Ayuda es un examen determinado raíz cuadrada entera de cada número y el residuo en cada caso º60 º86 º96 Raíz:_residuo:_ raíz:__
1 answer:
Answer:
Los pasos para calcular la raíz cuadrada entera y su residuo o resta se dará en la explicación;
Step-by-step explanation:
-60: Para saber exactamente cuál es su raíz cuadrada entera debemos buscar entre que cuadrados perfectos se encuentra comprendida este valor;
Tenemos que 7x7=49 y 8x8 = 64 (60 está comprendido entre 7 y 8) ,pero como la raíz de 49 se aproxima más a 60 entonces la raíz de 60 es 7 y su residuo o resta será el valor que falta para llegar a 60; así que tenemos:
=7 ; residuo: 60-49 =11
-86: 9x9 =81 ;81 se aproxima más a 86 ,entonces la raíz cuadrada entera de 86 es 9 y su residuo es 5:
=9; residuo : 86-81= 5
-96: 9x9=81 ; 81 por tanteo se aproxima más a 91,entonces la raíz cuadrada entera de 96 es 9 y su residuo es 15.
= 9x9 =81 ; residuo : 96-81= 15.
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<h3>Stan drinks 12 ounces of water in 1 hour</h3>
<em><u>Solution:</u></em>
Given that,
Stan drinks 3/4 of a 8 ounce glass of water in 1/2 of an hour
Which means,
Therefore,
6 ounces is drank in 0.5 hour
We have to find the water he drinks in an hour
Let "x" be the water he drinks in 1 hour
Thus we get,
6 ounce = 0.5 hour
x ounces = 1 hour
This forms a proportion and we can solve the sum by cross multiplying
Thus, he drinks 12 ounces of water in 1 hour
Answer:
0.64%.
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal Probability Distribution:
Problems of normal distributions can be solved using the z-score formula.
In a set with mean and standard deviation
, the z-score of a measure X is given by:
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 p-value, 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 and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
Mean of 75 grams and a standard deviation of 22 grams.
This means that
Sample of 144:
This means that
More than 80 or less than 70:
Both are the same distance from the mean, so we find one probability and multiply by 2.
The probability that it is less than 70 is the pvalue of Z when X = 70. So
By the Central Limit Theorem
has a pvalue of 0.0032
2*0.0032 = 0.0064.
0.0064*100% = 0.64%
The probability is 0.64%.