It means that the individual data points may vary a lot from the average.
5.30 is one example - although it does indicate a lower degree of precision.
The numbers indicate the relative hardness of the pencil lead.
It is close to becoming an exact answer too a whole number!!
2826.00 (if you wish to indicate the precision). Otherwise, stick with 2826.
It does not indicate anything if the mean is greater than the standard deviation.
It means that the individual data points may vary a lot from the average.
A small standard deviation indicates that the data points in a dataset are close to the mean or average value. This suggests that the data is less spread out and more consistent, with less variability among the values. A small standard deviation may indicate that the data points are clustered around the mean.
Standard deviation is a measure of the scatter or dispersion of the data. Two sets of data can have the same mean, but different standard deviations. The dataset with the higher standard deviation will generally have values that are more scattered. We generally look at the standard deviation in relation to the mean. If the standard deviation is much smaller than the mean, we may consider that the data has low dipersion. If the standard deviation is much higher than the mean, it may indicate the dataset has high dispersion A second cause is an outlier, a value that is very different from the data. Sometimes it is a mistake. I will give you an example. Suppose I am measuring people's height, and I record all data in meters, except on height which I record in millimeters- 1000 times higher. This may cause an erroneous mean and standard deviation to be calculated.
The standard deviation for a set of data is a measure of how much the individual observations are spread about their mean. A small value indicates that they are all tightly packed around the mean value whereas a large value indicates that the observations are not so close together.
Significant figures indicate the precision of a measurement.
The purpose of obtaining the standard deviation is to measure the dispersion data has from the mean. Data sets can be widely dispersed, or narrowly dispersed. The standard deviation measures the degree of dispersion. Each standard deviation has a percentage probability that a single datum will fall within that distance from the mean. One standard deviation of a normal distribution contains 66.67% of all data in a particular data set. Therefore, any single datum in the data has a 66.67% chance of falling within one standard deviation from the mean. 95% of all data in the data set will fall within two standard deviations of the mean. So, how does this help us in the real world? Well, I will use the world of finance/investments to illustrate real world application. In finance, we use the standard deviation and variance to measure risk of a particular investment. Assume the mean is 15%. That would indicate that we expect to earn a 15% return on an investment. However, we never earn what we expect, so we use the standard deviation to measure the likelihood the expected return will fall away from that expected return (or mean). If the standard deviation is 2%, we have a 66.67% chance the return will actually be between 13% and 17%. We expect a 95% chance that the return on the investment will yield an 11% to 19% return. The larger the standard deviation, the greater the risk involved with a particular investment. That is a real world example of how we use the standard deviation to measure risk, and expected return on an investment.
If the minimum value is the minimum observed value then it indicates that the distribution goes below the minimum observed value.If the minimum value is the minimum defined for the distribution then it indicates thatthe data do not come from the proposed distribution,estimates for the mean or standard deviation are incorrect, oryou have got a sample which is atypical.
A z score is a value that is used to indicate the distance of a certain number from the mean of a normally distributed data set. A z score of -1.0 means that the number is one standard deviation below the mean. A z score of +1.0 means that the number is one standard deviation above the mean. Z scores normally range from -4.0 to +4.0. Hope this helps! =)
0.001 in standard form is 1*10-3. The original number may not be correct to 3 significant figures, but if it is, it should have been written as 0.00100 to indicate the degree of precision. In that case the standard form would be 1.00*10-3.
liquid
Six. Assuming the trailing 0s are there to indicate a degree of precision.