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hichkok12 [17]
3 years ago
15

Describe the relationship between two variables when the correlation coefficient r is one of the following. (a) near –1 strong

positive linear correlation weak positive linear correlation strong negative linear correlation weak negative linear correlation weak or no linear correlation (b) near 0 strong negative linear correlation strong positive linear correlation weak negative linear correlation weak positive linear correlation weak or no linear correlation (c) near 1 weak positive linear correlation weak negative linear correlation weak or no linear correlation strong negative linear correlation strong positive linear correlation
Mathematics
1 answer:
zepelin [54]3 years ago
3 0

Answer:

a) strong negative linear correlation.

b) Weak or no linear correlation.

c) strong positive linear correlation.

Step-by-step explanation:

The correlation coefficient r measures the strength and direction (positive or negative) of two variables. The correlation coefficient r is always between -1 and 1. When the coefficient r is negative then the direction of the correlation is downhill (negative) and when it's positive then it's an uphill correlation (positive). Similarly, as the coefficient is closer to -1 or 1 the correlation is stronger, with zero being a non linear relationship.

Now back to the question:

a) Near -1: as we said before, this means an strong negative (-1) linear correlation.

b) Near 0: weak or no linear correlation (we cannot say if its positive or negative because we don't know it it's near zero from the right (positive numbers) or the left (negative numbers)

c) Near 1: strong positive (close to +1) linear correlation

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A sociologist wants to determine if the life expectancy of people in africa is less than the life expectancy of people in asia.
Colt1911 [192]

Answer:

The sample statistics are attached.

First, we need to determine the hypothesis.

The null hypothesis should be: H_{o} : u_{africa} \geq u_{asia}.

The alternative hypothesis should be: H_{a}: u_{africa}

The next step is about calculating the<em> t statistics </em>based on the samples. This <em>t test </em>will give the probability value or p-value, which determines if there's enough to reject the null hypothesis. The formula we need to use to find the t-value is: t=\frac{x_{1}-x_{2}  }{\sqrt{\frac{s_{1} ^{2} }{n_{1} } +\frac{s_{2} ^{2} }{n_{2} } } }

Replacing all the values into the formula, we have:

t=\frac{52.4-58.1}{\sqrt{\frac{(7.4)^{2} }{45}+\frac{(8.8)^{2} }{35}  } } \\t=\frac{-5.7}{\sqrt{1.2+2.2} } =\frac{-5.7}{1.8} =-3.2

Therefore, the t-value is -3.2.

Now, we using a level of significance of 0.01, and a degree of freedom (df) of 34, we use the t-table to find the p-value for this results. (df = N -1; in this case, we take the smaller sample, which is 35, giving us 34 of df).

Therefore, according to the table attached, <em>the p-value is less than 0.01,</em> which is less than our level of significance. This result means that the null hypothesis is rejected. In other words, there's enough evidence to say that <em>the life expectancy of people in Africa is less than the life of expectancy of people in Asia.</em>

<em></em>

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3 years ago
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