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If the mean height of the population is 68 inches and the standard deviation is 4 inches99.7 of the population will have a height within ranges?
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If average height for women is normally distributed with a mean of 65 inches and a standard deviation of 2.5 inches then approximately 95 percent of all women should be between what and what inches?
A normal distribution with a mean of 65 and a standard deviation of 2.5 would have 95% of the population being between 60 and 70, i.e. +/- two standard deviations.
What is the importance of the standard deviation?
It allows you to understand, or comprehend the average fluctuation to the average. example: the average height for adult men in the United States is about 70", with a standard deviation of around 3". This means that most men (about 68%, assuming a normal distribution) have a height within 3" of the mean (67"- 73"), one standard deviation, and almost all men (about 95%) have a height within 6" of the mean (64"-76"), two standard deviations. In summation standard deviation allows us to see the 'average' as a whole.
What is point estimation?
In statistics, point estimation is the process of providing a number or vector (which could be an infinite dimensional vector such as a function) that is stochastically 'close' in some sense to the actual value of that number or vector. For example, suppose that a population of people has a known mean height of 180 cm and an unknown standard deviation. Point estimation could be applied to a sample from this population to obtain an estimate of the standard deviation of its heights.
The standard abbreviation for height is h?
The standard abbreviation for height is ht.
What is a really easy explanation of standard deviation?
Standard deviation tells you how spread out your set of values is compared with the average (of your set of values).For example, if you have the heights of all the players in a soccer football team, then you can work out the average height (the mean). But if you know the mean, that doesn't tell you much about the spread. If the average height is 180 cm, you don't know if ALL the players were 180 cm, or if they were all between 175 and 195 cm. You don't know if one of them was 210 cm, or if some were really short. If we know the SPREAD then we have some extra information.The standard deviation is the average difference between a player's height and the average for the team. So if the team average height is 180 cm, and the standard deviation is small, say 4 cm, then you know that most players are between 176 and 184 cm. If the standard deviation is large (say 18 cm) then most players are between 162 and 198 cm, a much bigger range!! So the standard deviation really does tell you something about your data.Basically, standard deviation is when you measure the differences between your players and the average height. Some will be shorter than average (with a negative difference) and some will be taller than average (with a positive difference). And some may have a zero difference (if they are the same height as the mean).If you add up all these differences, the negative ones will cancel out the positives, and you won't get any useful information. So you SQUARE all the differences first before you add them up. When you square a negative number it becomes positive (-2 times -2 = +4). Then you get the average of all the squared differences (add them all up and divide the number of answers, that is, eleven). So for our eleven players, square the difference between each one's height and the average. Add them all together, and divide by 11. This answer is called the VARIANCE.(If you were only measuring a sample of the team you would divide by 10 [one FEWER than the total number], but because you measured the whole population of the team, you divide by 11.)Get the square root of the variance (remember you squared all the numbers, now you unsquare them), and the answer is the standard deviation. (Square root is the opposite of squared. Four squared = 16. The square root of 16 is 4.)Here it is again:Get the average (mean) of the heights of all your players.Work out all the differences between their heights and the average. Shorter players will have a negative difference, taller players will have a positive.Square each difference (Square means multiply it by itself, eg, -8 x -8 = +64). All the answers will be positive.Add all the answers together and divide by 11. This number is called the Variance.Get the square root of the Variance and THAT is the Standard Deviation.A small standard deviation (3 or 4 cm) tells you that most of the team are about the same size. A large standard deviation (15 to 20 cm) tells you that you have a bigger spread, and might have some really tall, and some really short. Answer:The question actually asked for "a really easy explanation". Now, although it is not an easy concept for any really easy explanation, I'm sure we can simplify a little the great mass that we have above for the average 'JoeBlow'.Standard deviation is, as mentioned above, a measure of "the spread", or how far spread apart, from the average of all the figures you are considering, or of all the set of measurements you have made about something.To possess meaning, we express this 'spread' using numbers. 1 standard deviation, for instance, ABOVE the average, or mean, of all the values in your sample is the point at which 34% of the values nearest, but above the mean lie. On the other hand, the 34% of numbers closest to the mean, but Below the mean is called the -1 standard deviation value. So 68% of all the values in your sample fall inside 1 standard deviation above the mean and 1 standard deviation below the mean. This region will, therefore, possess the middle 68% of all the values in your sample - which is most of them really.
