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Lemur [1.5K]
3 years ago
5

In a study of towns there was a positive correlation between the number of mail boxes and number of traffic lights in a town. Wh

ich variable is most likely the lurking variable that explains the correlation?
A commute times

B population

C ages of residents

D highest temperatures
Mathematics
2 answers:
mr Goodwill [35]3 years ago
8 0

Answer: B.population

Step-by-step explanation:

We know that a lurking variable is a variable that is not included in the analysis as an explanatory variable or response variable but it can affect the results of the analysis of relationships between variables.

In statistics, the population represents a very large group of participants that have at least one common feature.

Given: In a study of towns there was a positive correlation between the number of mail boxes and number of traffic lights in a town.  But it actually depends on the population too. So the interpretation here can be affected by the population.

Therefore, the population is most likely the lurking variable that explains the correlation .

Mariulka [41]3 years ago
5 0
<span>B population is correct......
</span>
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6 0
3 years ago
2564.0 millimeters tall. If the boards are too long they must be trimmed, and if the boards are too short they cannot be used. A
Leokris [45]

Answer:

We conclude that the board's length is equal to 2564.0 millimeters.

Step-by-step explanation:

We are given that a sample of 26 is made, and it is found that they have a mean of 2559.5 millimeters with a standard deviation of 15.0.

Let \mu = <u><em>population mean length of the board</em></u>.

So, Null Hypothesis, H_0 : \mu = 2564.0 millimeters    {means that the board's length is equal to 2564.0 millimeters}

Alternate Hypothesis, H_A : \mu \neq 2564.0 millimeters      {means that the boards are either too long or too short}

The test statistics that will be used here is <u>One-sample t-test statistics</u> because we don't know about the population standard deviation;

                             T.S.  =  \frac{\bar X-\mu}{\frac{s }{\sqrt{n}} }  ~  t_n_-_1

where, \bar X = sample mean length of boards = 2559.5 millimeters

            s = sample standard deviation = 15.0 millimeters

             n = sample of boards = 26

So, <em><u>the test statistics</u></em> =  \frac{2559.5-2564.0}{\frac{15.0 }{\sqrt{26}} }  ~   t_2_5

                                     =  -1.529    

The value of t-test statistics is -1.529.

Now, at a 0.05 level of significance, the t table gives a critical value of -2.06 and 2.06 at 25 degrees of freedom for the two-tailed test.

Since the value of our test statistics lies within the range of critical values of t, so <u><em>we have insufficient evidence to reject our null hypothesis</em></u> as it will not fall in the rejection region.

Therefore, we conclude that the board's length is equal to 2564.0 millimeters.

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4 years ago
I hope that we pass this semester god bless :)) calming it
Umnica [9.8K]

Answer:

me too

Step-by-step explanation:

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