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lana [24]
2 years ago
15

The slope of the trend line is 15. What does that mean in regard to the data of the scatterplot? Check all that apply. The slope

represents the rate of change of the data. Advertising costs increase $15,000 as sales increase by $1,000. Sales increase $15,000 as ads increase by $1,000. A positive slope infers a negative correlation. A positive slope infers a positive correlation.'

Mathematics
2 answers:
Dvinal [7]2 years ago
7 0
Without the graph, we can't be 100% certain. However, it makes sense that the sales increase by $15,000 as the ads increase by $1000. It is always the change in the vertical axis over the change in the horizontal axis.

A positive slope is always a positive correlation.
tigry1 [53]2 years ago
7 0

1. The slope represents the rate of change of the data.

3. Sales increase $15,000 as ads increase by $1,000.

5. A positive slope infers a positive correlation.

<u>*These are all correct.</u>

Brianna bts
2 years ago
edge awnser thxs
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If the complex number x = 3 + bi and |x|2 = 13, which is a possible value of b?
Naddika [18.5K]

Answer:

A: 2

Step-by-step explanation:

EDGE 2021

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2 years ago
Trent's mom grave him $30. He earned another $12 completing chores. Trent spent $15 at the movie theater and $6 on lunch. How mu
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In the book Essentials of Marketing Research, William R. Dillon, Thomas J. Madden, and Neil H. Firtle discuss a research proposa
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Answer:

Null hypothesis:p_{1} = p_{2}  

Alternative hypothesis:p_{1} \neq p_{2}  

z=\frac{0.179-0.15}{\sqrt{0.17(1-0.17)(\frac{1}{140}+\frac{1}{60})}}=0.500  

p_v =2*P(Z>0.500)=0.617  

So the p value is a very low value and using any significance level for example \alpha=0.05, 0,1,0.15 always p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can say the two proportions NOT differs significantly.  

Step-by-step explanation:

Data given and notation  

X_{1}=25 represent the number of homeowners who would buy the security system

X_{2}=9 represent the number of renters who would buy the security system

n_{1}=140 sample 1

n_{2}=60 sample 2

p_{1}=\frac{25}{140}=0.179 represent the proportion of homeowners who would buy the security system

p_{2}=\frac{9}{60}= 0.15 represent the proportion of renters who would buy the security system

z would represent the statistic (variable of interest)  

p_v represent the value for the test (variable of interest)  

Concepts and formulas to use  

We need to conduct a hypothesis in order to check if the two proportions differs , the system of hypothesis would be:  

Null hypothesis:p_{1} = p_{2}  

Alternative hypothesis:p_{1} \neq p_{2}  

We need to apply a z test to compare proportions, and the statistic is given by:  

z=\frac{p_{1}-p_{2}}{\sqrt{\hat p (1-\hat p)(\frac{1}{n_{1}}+\frac{1}{n_{2}})}}   (1)  

Where \hat p=\frac{X_{1}+X_{2}}{n_{1}+n_{2}}=\frac{25+9}{140+60}=0.17  

Calculate the statistic  

Replacing in formula (1) the values obtained we got this:  

z=\frac{0.179-0.15}{\sqrt{0.17(1-0.17)(\frac{1}{140}+\frac{1}{60})}}=0.500  

Statistical decision

For this case we don't have a significance level provided \alpha, but we can calculate the p value for this test.    

Since is a two sided test the p value would be:  

p_v =2*P(Z>0.500)=0.617  

So the p value is a very low value and using any significance level for example \alpha=0.05, 0,1,0.15 always p_v>\alpha so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and we can say the two proportions NOT differs significantly.  

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Answer:

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51x + 150 < 55x + 100

(get everything on the correct sides... combine the like terms)

51x - 55x < 100 - 150

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