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AnnZ [28]
3 years ago
12

The experimental probability that Diana will win a game is 3/8. If she plays the game 80 times, approximately how many times sho

uld she expect to win?
Mathematics
1 answer:
castortr0y [4]3 years ago
5 0

Answer:

She should win approximately 30 times.

Step-by-step explanation:

Since the probability of winning is 3/8, we multiply 3/8 with 80 to get 30.

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6 0
3 years ago
Read 2 more answers
Solve equation show all steps what 2x-3x+5=18
PSYCHO15rus [73]

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Let's solve your equation step-by-step.

2x-3x+5=18

Step 1: Simplify both sides of the equation.

2x-3x+5=18\\2x + -3x + 5 = 18

( 2x + -3x ) + ( 5) = 18 (Combine Like Terms)

-x + 5 = 18\\-x + 5 = 18

Step 2: Subtract 5 from both sides.

-x + 5 - 5 = 18 - 5 \\-x = 13

Step 3: Divide both sides by -1.

\frac{-x}{-1} = \frac{13}{-1} \\x = -13

Answer : \boxed {x = -13}

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Have a great day/night!

❀*May*❀

3 0
3 years ago
Read 2 more answers
Evelyn has $524.96 in her checking account. She must maintain a $500 balance to avoid a fee. She wrote a check for $32.50 today.
cupoosta [38]

A linear inequality to represent the algebraic expression is given as 492.46 - x ≥ 500

<h3>Linear Inequality</h3>

Linear inequalities are inequalities that involve at least one linear algebraic expression, that is, a polynomial of degree 1 is compared with another algebraic expression of degree less than or equal to 1.

In this problem, her minimum balance must not decrease beyond $500 or she will pay a fee.

where

  • x = withdrawals

The inequality to represent this can be written as

524.96 - 32.50 - x ≥ 500

Simplifying this;

492.46 - x ≥ 500

The linear inequality is 492.46 - x ≥ 500

Learn more on linear inequality here;

brainly.com/question/23093488

#SPJ1

4 0
1 year ago
Some scientists believe alcoholism is linked to social isolation. One measure of social isolation is marital status. A study of
frez [133]

Answer:

1) H0: There is independence between the marital status and the diagnostic of alcoholic

H1: There is association between the marital status and the diagnostic of alcoholic

2) The statistic to check the hypothesis is given by:

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

3) \chi^2 = \frac{(21-33.143)^2}{33.143}+\frac{(37-41.429)^2}{41.429}+\frac{(58-41.429)^2}{41.429}+\frac{(59-46.857)^2}{46.857}+\frac{(63-58.571)^2}{58.571}+\frac{(42-58.571)^2}{58.571} =19.72

4) 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} >19.72)=5.22x10^{-5}

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

"=1-CHISQ.DIST(19.72,2,TRUE)"

Since the p value is lower than the significance level so then we can reject the null hypothesis at 5% of significance, and we can conclude that we have association between the two variables analyzed.

Step-by-step explanation:

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".

Assume the following dataset:

                    Diag. Alcoholic   Undiagnosed Alcoholic    Not alcoholic    Total

Married                     21                              37                            58                116

Not Married              59                             63                            42                164

Total                          80                             100                          100              280

Part 1

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

H0: There is independence between the marital status and the diagnostic of alcoholic

H1: There is association between the marital status and the diagnostic of alcoholic

The level os significance assumed for this case is \alpha=0.05

Part 2

The statistic to check the hypothesis is given by:

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

Part 3

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{80*116}{280}=33.143

E_{2} =\frac{100*116}{280}=41.429

E_{3} =\frac{100*116}{280}=41.429

E_{4} =\frac{80*164}{280}=46.857

E_{5} =\frac{100*164}{280}=58.571

E_{6} =\frac{100*164}{280}=58.571

And the expected values are given by:

                    Diag. Alcoholic   Undiagnosed Alcoholic    Not alcoholic    Total

Married             33.143                       41.429                        41.429                116

Not Married     46.857                      58.571                        58.571                164

Total                   80                              100                             100                 280

And now we can calculate the statistic:

\chi^2 = \frac{(21-33.143)^2}{33.143}+\frac{(37-41.429)^2}{41.429}+\frac{(58-41.429)^2}{41.429}+\frac{(59-46.857)^2}{46.857}+\frac{(63-58.571)^2}{58.571}+\frac{(42-58.571)^2}{58.571} =19.72

Part 4

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} >19.72)=5.22x10^{-5}

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

"=1-CHISQ.DIST(19.72,2,TRUE)"

Since the p value is lower than the significance level so then we can reject the null hypothesis at 5% of significance, and we can conclude that we have association between the two variables analyzed.

7 0
3 years ago
The cumulative distribution function f(x) of a discrete random variable x is given by f(0) =. 30, f(1) =. 70, f(2) =. 90 and f(3
Lisa [10]

Correlation between x & y is 0.6125.

In probability theory and statistics, the cumulative distribution function of a real-valued random variable X, or simply the distribution function of X weighted by x, is the probability that X takes a value less than or equal to x.

The cumulative distribution function (CDF) of a random variable X is defined as FX(x)=P(X≤x) for all x∈R. Note that the subscript X indicates that this is the CDF of the random variable X. Also note that the CDF is defined for all x∈R. Let's look at an example.

Learn more about cumulative distribution here: brainly.com/question/24756209

#SPJ4

7 0
1 year ago
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