Yes, and the justification comes from the Central Limit Theorem.
The empirical rule can only be used for a normal distribution, so I will assume you are referring to a normal distribution. Chebyshev's theorem can be used for any distribution. The empirical rule is more accurate than Chebyshev's theorem for a normal distribution. For 2 standard deviations (sd) from the mean, the empirical rule says 95% of the data are within that, and Chebyshev's theorem says 1 - 1/2^2 = 1 - 1/4 = 3/4 or 75% of the data are within that. From the standard normal distribution chart, the answer for 2 sd from the mean is 95.44% So, as you can see the empirical rule is more accurate.
The central limit theorem is one of two fundamental theories of probability. It's very important because its the reason a great number of statistical procedures work. The theorem states the distribution of an average has the tendency to be normal, even when it turns out that the distribution from which the average is calculated is definitely non-normal.
to simply organise your numbers.ajm If you can make a histogram, a dotplot, or even a boxplot; there is no reason to do a steam and leaf plot. It's the worst graph. With a stem and leaf graph, you can see the distribution of data points, and determine whether it's normal distribution or not. As mentioned above, there are better graphs for doing that, though.
The difference isn't approximate. Gross pay is how much in total you have been paid. Net pay is the amount of money you have left after spending it. So for example, Your Gross pay each year is $200,000 but after taxes, bills, fun, and luxuries your net pay is $12,000 a year.
4 sheets approximately 24 inches by 48 inches can be made from a 4 foot by 8 foot piece of plywood. The reason it is approximate is the kerf of the saw blade takes up about 1/16 of an inch.
Approximately normal distributions occur in many situations, as explained by the central limit theorem. When there is reason to suspect the presence of a large number of small effects acting additively and independently, it is reasonable to assume that observations will be normal. Reference Wikipedia on Normal Distribution. See related link. Examples of bell shaped curves are t-distribution and normal distribution. There is little difference between the two curves when the sample size is greater than 30.
The empirical rule can only be used for a normal distribution, so I will assume you are referring to a normal distribution. Chebyshev's theorem can be used for any distribution. The empirical rule is more accurate than Chebyshev's theorem for a normal distribution. For 2 standard deviations (sd) from the mean, the empirical rule says 95% of the data are within that, and Chebyshev's theorem says 1 - 1/2^2 = 1 - 1/4 = 3/4 or 75% of the data are within that. From the standard normal distribution chart, the answer for 2 sd from the mean is 95.44% So, as you can see the empirical rule is more accurate.
The central limit theorem is one of two fundamental theories of probability. It's very important because its the reason a great number of statistical procedures work. The theorem states the distribution of an average has the tendency to be normal, even when it turns out that the distribution from which the average is calculated is definitely non-normal.
For theoretical reasons (such as the central limit theorem), any variable that is the sum of a large number of independent factors is likely to be normally distributed. For this reason, the normal distribution is used throughout statistics, natural science, and social science as a simple model for complex phenomena.
Not all univariate data will be normally distributed. Graphing the data will help you determine if you got the kind of distribution you were expecting, and if not, what kinds of tests will be appropriate for what you got. A strange distribution when you had reason to expect, say, a normal distribution would help you uncover possible problems with data collection.
Global population distribution is where people areAnd how many people are in one area and for what reason
what is optimum point
The uneven distribution of population in Pakistan can be attributed to factors such as geographic features like mountains and deserts that limit inhabitable areas, historical patterns of settlement, economic opportunities in urban areas, and social factors like cultural preferences and availability of resources. Additionally, government policies and infrastructure development also play a role in shaping population distribution in the country.
all of the above
Trade networks develop to exchange resources.
to simply organise your numbers.ajm If you can make a histogram, a dotplot, or even a boxplot; there is no reason to do a steam and leaf plot. It's the worst graph. With a stem and leaf graph, you can see the distribution of data points, and determine whether it's normal distribution or not. As mentioned above, there are better graphs for doing that, though.
No. They are required by law to settle an estate with expediency. If the executor, or co-executor, is delaying the distribution for no apparent reason they should be reported to the court. They can be replaced.No. They are required by law to settle an estate with expediency. If the executor, or co-executor, is delaying the distribution for no apparent reason they should be reported to the court. They can be replaced.No. They are required by law to settle an estate with expediency. If the executor, or co-executor, is delaying the distribution for no apparent reason they should be reported to the court. They can be replaced.No. They are required by law to settle an estate with expediency. If the executor, or co-executor, is delaying the distribution for no apparent reason they should be reported to the court. They can be replaced.