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If the sample mean is 10 the hypothesized population mean is 9 and the population standard deviation is 4 compute the test value needed for the z test?
Wiki User
∙ 13y ago
- n = sample size
- n1 = sample 1 size
- n2 = sample 2 size
- = sample mean
- μ0 = hypothesized population mean
- μ1 = population 1 mean
- μ2 = population 2 mean
- σ = population standard deviation
- σ2 = population variance
Wiki User
∙ 13y ago
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Why standard deviation is more often used than variance?
Both variance and standard deviation are measures of dispersion or variability in a set of data. They both measure how far the observations are scattered away from the mean (or average). While computing the variance, you compute the deviation of each observation from the mean, square it and sum all of the squared deviations. This somewhat exaggerates the true picure because the numbers become large when you square them. So, we take the square root of the variance (to compensate for the excess) and this is known as the standard deviation. This is why the standard deviation is more often used than variance but the standard deviation is just the square root of the variance.
What is rate measure and calculation of errors?
Standard error (statistics)From Wikipedia, the free encyclopediaFor a value that is sampled with an unbiased normally distributed error, the above depicts the proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value.The standard error is a method of measurement or estimation of the standard deviation of the sampling distribution associated with the estimation method.[1] The term may also be used to refer to an estimate of that standard deviation, derived from a particular sample used to compute the estimate.For example, the sample mean is the usual estimator of a population mean. However, different samples drawn from that same population would in general have different values of the sample mean. The standard error of the mean (i.e., of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population. Secondly, the standard error of the mean can refer to an estimate of that standard deviation, computed from the sample of data being analyzed at the time.A way for remembering the term standard error is that, as long as the estimator is unbiased, the standard deviation of the error (the difference between the estimate and the true value) is the same as the standard deviation of the estimates themselves; this is true since the standard deviation of the difference between the random variable and its expected value is equal to the standard deviation of a random variable itself.In practical applications, the true value of the standard deviation (of the error) is usually unknown. As a result, the term standard error is often used to refer to an estimate of this unknown quantity. In such cases it is important to be clear about what has been done and to attempt to take proper account of the fact that the standard error is only an estimate. Unfortunately, this is not often possible and it may then be better to use an approach that avoids using a standard error, for example by using maximum likelihood or a more formal approach to deriving confidence intervals. One well-known case where a proper allowance can be made arises where Student's t-distribution is used to provide a confidence interval for an estimated mean or difference of means. In other cases, the standard error may usefully be used to provide an indication of the size of the uncertainty, but its formal or semi-formal use to provide confidence intervals or tests should be avoided unless the sample size is at least moderately large. Here "large enough" would depend on the particular quantities being analyzed (see power).In regression analysis, the term "standard error" is also used in the phrase standard error of the regression to mean the ordinary least squares estimate of the standard deviation of the underlying errors.[2][3]
How do you compute discrete variables?
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
How is scientific notation useful?
Scientific notation (also called standard form or exponential notation) is a way of writing numbers that accommodates values too large or small to be conveniently written in standard decimal notation
How do you compute the probability distribution of a function of two Poisson random variables?
we compute it by using their differences
Is the range used to compute the median or standard deviation?
Neither.
What is a better measure of variability range or standard deviation?
The standard deviation is better since it takes account of all the information in the data set. However, the range is quick and easy to compute.
Is a standard deviation dependent on the mean?
To find the standard deviation, you must first compute the mean for the data set. So the answer is yes. Just have a look at the 5 steps needed to compute a standard deviation and you will see why the answer is yes. In reality, people most often use calculators or computers to do this. However, it is good to understand what they are doing. 1. Compute the deviation by subtracting the mean from each value. 2. Square each individual deviation. 3. Add up the squared deviations. 4. Divide by one less than the sample size. 5. Take the square root
What is the mean and standard deviation of 60 percent of 300?
There is insufficient information in the question to answer it. In order to compute a mean and a standard deviation, you need at least two data points, but the question only gave one. Please restate the question.
What does is mean when the standard deviation is 0?
Intuitively, a standard deviation is a change from the expected value.For the question you asked, this means that the change in the "results" doesn't exist, which doesn't really happen. If the standard deviation is 0, then it's impossible to perform the test! This shows that it's impossible to compute the probability with the "null" standard deviation from this form:z = (x - µ)/σIf σ = 0, then the probability doesn't exist.
Why do we have to compute for the mean median mode and standard deviation?
To obtain a much better, simpler, and more practical understanding of the data distribution.
How do you compute mean deviation?
You don't need to. The mean deviation is, by definition, zero.
How to compute residual variance of RIL and Sensex?
Variance is variability and diversity of security from average mean and expected value Variance = standard deviation fo security * co relation (r) devided by standanrd deviation of sensex
Why standard deviation is more often used than variance?
Both variance and standard deviation are measures of dispersion or variability in a set of data. They both measure how far the observations are scattered away from the mean (or average). While computing the variance, you compute the deviation of each observation from the mean, square it and sum all of the squared deviations. This somewhat exaggerates the true picure because the numbers become large when you square them. So, we take the square root of the variance (to compensate for the excess) and this is known as the standard deviation. This is why the standard deviation is more often used than variance but the standard deviation is just the square root of the variance.
What is the z score for 0.71?
This question cannot be answered. You need the mean and standard deviation in order to compute a Z score for a Raw score. Please restate the question.
What is the z-score for 45.00?
There is insufficient information in the question to properly answer it. In order to compute a Z score from a raw score, you need the mean and the the standard deviation, neither of which was given. Please restate the question.
Data A 77 64 71 55 72 73 71 47 52 Data 40 25 26 12 2 46 22 23 28 34 which one what data sets shows more variatiion around the mean?
Compute the variance (or its square root , standard deviation) of each of the data set. Set 1: standard deviation = 10.121 Set 2: standard deviation = 12.09 Set 2 shows more variation around the mean. Check the link below
