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zavuch27 [327]
3 years ago
13

How does changing the function from f(x) = 3 sin 2x to g(x) = 3 sin 2x + 5 affect the range of the function?

Mathematics
2 answers:
Margarita [4]3 years ago
8 0

Answer:

Change in function f(x) to g(x): 5 unit up

Range: [2,8]

Step-by-step explanation:

Given:

f(x)=3\sin2x

g(x)=3\sin(2x)+5

First we have to see the change from f(x) to g(x).

g(x)=3\sin(2x)+5

g(x)=f(x)+5

f(x)=3\sin2x

If we shift f(x) 5 unit up to get g(x)

g(x)=3\sin(2x)+5

Effect: f(x) shift 5 unit up

Now we see change in range.

Range of f(x)=3\sin2x

[-3,3]

Graph shift 5 unit up.

So, Range will shift 5 unit up.

Range of g(x)=3\sin(2x)+5

[-3+5,3+5]\Rightarrow [2,8]

Hence, The range of g(x) is [2,6]

Travka [436]3 years ago
4 0
The difference between f(x) and g(x) is the +5 . Adding 5 to the end will increase all y-values obtained for the function. The range is all possible y-values of the function. The function is a wave, which moves between high and low points; +5 in the positive y direction and -5 in the negative y direction is increasing the range by 10.
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"Immediately after a ban on using hand-held cell phones while driving was implemented, compliance with the law was measured. A r
sergiy2304 [10]

Answer:

(a) Null Hypothesis, H_0 : p_1-p_2=0  or  p_1= p_2  

    Alternate Hypothesis, H_A : p_1-p_2\neq 0  or  p_1\neq p_2

(b) We conclude that there is a statistical difference in these two proportions measured initially and then one year later.

Step-by-step explanation:

We are given that a random sample of 1,250 drivers found that 98.9% were in compliance. A year after the implementation, compliance was again measured to see if compliance was the same (or not) as previously measured.

A different random sample of 1,100 drivers found 96.9% compliance."

<em />

<em>Let </em>p_1<em> = proportion of drivers that were in compliance initially</em>

p_2<em> = proportion of drivers that were in compliance one year later</em>

(a) <u>Null Hypothesis</u>, H_0 : p_1-p_2=0  or  p_1= p_2      {means that there is not any statistical difference in these two proportions measured initially and then one year later}

<u>Alternate Hypothesis</u>, H_A : p_1-p_2\neq 0  or  p_1\neq p_2     {means that there is a statistical difference in these two proportions measured initially and then one year later}

The test statistics that will be used here is <u>Two-sample z proportion statistics</u>;

                     T.S.  = \frac{(\hat p_1-\hat p_2)-(p_1-p_2)}{\sqrt{ \frac{\hat p_1(1-\hat p_1)}{n_1} + \frac{\hat p_2(1-\hat p_2)}{n_2}} }  ~ N(0,1)

where, \hat p_1 = sample proportion of drivers in compliance initially = 98.9%

\hat p_2 = sample proportion of drivers in compliance one year later = 96.9%

n_1 = sample of drivers initially = 1,250

n_2 = sample of drivers one year later = 1,100

(b) So, <u><em>the test statistics</em></u>  =  \frac{(0.989-0.969)-(0)}{\sqrt{ \frac{0.989(1-0.989)}{1,250} + \frac{0.969(1-0.969)}{1,100}} }  

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<u>Now, P-value of the test statistics is given by;</u>

         P-value = P(Z > 3.33) = 1 - P(Z \leq 3.33)

                                            = 1 - 0.99957 = <u>0.00043</u>

Since in the question we are not given with the level of significance so we assume it to be 5%. Now at 5% significance level, the z table gives critical values between -1.96 and 1.96 for two-tailed test.

<em>Since our test statistics does not lies within the range of critical values of z, so we have sufficient evidence to reject our null hypothesis as it will fall in the rejection region due to which </em><u><em>we reject our null hypothesis.</em></u>

Therefore, we conclude that there is a statistical difference in these two proportions measured initially and then one year later.

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