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andrew-mc [135]
3 years ago
6

$18.48 rounded to nearest dollar?

Mathematics
2 answers:
Minchanka [31]3 years ago
7 0
$18.00 would be $18.48 rounded to the nearest Dollar.
ANEK [815]3 years ago
6 0
$18 is the rounded dollar to $18.48
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68% of the tickets sold at a water park were child tickets. If the park sold 50 tickets in all, how many child tickets did it se
Mnenie [13.5K]

Answer:

\boxed{\bf\: The  \: park  \: sold  \boxed{ 34{}} \: child  \: tickets.}

Step-by-step explanation:

<u>Given:</u>

Percentage of Tickets sold at a water park - 68%

The Number of Tickets if the park sold in all - 50

<u>To </u><u>Find:</u>

The Number of child tickets the park sold.

<u>Solution:</u>

We know that 68% equals to 68/100.

Step 1: Multiply 68/100 by 50 which is the number of tickets the park sold :-

\sf \:  =  \:  \cfrac{68}{100}  \times 50

<u>Note</u>:The Simplified form of 68/100 * 50 would be the answer to this question.

Step 2: <u>Cancel One zero of 50 and one zero of 100,That is</u>:-

\sf =  \cfrac{68}{ 10\cancel0}  \times 5 \cancel0

<em>Results to,</em>

\sf  = \cfrac{68}{10}  \times 5

Step 3: <u>Cancel 5 and 10, That is</u>:-

\sf  = \cfrac{68}{ \cancel{10 }}  \times  \cancel5

<em>Results to,</em>

\sf =\:  \cfrac{68}{2}  \times 1

\sf = \cfrac{68}{2}

Step 4: <u>Cancel 68 and 2, That is</u>:-

\sf =\:  \cfrac{ \cancel{68}}{ \cancel2}

<em>Results to,</em>

\sf  =  \cfrac{34}{1}

\sf  = 34

34 is the result.

Hence,

\underline{ \rm \: The  \: park  \: sold  \: \bold{ 34 } \: child  \: tickets.}

________________________________

I hope this helps!

Let me know if you have any questions.I am joyous to help!

6 0
3 years ago
There is strong believe that language skills of Political science students are greater than students who study Finance. Research
BaLLatris [955]

Answer:

a

The null hypothesis is  H_o :  \mu_1 = \mu_2

The alternative hypothesis  H_a : \mu_1 >  \mu_2

b

p-value   = 0.232

c

    The decision rule is  

Fail to reject the null hypothesis

Step-by-step explanation:

From the question we are told that

  The value given is  

S/N

1       7 5

2      4 3

3      8 7

4      8 8

5      7 9

6      7 5

7      6 5

Generally the sample mean for the first sample  is  mathematically represented as

        \= x _1 = \frac{\sum x_i }{n}

=>     \= x _1 = \frac{7 +4 + \cdots + 6}{7}

=>     \= x _1 =  6.714

Generally the sample mean for the second sample is mathematically represented as

        \= x _2 = \frac{\sum x_i }{n}

=>     \= x _2 = \frac{5 + 3+ \cdots + 5}{7}

=>     \= x _2 =  6

Generally the sample standard deviation  for the first sample  is  mathematically represented as

           s_1 = \sqrt{\frac{\sum (x_i - \= x_1)^2 }{n-1 } }

=>       s_1 = \sqrt{\frac{ (7 - 6.714 )^2 +(4 - 6.714 )^2 + \cdots + (6 - 6.714 )^2 }{7-1 } }

=>       s_1 = 1.905

Generally the sample standard deviation  for the second  sample  is  mathematically represented as

           s_2 = \sqrt{\frac{\sum (x_i - \= x_2)^2 }{n-1 } }

=>       s_2 = \sqrt{\frac{ (5 - 6.714 )^2 +(3 - 6.714 )^2 + \cdots + (5 - 6.714 )^2 }{7-1 } }

=>       s_1 = 4.33

Generally the pooled standard deviation is  

      s = \sqrt{\frac{(n_1 - 1 )s_1^2 + (n_2 - 1 )s_2^2}{n_1 + n_2 -2 } }

=>    s = \sqrt{\frac{(7 - 1 )1.905^2 + (7 - 1 )4.333^2}{7 + 7 -2 } }

=>    s = 1.766

The null hypothesis is  H_o :  \mu_1 = \mu_2

The alternative hypothesis  H_a : \mu_1 >  \mu_2

Generally the test statistics is mathematically represented as

      t = \frac{\= x _1 - \= x_2 }{s * \sqrt{\frac{1}{n_1} + \frac{1}{n_2}}  }

=>   t = \frac{6.714  - 6 }{1.766  * \sqrt{\frac{1}{7} + \frac{1}{7}}  }

=>   t = 0.757

Generally the degree of freedom is mathematically represented as

     df = n_1 + n_2 - 2

=>   df = 7 + 7 - 2

=>   df = 12

From the t distribution table the probability of  t = 0.757 at a degree of freedom of df = 12 is  

     t_{ 0.757 , 12} = 0.232

Generally the p-value is  

      p-value  = t_{ 0.757 , 12} = 0.232

From the values  obtained we see that p-value >  \alpha  hence  

   The decision rule is  

Fail to reject the null hypothesis

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