The answer depends on the value of the new point. If the new value is near the mean then the new standard deviation (SD) will be smaller, if it is far away, the new SD will be larger.
If each value in a data set is multiplied by a constant, the standard deviation of the resulting data set is also multiplied by that constant. In this case, since the original standard deviation is 12 points and each value is multiplied by 1.75, the new standard deviation would be 12 * 1.75 = 21 points.
Standard deviation is the spread of the data. If each score has 7 added, this would not affect the spread of the data - it would be just as evenly spaced or clumped up, but 7 greater. The only thing that would affect the spread is multiplying every data point by 0.9. This makes distances between the data points 0.9 times as big, and thus makes the standard deviation 0.9 times as big. The standard deviation was 5.6, and so now is 5.6x0.9 = 5.04
A list of daily temperatures in Phoenix, Arizona, for each day in DecemberA chart showing the total sales for each of New York's three biggest supermarketscomment And may well include analyses such as average, median, standard deviation etc.
Move the decimal point to the left or right so that it is after the first non-zero digit. Count the number of places that the decimal point has been moved.The mantissa for the standard for is the new decimal representation. The index, for the 10 is the number of places that the decimal point was moved: positive if the dp was moved to the left and negative if to the right.So, for example,34500 = 3.45*10^40.000345 = 3.45*10^-4
The New American Standard Bible is not Catholic and is missing the deuterocanonical books removed by the Protestant Reformers. The New American Bible, however, is Catholic.
It depends on the standard deviation and risk of the new stock.
It would be 3*5 = 15.
If each value in a data set is multiplied by a constant, the standard deviation of the resulting data set is also multiplied by that constant. In this case, since the original standard deviation is 12 points and each value is multiplied by 1.75, the new standard deviation would be 12 * 1.75 = 21 points.
Standard deviation is the spread of the data. If each score has 7 added, this would not affect the spread of the data - it would be just as evenly spaced or clumped up, but 7 greater. The only thing that would affect the spread is multiplying every data point by 0.9. This makes distances between the data points 0.9 times as big, and thus makes the standard deviation 0.9 times as big. The standard deviation was 5.6, and so now is 5.6x0.9 = 5.04
The mean will move up by 5 also as the whole data set has shifted up by 5, hence the mean is 105. The spread of the data has not changed, its just been "lifted up and moved along 5" and so the standard deviation is the same, i.e. 15 Hope this helps
use this link http://www.ltcconline.net/greenl/Courses/201/probdist/zScore.htm Say you start with 1000 observations from a standard normal distribution. Then the mean is 0 and the standard deviation is 1, ignoring sample error. If you multiply every observation by Beta and add Alpha, then the new results will have a mean of Alpha and a standard deviation of Beta. Or, do the reverse. Start with a normal distribution with mean Alpha and standard deviation Beta. Subtract Alpha from all observations and divide by Beta and you wind up with the standard normal distribution.
Relative Standard Deviation (RSD) is a measure of precision (not accuracy). RSD is sometimes called coefficient of variation (CV) and often is calculated as a percentage. s = standard deviation x = mean RSD = s/x, as a percentage, (s/x) *100 The RSD allows standard deviations of different measurements to be compared more meaningfully. For example, if one is measuring the concentration of two compounds A and B and the result is 0.5 (+/-) 0.4 ng/mL for compound A and 10 (+/-) 2 ng/mL for compound B, one may look at the standard deviation for compound A and say because it is lower (0.4 vs. 2) than for B, the measurement for A was more precise. Actually this is not the case. When the %RSD is used the new values for compound A and B are 0.5 (+/-) 80% and 10 (+/-) 20% respectively, therefore, the measurement for compound B is more precise.
An ordinary variable is standardised by taking a linear transformation of it such that the new variable has standard properties. For example, if X is a Gaussian (Normal) variable with mean mu and standard deviation sigma, then Z = (X - mu)/sigma has a Normal distribution with mean 0 and SD = 1: it has been standardised.
First, we assume all the probabilities follow normal distribution, so the sum of two means also follows the normal distribution and the standard deviation is the sum of the standard deviations.The method goes as follows:Since you have the cycle time for each workstation, assign a value that should be the limit between the duration of the jobs and the cycle time,begin firstly by standardizing the first job which has to be assigned, with the mean of this job and its standard deviation. If it does not exceed the cycle time, assign this job in this station.add to the current workstation the second job and add the two means together as well as the standard deviations. If the cycle time is exceeded, open a new workstation.
A list of daily temperatures in Phoenix, Arizona, for each day in DecemberA chart showing the total sales for each of New York's three biggest supermarketscomment And may well include analyses such as average, median, standard deviation etc.
A list of daily temperatures in Phoenix, Arizona, for each day in DecemberA chart showing the total sales for each of New York's three biggest supermarketscomment And may well include analyses such as average, median, standard deviation etc.
there are 74 lines on both sides of paper but on 1 side there is 35.