probability that at most 8 of them take the bus to school is 0.925, written in percentage form this is 92.5%

<h3>How to find the probability?</h3>

We know that roughly 75% of the students take the bus, then, if we select a student at random.

There is a probability of 0.75 that the student takes the bus.
There is a probability of 0.25 that the student does not take the bus.

The probability that at most 8 out of 9 students take the bus, is equal to one minus the probability of the 9 taking the bus, which is:

p = (0.75)^9 = 0.075

Then we have:

P = 1 - 0.075 = 0.925

The probability that at most 8 of them take the bus to school is 0.925, written in percentage form this is 92.5%

If you want to learn more about probability, you can read:

brainly.com/question/251701

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2 years ago

Please help will give brainliest and 30 pts

Rus_ich [418]

You’re not giving 30 points, you’re only giving 5

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3 years ago

Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality ra

anyanavicka [17]

Answer:

Because the P-value is _(<u>0.02) less </u> than the significance level 0.05, there <u> is </u> sufficient evidence to support the claim that there is a linear correlation between lemon imports and crash fatality rates for a significance level of α=0.05.

C. The results suggest that imported lemons cause car fatalities.

Step-by-step explanation:

Hello!

The study variables are:

X₁: Weight of imported lemons.

X₂: Car crash fatality rate.

The objective is to test if the imported lemons affect the occurrence of car fatalities. To do so you are asked to use a linear correlation test.

I've made a Scatterplot with the given data, it is attached to the answer.

To be able to use the parametric linear correlation you can use the parametric test (Person) or the nonparametric test Spearman. For Person, you need your variables to have a bivariate normal distribution. Since one of the variables is a discrete variable (ratio of car crashes) and the sample is way too small to make an approximation to a normal distribution, the best test to use is Spearman's rank correlation.

This correlation coefficient (rs) takes values from -1 to 1

If rs = -1 this means that there is a negative correlation between the variables

If rs= 1 this means there is a positive correlation between the variables

If rs =0 then there is no correlation between the variables.

The hypothesis is:

H₀: There is no linear association between X₁ and X₂

H₁: There is a linear association between X₁ and X₂

α: 0.05

To calculate the Spearman's correlation coefficient you have to assign ranks to the observed values of each variable, from the smallest to the highest). Then you have to calculate the difference (d)between the ranks and the square of that difference (d²). (see attachment)

The formula for the correlation coefficient is:

$rs= 1 - \frac{6* (sum of d^2)}{(n-1)n(n+1)}$

$rs= 1 - \frac{6* (40)}{4*5*6}$

rs= -1

For this value of the correlation coefficient, the p-value is 0.02

Since the p-value (0.02) is less than the significance level (0.05) the decision is to reject the null hypothesis. In other words, there is a linear correlation between the imported lemons and the car crash fatality ration, this means that the modification in the lemon import will affect the car crash fatality ratio.

Note: the correlation coefficient is negative, so you could say that there is a correlation between the variables and this is negative (meaning that when the lemon import increases, the car crash fatality ratio decreases)

I hope it helps!