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How do you calculate a coefficient knowing the expected rate of return and standard deviation?
It depends on what the underlying distribution is and which coefficient you want to calculate.
The standard error of measurement is always zero when the reliability coefficient equals what number?
.00
How can you calculate standard error in volume?
The standard error is calculated by dividing the actual volume by the experimental volume. This is a common technique used in the laboratory.
How do you calculate the error of a median for a non-parametric distribution?
You would need to take repeated samples, find their median and then calculate the standard error of these values.
How do you calculate coefficient of variation in spss?
The coefficient of variation is a method of measuring how spread out the values in a data set are relative to the mean. It is calculated as follows: Coefficient of variation = σ / μ Where: σ = standard deviation of the data set μ = average of the data set If you want to know more about it, you can visit SilverLake Consulting which will help you calculate the coefficient of variation in spss.
How do you calculate a coefficient knowing the expected rate of return and standard deviation?
It depends on what the underlying distribution is and which coefficient you want to calculate.
The standard error of measurement is always zero when the reliability coefficient equals what number?
.00
What is the standard error of the sampling distribution equal to when you do not know the population standard deviation?
You calculate the standard error using the data.
How do you calculate coefficient of variation?
The coefficient of variation is usually calculated by diving the standard deviation by the mean of a particular set of data. The coefficient of variation is usually expressed as CV.
How can you calculate standard error in volume?
The standard error is calculated by dividing the actual volume by the experimental volume. This is a common technique used in the laboratory.
How do you calculate the error of a median for a non-parametric distribution?
You would need to take repeated samples, find their median and then calculate the standard error of these values.
How does one calculate the standard error of the sample mean?
Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.
Why do you need to calculate standard deviation and relative error?
to ensure your experiment is precise and to prevent error to happen during experiment
How do you calculate coefficient of variation in spss?
The coefficient of variation is a method of measuring how spread out the values in a data set are relative to the mean. It is calculated as follows: Coefficient of variation = σ / μ Where: σ = standard deviation of the data set μ = average of the data set If you want to know more about it, you can visit SilverLake Consulting which will help you calculate the coefficient of variation in spss.
How do you calculate the absolute probable error of the mean?
(0.6745 * Standard deviation)/ (n^1/2) :)
How would you calculate Extinction coefficient of NADH at 260nm?
By using Beer's law and making a standard curve of differing NADH concentrations at 260nm
What does a high t statistic mean?
Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated. Assuming you mean the t-statistic from least squares regression, the t-statistic is the regression coefficient (of a given independent variable) divided by its standard error. The standard error is essentially one estimated standard deviation of the data set for the relevant variable. To have a very large t-statistic implies that the coefficient was able to be estimated with a fair amount of accuracy. If the t-stat is more than 2 (the coefficient is at least twice as large as the standard error), you would generally conclude that the variable in question has a significant impact on the dependent variable. High t-statistics (over 2) mean the variable is significant. What if it's REALLY high? Then something is wrong. The data points might be serially correlated.
