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Georgia [21]
3 years ago
15

Use the difference of two squares identity to factor each of the following expressions. (a) x²-81 (b) (3x+y)² -(2y)² (c) 4-(x-1)

² (d) (x+2)²-(y-2)²
Mathematics
1 answer:
ioda3 years ago
4 0

Answer:

a) x^2-9^2=(x+9)(x-9)

b) (3x+y)^2-(2y)^2=(3x+3y)(3x-y)

c) 2^2-(x-1)^2=(1+x)(3-x)

d) (x+2)^2-(y-2)^2=(x+y)(x-y+4)

Explanation:

Factor the expression using difference of two squares.

Formula:

a^2-b^2=(a+b)(a-b)

Part a)  x²-81

write 81 as perfect square.

x^2-9^2

a\rightarrow x

b\rightarrow 9

x^2-9^2=(x+9)(x-9)

Part b) (3x+y)² -(2y)²

a\rightarrow 3x+y

b\rightarrow 2y

(3x+y)^2-(2y)^2=(3x+y+2y)(3x+y-2y)

(3x+y)^2-(2y)^2=(3x+3y)(3x-y)

Part c) 4-(x-1)²

write 4 as perfect square,

2^2-(x-1)^2

a\rightarrow 2

b\rightarrow x-1

2^2-(x-1)^2=(2+x-1)(2-x+1)

2^2-(x-1)^2=(1+x)(3-x)

Part d) (x+2)²-(y-2)²

a\rightarrow x+2

b\rightarrow y-2

(x+2)^2-(y-2)^2=(x+2+y-2)(x+2-y+2)

(x+2)^2-(y-2)^2=(x+y)(x-y+4)

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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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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:

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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

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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= 6x² -2x -8 -(x² -9x +18)

= 6x² -2x -8 -x² +9x -18

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