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vlabodo [156]
2 years ago
14

What is something that will appreciate in value? What is something that will depreciate in value?

Mathematics
1 answer:
Tatiana [17]2 years ago
8 0

Answer:

A positive slope will appreciate in value. A negative slope will depreciate in value.

Step-by-step explanation:

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PLEASE HELP!<br><br> 36&lt;8p+4&lt;44<br> The solution set is__.
Nataliya [291]
4<p<5
(4,5) 
Open circles, not shaded. 
Hope this helped!
5 0
3 years ago
Experian would like to test the hypothesis that the average credit score for an adult in Virginia is different from the average
aliya0001 [1]

Answer:

a. We fail to reject the hypothesis that the average credit score for an adult in Virginia is different from the average credit score for an adult in North Carolina at the significance level of 0.05.

b. The 95% confidence interval for the true difference of means is -2.2468 and 36.2468. There is a probability of 95% that the true difference of means \mu_{1}-\mu_{2} is between -2.2468 and 36.2468. This confidence interval contains the number 0, this is consistent with the results we got in a. Because we fail to reject the hypothesis that the average credit score for an adult in Virginia is different from the average credit score for an adult in North Carolina at the significance level of 0.05.

Step-by-step explanation:

Let \mu_{1}-\mu_{2} be the true difference between the average credit score for an adult in Virginia and the average credit score for an adult in North Carolina. We have the large sample sizes n_{1} = 40 and n_{2} = 35, the unbiased point estimate for \mu_{1}-\mu_{2} is \bar{x}_{1} - \bar{x}_{2}, i.e., 699-682 = 17.

The standard error is given by \sqrt{\frac{\sigma^{2}_{1}}{n_{1}}+\frac{\sigma^{2}_{2}}{n_{2}}}, i.e.,

\sqrt{\frac{(44)^{2}}{40}+\frac{(41)^{2}}{35}} = 9.8198.

a. We want to test H_{0}: \mu_{1}-\mu_{2} = 0 vs H_{1}: \mu_{1}-\mu_{2} \neq 0 (two-tailed alternative). The rejection region is given by RR = {z | z < -1.96 or z > 1.96} where -1.96 and 1.96 are the 2.5th and 97.5th quantiles of the standard normal distribution respectively. The test statistic is Z = \frac{\bar{x}_{1} - \bar{x}_{2}-0}{\sqrt{\frac{\sigma^{2}_{1}}{n_{1}}+\frac{\sigma^{2}_{2}}{n_{2}}}} and the observed value is z_{0} = \frac{17}{9.8198} = 1.7312. Because 1.7312 does not fall inside RR, we fail to reject the null hypothesis.

b. The endpoints for a 95% confidence interval for \mu_{1}-\mu_{2} is given by 17\pm (z_{0.05/2})9.8198, i.e., 17\pm (z_{0.025})9.8198 where z_{0.025} is the 2.5th quantile of the standard normal distribution, i.e., -1.96, so, we have 17-(1.96)(9.8198) and 17+(1.96)(9.8198), i.e., -2.2468 and 36.2468. There is a probability of 95% that the true difference of means \mu_{1}-\mu_{2} is between -2.2468 and 36.2468. This confidence interval contains the number 0, this is consistent with the results we got in a. Because we fail to reject the hypothesis that the average credit score for an adult in Virginia is different from the average credit score for an adult in North Carolina at the significance level of 0.05.

3 0
3 years ago
Find the factor pairs of 80? Please help me :,(
koban [17]
1,80
2,40
4,20
5,16
8,10
3 0
3 years ago
Read 2 more answers
Round 2.408 to the nearest hundredth
Verdich [7]
8 in 2.408 is closer to 10, so you round 2.408 to 2.41
7 0
3 years ago
Read 2 more answers
2296.0 m= ____________ km in metric conversions
Andreyy89

Answer:

2.296 km

Step-by-step explanation:

The "meters to kilometers" conversion factor is:

 1 km

-------------

1000 m

Multiply 2296.0 m by this factor:

 1 km           2296.0 m

------------- * ------------------- = 2.296 km

1000 m               1

3 0
3 years ago
Read 2 more answers
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