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Oxana [17]
3 years ago
11

Order the following from greatest to least 1/4, 0.33, 1/8, 0

Mathematics
2 answers:
SSSSS [86.1K]3 years ago
8 0

Order from greatest to least is 0.33, 1/4, 1/8, 0

<u>Solution:</u>

Give that numbers are 1/4, 0.33, 1/8, 0

We have to arrange them from greatest to least which means descending order

As we can see that, numbers are in different forms, so let us convert them all into decimal forms first.

\frac{1}{4}=0.25

\frac{1}{8}=0.125

Now the given set of numbers are 0.25, 0.33, 0.125, 0

By comparing we get,

0.33 > 0.25 > 0.125 > 0

So, the order of numbers in given forms is 0.33, 1/4, 1/8, 0

schepotkina [342]3 years ago
4 0

Answer: 0.33 , 1/4 , 1/8 , 0

Step-by-step explanation:

1/4 is 0.25

1/8 is 0.125

After converting to decimal it’s all easy

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Answer:

\chi^2 = \frac{(32-42)^2}{42}+\frac{(18-8)^2}{8}+\frac{(68-63)^2}{63}+\frac{(7-12)^2}{12}+\frac{(89-84)^2}{84}+\frac{(11-16)^2}{16}=19.221

Now we can calculate the degrees of freedom for the statistic given by:

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Since the p values is higher than a significance level for example \alpha=0.05, we can reject the null hypothesis at 5% of significance, and we can conclude that the two variables are dependent at 5% of significance.

Step-by-step explanation:

Previous concepts

A chi-square goodness of fit test "determines if a sample data matches a population".

A chi-square test for independence "compares two variables in a contingency table to see if they are related. In a more general sense, it tests to see whether distributions of categorical variables differ from each another".

Solution to the problem

Assume the following dataset:

Size Company/ Heal. Ins.   Yes   No  Total

Small                                      32   18    50

Medium                                 68     7    75

Large                                     89    11    100

_____________________________________

Total                                     189    36   225

We need to conduct a chi square test in order to check the following hypothesis:

H0: independence between heath insurance coverage and size of the company

H1:  NO independence between heath insurance coverage and size of the company

The statistic to check the hypothesis is given by:

\sum_{i=1}^n \frac{(O_i -E_i)^2}{E_i}

The table given represent the observed values, we just need to calculate the expected values with the following formula E_i = \frac{total col * total row}{grand total}

And the calculations are given by:

E_{1} =\frac{50*189}{225}=42

E_{2} =\frac{50*36}{225}=8

E_{3} =\frac{75*189}{225}=63

E_{4} =\frac{75*36}{225}=12

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E_{6} =\frac{100*36}{225}=16

And the expected values are given by:

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Small                                      42    8    50

Medium                                 63     12    75

Large                                     84    16    100

_____________________________________

Total                                     189    36   225

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\chi^2 = \frac{(32-42)^2}{42}+\frac{(18-8)^2}{8}+\frac{(68-63)^2}{63}+\frac{(7-12)^2}{12}+\frac{(89-84)^2}{84}+\frac{(11-16)^2}{16}=19.221

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df=(rows-1)(cols-1)=(3-1)(2-1)=2

And we can calculate the p value given by:

p_v = P(\chi^2_{2} >19.221)=0.000067

And we can find the p value using the following excel code:

"=1-CHISQ.DIST(19.221,2,TRUE)"

Since the p values is higher than a significance level for example \alpha=0.05, we can reject the null hypothesis at 5% of significance, and we can conclude that the two variables are dependent at 5% of significance.

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