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Ronch [10]
3 years ago
14

4. Add the proper constant to the binomial so that the resulting trinomial is a perfect square trinomial. Then factor the

Mathematics
1 answer:
lora16 [44]3 years ago
6 0

Answer:

The constant is 289/4

The factored form is (x + 17/2)²

Step-by-step explanation:

Perfect square trinomials are in standard form ax² + bx + c; when the square root of "ax²", multiplied by the square root of "c", multiplied by 2, is equal to "bx". In equation form:

2√(ax²)√c = bx

In x² + 17x + c:

ax² = x²

bx = 17x

c = the constant term we are finding

Substitute what we know into the equation, then find c.

2√(ax²)√c = bx

2√(x²)√c = 17x        Simplify √(x²) = x because √ and ² cancel out

2x√c = 17x

√c = 17x / 2x         Divide both sides by 2x

√c = 17/2            Keep as a fraction to get the exact value of "c"

c = (17/2)²         Square both sides and simplify

c = 289/4

Therefore the constant term is 289/4.

To factor a perfect trinomial, use the form:

ax² + bx + c = ( √(ax²) ± √c )²

Take the square root of the first term, plus/minus the square root of the last term, then square the entire equation.

Whether the middle sign is plus (+) or minus (-) depends on if bx is positive or negative.

The perfect trinomial is x² + 17x +  289/4 after having substituted "c".

Square root the first term, plus square root the last term and all squared:

( √(x²) + √(289/4) )²   Simplify. Find the square root of the numbers.

= (x + 17/2)²

The factored form is (x + 17/2)².

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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:

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

And we can calculate the p value given by:

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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.

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

E_{5} =\frac{100*189}{225}=84

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

And the expected values are given by:

Size Company/ Heal. Ins.   Yes   No  Total

Small                                      42    8    50

Medium                                 63     12    75

Large                                     84    16    100

_____________________________________

Total                                     189    36   225

And now we can calculate the statistic:

\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:

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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