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Susan is buying three different colors of tiles for her kitchen floor. She is buying 25 more red tiles than beige tiles, and thr

Vanyuwa [196]

Let us assume that the number of beige tiles bought by Susan = B
Number of red tiles bought by Susan = R
Number of navy-blue tiles bought by Susan =N
Number of tiles bought altogether = 435
Now from the given question we know:
Number of red tiles bought is 25 more than the number of beige tiles.
So
R = 25 + B
Number of navy blue tiles bought is 3 times that of the number of beige tile bought
So
N = 3B
We already know that the total number of tiles bought is 435
Hence
B + R + N = 435
B + (25 + B) + 3B = 435
5B + 25 = 435
5B = 435 -25
5B = 410
B = 82
R = 25 + B
   = 25 + 82
   = 107
N = 3B
   = 3 * 82
   = 246
So the number of Beige tiles bought by Susan = 82
The number of red tiles bought by Susan = 107
The number of navy-blue tiles bought by Susan = 246

5 0

3 years ago

Please help and show work if you can

laila [671]

Answer:

25. a = 60

26. a = 17

Step-by-step explanation:

In these two problems we are going to use a property of parallelograms that says: opposite angles have equal measure

so we have

25.

$6a+10=130\\\\6a=130-10\\\\6a=120\\\\a=60$

and for 26 we have

$4a-4=2a+30\\\\4a=2a+30+4\\\\4a=2a+34\\\\4a-2a=34\\\\2a=34\\\\a=17$

6 0

3 years ago

The average score of all golfers for a particular course has a mean of 75 and a standard deviation of 4. Suppose 64 golfers play

Vilka [71]

Answer:

0.0228 = 2.28% probability that the average score of the 64 golfers exceeded 76.

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 normally distributed samples are solved using 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 $s = \frac{\sigma}{\sqrt{n}}$ .