PLEASE HELP...........................

Miguel e nádia trabalham num escritório de contabilidade. A cada 45 minutos miguel vai á sala de café para relaxar um pouco, enq

Kazeer [188]

Responder:

3 horas

Explicação passo a passo:

Dado :

Miguel a cada 45 minutos

Nádia a cada 60 minutos

Número de horas que eles vão se ver no mesmo lugar:

Para fazer isso ;

Obtenha o menor múltiplo comum de 60 e 45

Múltiplos de:

45: 45, 90, 135, 180, 225.

60: 60, 120, 180, 240, 300.

O menor múltiplo comum de 45 e 60 é 180

° Assim, eles se verão no mesmo lugar após 180 minutos;

Número de horas = 180/60 = 3 horas

6 0

2 years ago

The head of maintenance at XYZ Rent-A-Car believes that the mean number of miles between services is 3639 3639 miles, with a var

Andrei [34K]

Answer:

96.6% probability that the mean of a sample would differ from the population mean by less than 126 miles

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

A reminder is that the standard deviation is the square root of the variance.

Normal probability distribution

When the distribution is normal, we use the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .