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Is Mean is more sensitive to skewness than the median?
If the distribution is positively skewed , then the mean will always be the highest estimate of central tendency and the mode will always be the lowest estimate of central tendency (If it is a uni-modal distribution). If the distribution is negatively skewed then mean will always be the lowest estimate of central tendency and the mode will be the highest estimate of central tendency. In both positive and negative skewed distribution the median will always be between the mean and the mode. If a distribution is less symmetrical and more skewed, you are better of using the median over the mean.
What does closed mean in algebra?
Cloed means less than.
What does mas mean?
It means more. For example: Mas o menos. (More or less)
What does 6 less than -3 mean?
It means that a number is 6 less then -3. you subtract 6 from -3 which equals -9. The answer is -9.
What is the meaning of serious side and lighter side?
serious side means to be very serious and mean about something serious. lighter side means to be less mean and more bright thinking.
Why is the standard deviation of a distribution of means smaller than the standard deviation of the population from which it was derived?
The reason the standard deviation of a distribution of means is smaller than the standard deviation of the population from which it was derived is actually quite logical. Keep in mind that standard deviation is the square root of variance. Variance is quite simply an expression of the variation among values in the population. Each of the means within the distribution of means is comprised of a sample of values taken randomly from the population. While it is possible for a random sample of multiple values to have come from one extreme or the other of the population distribution, it is unlikely. Generally, each sample will consist of some values on the lower end of the distribution, some from the higher end, and most from near the middle. In most cases, the values (both extremes and middle values) within each sample will balance out and average out to somewhere toward the middle of the population distribution. So the mean of each sample is likely to be close to the mean of the population and unlikely to be extreme in either direction. Because the majority of the means in a distribution of means will fall closer to the population mean than many of the individual values in the population, there is less variation among the distribution of means than among individual values in the population from which it was derived. Because there is less variation, the variance is lower, and thus, the square root of the variance - the standard deviation of the distribution of means - is less than the standard deviation of the population from which it was derived.
What does it mean if the standard deviation is less than the mean?
If the mean is less than or equal to zero, it means there has been a serious calculation error. If the mean is greater than zero and the distribution is Gaussian (standard normal), it means that there is an 84.1% chance that the value of a randomly variable will be positive.
When is the mean less than the median?
When the data distribution is negatively skewed.
How come the mean is less than the median?
If the distribution is not symmetric, the mean will be different from the median. A negatively skewed distribution will have a mean hat is smaller than the median, provided it is unimodal.
How would the mean and standard deviation change if the largest data in each set were removed?
Yes. The standard deviation and mean would be less. How much less would depend on the sample size, the distribution that the sample was taken from (parent distribution) and the parameters of the parent distribution. The affect on the sampling distribution of the mean and standard deviation could easily be identified by Monte Carlo simulation.
How does the population in Mexico affect food supply?
Well, a bigger population means more food being consumed. However, a bigger population means more people are working. That means that a percentage of the new population will work in the food industry and so more food will be processed. A smaller population means less food being cunsumed. That means less people are working. So less people will be working in the food industry and so less food will be processed. So really, the population and food supply have a correlation.
What is the advantage of less population in Asian countries?
Less population means more benefits, services and prosperity for everyone. A controlled population strives better.
What is the difference between Large and small sample?
0.7.1 Difference between Small and Large Samples:-Though it is difficult t draw a clear-cut line of demarcation between large and small samples it is normally agreed amongst statisticians that a sample is to be recorded as large only if its size exceeds 30. The tests of significance used for dealing with problems samples for the reason that the assumptions that we make in case of large samples do not hold good for small samples.The assumption made while dealing with problems relating to large samples are:-(i) The random sampling distribution of a statistic is approximately normal. and(ii) Values given by the samples are sufficiently close to the population value and can be used in its place for calculating the standard error of the estimate.Fourmula0.7.2 (Large Sample) Testing the significance of the difference between the means of two samples.)To compare the means of two population we must understand the theory concerning the distribution of differences of sample means. Statisticians have determined that the distribution distribution difference between mean d (d Mean's) is approximately normal for large samples of n1 and n2. That is the distribution of differences of sample means is normal as long as neither n1 nor n2 Is less than 30. We can therefore use the probabilities associated with the normal distribution to construct confidence intervals and to perform hypothesis tests associated with this distribution.PROCUEDURS:-1. To compare the (μ1) mean of population 1 with the mean (μ2), of population 2 two independent random random samples of sizes n1 and n2 are to be selected from population 1 and population 2 respectively.By independent we mean that the sample drawn from population 1, in no way affects the sample drawn from population 2 fro example drawing two samples from men population and women population2. Compute (Mean1) and (Mean 2) i.e., mean of the sample 1 and 23. Computer the difference in the two samples means, d (mean) i.e,. d(Mean) = (Mean1 -Mean2).Thus for each pair of sample means of (Mean1) and (Mean2). a value of d(Mean) is obtained. The result is therefore a distribution of d(Mean)s.4. If μ1 and σ1 are the parameters of population 1. and μ2 and σ2 are the parameters of population 2, then for the distribution of d(Mean)s the menu μd(Mean)s is given by the equationμd(Mean)s = μ1 - μ2 the mean of the difference of the distribution of mean is the difference of the means of the two populations being compared.5. The standard deviation (or standard error) of the distribution of d(mean)s (written as σd(Mean)s) is given by the equation(Large Sample) Testing the significance of the difference between the means of two samples.)1. Point Estimation:- According to Central Limit Theorem for large samples the means of sampling distribution are normally distributed. The procedure that is frequently used to obtain a point estimate for the m of some population involves the following steps:(a) Select a representation (random) sample of the population.(b) Determine the mean (Mean) of the sample data(c) Assert that the value of M is the corresponding value of (Mean) i.e., = μ.2. Interval Estimation:-An extension of the above method of obtaining an estimate for μ is with the confidence interval, i.e., an interval estimate for μ.The advantages of interval estimate are:1. Interval estimate is more likely to be correct than the point estimate.2. We can calculate the probability that a given interval contains the mean of a population. We therefore speak of a specific interval as having "90' per cent probability of containing μ.3. We can choose the value of the probability we want for a given interval before we actually construct it.Recall that the central limit theorem asserts that for large sample sizes, the means are normally distributed. Furthermore, we know that any given mean (Mean) value can be standardized with the equation.Where μ = Mean of the populationμ.(Mean) = Mean of the sampling distribution of means.σ (Mean) = standard error or sampling distributionSinceμ.(Mean) μ we can write the following equationNow, with a given pair of Z values associated with some percentage of the Z distribution and equation, we can determine an upper and lower boundary for the same percentage of (Mean) values in the given mean distribution.
Square of the deviation will be less if?
If the deviation is small ie if the distribution is packed close to the mean.
What does a z score of -4 indicate?
It means that your raw score is four standard deviations below the mean. This will mean different things depending on the context of the question. If you're looking at the probability of a single score occurring in a given distribution (say, a score of 40 in a distribution of scores with a mean of 80 and a std. dev. of 10), then this means that the probability of getting a 40 is very, very low--less than .00002.
Is considerably skewed distribution the same as bimodal distribution?
No. A distribution may be non-skewed and bimodal or skewed and bimodal. Bimodal means that the distribution has two modes, or two local maxima on the curve. Visually, one can see two peaks on the distribution curve. Mixture problems (combination of two random variables with different modes) can produce bimodal curves. See: http://en.wikipedia.org/wiki/Bimodal_distribution A distribution is skewed when the mean and median are different values. A distribution is negatively skewed when the mean is less than the median and positively skewed if the mean is greater than the median. See: http://en.wikipedia.org/wiki/Skewness
Why is median age good?
Median age gives a more accurate representation of the central tendency of a population's age distribution, as it is less affected by outliers compared to the mean. It provides a clearer understanding of the typical age of a population and can be useful for demographic analysis and policy making.
