What is the mode of the data

Mathematics

1 answer:

Setler79 [48]4 years ago

8 0

Answer: 5

Step-by-step explanation: The number that occurs the most in a data set is the mode. In this picture the mode is 5 because 5 occurs the most. It occurs 6 times.

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At the beginning of the day the stock market goes up 30 1/2 points. At the end of the day, the stock market goes down 100 3/4 po

Akimi4 [234]

Well, is namely their difference, so, let's first convert the mixed fractions to "improper", and subtract.

$\bf \stackrel{mixed}{30\frac{1}{2}}\implies \cfrac{30\cdot 2+1}{2}\implies \stackrel{improper}{\cfrac{61}{2}}
\\\\\\
\stackrel{mixed}{100\frac{3}{4}}\implies \cfrac{100\cdot 4+3}{4}\implies \stackrel{improper}{\cfrac{403}{4}}\\\\
-------------------------------\\\\
\cfrac{61}{2}-\cfrac{403}{4}\impliedby \textit{so our \underline{LCD is 4}}\implies \cfrac{(2\cdot 61)~-~(1\cdot 403)}{4}
\\\\\\
\cfrac{122-403}{4}\implies \cfrac{-281}{4}\implies -70\frac{1}{4}$

5 0

3 years ago

100000 less than 560313 is

arsen [322]

100000
-560313

460313

3 0

3 years ago

CNNBC recently reported that the mean annual cost of auto insurance is 965 dollars. Assume the standard deviation is 113 dollars

velikii [3]

Answer:

P(939.6 < X < 972.5) = 0.6469

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 $\mu$ 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 $s = \frac{\sigma}{\sqrt{n}}$ .