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timama [110]
3 years ago
14

Lisa makes $5 per hour babysitting and $12 per hour giving music lessons. One weekend, she worked a total of 18 hours and made $

139. How many hours did she spend babysitting that weekend?
Mathematics
2 answers:
Alona [7]3 years ago
8 0

Answer:

11 Hours

Step-by-step explanation:

Let's say Lisa makes x hours babysitting and y hours giving music lessons.

Because she worked a total of 18 hours, we know that x + y = 18.

5 * x is how much money she earns babysitting for x hours, while 12 * y is how much money she earns giving music lessons for y hours. Adding them up gives her total revenue:

5x + 12y = 139

We now have a system of equations. Let's multiply both sides of x + y = 18 by 12:

12 * (x + y) = 18 * 12

12x + 12y = 216

Now, subtract 5x + 12y = 139 from 12x + 12y = 216:

    12x + 12y = 216

-     5x + 12y = 139

_________________

      7x + 0y = 77

Divide both sides by 7:

x = 11

Thus, Lisa spent 11 hours babysitting.

Valentin [98]3 years ago
5 0

Answer:

11 hours baby sitting and 7 hours music lessons

Step-by-step explanation:

you have to solve:

x + y = 18

and

5x + 12y= 139

assuming that x is the total hours of baby sitting and y is the total hours of music lessons

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Answer:

Null hypothesis:p_{1} = p_{2}  

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

z=\frac{0.6-0.2}{\sqrt{0.4(1-0.4)(\frac{1}{50}+\frac{1}{50})}}=4.082    

p_v =2*P(Z>4.082)=4.46x10^{-5}  

So the p value is a very low value and using any significance level for example \alpha=0.05 always p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can say the the proportion 1 is significantly different from proportion 2.

Step-by-step explanation:

1) Data given and notation  

X_{1}=30 represent the number of people with a characteristic in 1

X_{2}=10 represent the number of people with a characteristic in 2

n_{1}=50 sample of 1 selected  

n_{2}=50 sample of 2 selected  

p_{1}=\frac{30}{50}=0.6 represent the proportion of people with a characteristic in 1

p_{2}=\frac{10}{50}=0.2 represent the proportion of people with a characteristic in 2

z would represent the statistic (variable of interest)  

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

2) Concepts and formulas to use  

We need to conduct a hypothesis in order to check if the proportion 1 is different from proportion 2 , 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{30+10}{50+50}=0.4  

3) Calculate the statistic  

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

z=\frac{0.6-0.2}{\sqrt{0.4(1-0.4)(\frac{1}{50}+\frac{1}{50})}}=4.082    

4) 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>4.082)=4.46x10^{-5}  

So the p value is a very low value and using any significance level for example \alpha=0.05 always p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can say the the proportion 1 is significantly different from proportion 2.  

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