If you can see, the 4 in the thousands place is bigger than the tens place. It is pretty obvious. Thousand is bigger than ten, right? If you had ten shirts and thousand shirts, it would be a big difference. Start from the right.
Ones, Tens, Hundred, Thousands, Ten thousands, Hundred thousand, Millions, Ten millions, Hundred millions, Billion, Ten billion, Hundred billion, Trillion, Ten trillion, Hundred trillion, Quadrillion, Ten quadrillion, Hundred quadrillion, Quintillion, ten quintillion, hundred quintillion, sextillions, ten sextillions, hundred sextillions, octillions,ten octillions, hundred octillions, etc...
Using the conversion of exponent to power, the equivalent expression is given as follows:
![\frac{\sqrt{x}}{\sqrt[4]{y}z^2}](https://tex.z-dn.net/?f=%5Cfrac%7B%5Csqrt%7Bx%7D%7D%7B%5Csqrt%5B4%5D%7By%7Dz%5E2%7D)
<h3>How to transform an exponent to a power?</h3>
It happens according to the following rule, with the denominator as the root and the numerator as the exponent:
![a^{\frac{n}{m}} = \sqrt[m]{a^n}](https://tex.z-dn.net/?f=a%5E%7B%5Cfrac%7Bn%7D%7Bm%7D%7D%20%3D%20%5Csqrt%5Bm%5D%7Ba%5En%7D)
In this problem, we are given the following expression:
Negative exponents go to the denominator, hence the <em>equivalent expression</em> is given by:
More can be learned about the conversion of exponent to power at brainly.com/question/2020414
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As a fraction it would be 175/100, as a mixed number it would be 1 75/100, simplified it would be 1 3/4
Answer:
Step-by-step explanation:
Hello!
The variable of interest is
X: mark obtained in an aptitude test by a candidate.
This variable has a mean μ= 128.5 and standard deviation σ= 8.2
You have the data of three scores extracted from the pool of aptitude tests taken.
148, 102, 152
The average is calculated as X[bar]= Σx/n= (148+102+152)/3= 134
An outlier is an observation that is significantly distant from the rest of the data set. They usually represent experimental errors (such as a measurement) or atypical observations. Some statistical measurements, such as the sample mean, are severely affected by this type of values and their presence tends to cause misleading results on a statistical analysis.
Using the mean and the standard deviation, an outlier is any value that is three standard deviations away from the mean: μ±3σ
Using the population values you can calculate the limits that classify an observed value as outlier:
μ±3σ
128.5±3*8.2
(103.9; 153.1)
This means that any value below 103.9 and above 153.1 can be considered an outlier.
For this example, there is only one outlier, that this the extracted score 102
I hope this helps!
