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rewona [7]
3 years ago
9

One hundred draws are made at random with replacement from box A, and 250 are made at random with replacement from box B. (a) 50

of the draws from box A are positive, compared to 131 from box B: 50.0% versus 52.4%. Is this difference real, or due to chance
Mathematics
1 answer:
Yuliya22 [10]3 years ago
3 0

Answer:

Therefore WE accept the Null hypothesis H_0 That 50.0% versus 52.4%.difference is real

Step-by-step explanation:

Sample size A n_a=100

Sample size B n_b=250

Positive draw from box A n_a=50

Positive draw from box b n_b=131

Generally the equation for Probability of Positive draw from Box A is mathematically given by

 P_1=\frac{50}{100}

 P_1=50%

Therefore

 1-P_1=50\%

Generally the equation for Probability of Positive draw from Box A is mathematically given by

 P_1=\frac{131}{250}

 P_1=52.4%

Therefore

 1-P_1=47.6\%

Generally the equation for Standard error S.E is mathematically given by

 S.E=\sqrt{\frac{n_1(p_1)*(1-P_1)}{n_1^2}+\frac{n_2(P_2(1-p^2))}{n_2^2}}

 S.E=\sqrt{\frac{100(0.50)*(50)}{100^2}+\frac{(250)(0.524(47))}{250^2}}

 S.E=0.592

 S.E=59.2\%

Therefore

 Z=\frac{50-52.4}{59.2}

Generally

 P value P>0.05

Therefore WE accept the Null hypothesis H_0 That 50.0% versus 52.4%.difference is real

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Answer:

Right or positively skewed

Step-by-step explanation:

From the median and the interquartile range we can find the first and third quartile values Q3=40.15+(4.98/2)=42.64 and Q1=37.66.

Now we have enough evidence to conclude that is Right or positively skewed

1. The mean is above the median

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The evidence suggests that most of the data is concentrated at the bottom of the distribution making it right or positively skewed.

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Answer:

40

Step-by-step explanation:

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32 + 8 = 40

Could really use brainliest if thif helps

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-2j < -14

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Granola Crunch cereal is packaged in 1 pound boxes. Susan Torres, a quality control analyst who works for the manufacturer of th
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Answer:

z=\frac{1.02-1}{\frac{0.04}{\sqrt{40}}}=3.162    

p_v =2*P(z>3.162)=0.0016    

If we compare the p value and the significance level given \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to to reject the null hypothesis, so we can conclude that the true mean is different from 1 pound at 5% of signficance.

Step-by-step explanation:

Data given and notation    

\bar X=1.02 represent the sample mean

\sigma=0.04 represent the population standard deviation    

n=40 sample size    

\mu_o =1 represent the value that we want to test  

\alpha=0.05 represent the significance level for the hypothesis test.    

z would represent the statistic (variable of interest)    

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

State the null and alternative hypotheses.    

We need to conduct a hypothesis in order to check if the true mean is equal to 1 pound or no :    

Null hypothesis:\mu =1    

Alternative hypothesis:\mu \neq 1    

Since we know the population deviation, is better apply a z test to compare the actual mean to the reference value, and the statistic is given by:    

z=\frac{\bar X-\mu_o}{\frac{\sigma}{\sqrt{n}}} (1)    

z-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".    

Calculate the statistic    

We can replace in formula (1) the info given like this:    

z=\frac{1.02-1}{\frac{0.04}{\sqrt{40}}}=3.162    

P-value    

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

p_v =2*P(z>3.162)=0.0016    

Conclusion    

If we compare the p value and the significance level given \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to to reject the null hypothesis, so we can conclude that the true mean is different from 1 pound at 5% of signficance.    

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