What is the difference from having a standard deviation that is a sample and population and how do you tell which one is suited for the question.?
A standard deviation for a sample makes a judgment on the whole data set whereas the population standard deviation uses the shole data set. If the questions says for example, a sample of 50 peoples height was taken... you would use the sample method but if you were asked : "Everyone in the class had their height measured" you could use the population method Hope that helps
What is the estimd population mean of sample 36 mean weight 3.01 standard diviation 03?
The answer will depend on the population mean of what variable? Height?, length or is it simply weight. If it is weight, the estimated (not estimd) population mean is 3.01 units: the same as the sample mean. The standard deviation (not diviation) is irrelevant.The answer will depend on the population mean of what variable? Height?, length or is it simply weight. If it is weight, the estimated (not estimd) population mean is 3.01 units: the same as the sample mean. The standard deviation (not diviation) is irrelevant.The answer will depend on the population mean of what variable? Height?, length or is it simply weight. If it is weight, the estimated (not estimd) population mean is 3.01 units: the same as the sample mean. The standard deviation (not diviation) is irrelevant.The answer will depend on the population mean of what variable? Height?, length or is it simply weight. If it is weight, the estimated (not estimd) population mean is 3.01 units: the same as the sample mean. The standard deviation (not diviation) is irrelevant.
What does it mean when the standard deviation is 1?
A standard deviation is a statistical measure of the variation there in a population or group. A standard deviation of 1 means that 68% of the members of the population are withing plus or minus the value of the standard deviation from the average. For example: assume the average height of men is 5 feet 9 inches, and the standard deviation is three inches. Then 68% of all men are between 5' 6" and 6' which is 5'9" plus or minus 3 inches. [Note: this is only to illustrate and is not intended to be a real/correct statistic of men's heights.]
If average height for women is normally distributed with a mean of 65 inches and a standard deviation of 2.5 inches then approximately 95 percent of all women should be between what and what inches?
A normal distribution with a mean of 65 and a standard deviation of 2.5 would have 95% of the population being between 60 and 70, i.e. +/- two standard deviations.
What determines the standard deviation to be high?
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.
What is the importance of the standard deviation?
It allows you to understand, or comprehend the average fluctuation to the average. example: the average height for adult men in the United States is about 70", with a standard deviation of around 3". This means that most men (about 68%, assuming a normal distribution) have a height within 3" of the mean (67"- 73"), one standard deviation, and almost all men (about 95%) have a height within 6" of the mean (64"-76"), two standard deviations. In summation standard deviation allows us to see the 'average' as a whole.
What is point estimation?
In statistics, point estimation is the process of providing a number or vector (which could be an infinite dimensional vector such as a function) that is stochastically 'close' in some sense to the actual value of that number or vector. For example, suppose that a population of people has a known mean height of 180 cm and an unknown standard deviation. Point estimation could be applied to a sample from this population to obtain an estimate of the standard deviation of its heights.
How do you interpret if this is the data SD of 15.79 and mean of 126.9 or SD of 8.29 and mean of 124.7 also How do you know if the standard deviation is high and is there a highest possible SD?
n probability theory and statistics, thestandard deviation of a statistical population, a data set, or a probability distribution is the square root of itsvariance. Standard deviation is a widely used measure of the variability ordispersion, being algebraically more tractable though practically less robustthan the expected deviation or average absolute deviation.It shows how much variation there is from the "average" (mean). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.For example, the average height for adult men in the United States is about 70 inches (178 cm), with a standard deviation of around 3 in (8 cm). This means that most men (about 68 percent, assuming a normal distribution) have a height within 3 in (8 cm) of the mean (67-73 in (170-185 cm)) - one standard deviation, whereas almost all men (about 95%) have a height within 6 in (15 cm) of the mean (64-76 in (163-193 cm)) - 2 standard deviations. If the standard deviation were zero, then all men would be exactly 70 in (178 cm) high. If the standard deviation were 20 in (51 cm), then men would have much more variable heights, with a typical range of about 50 to 90 in (127 to 229 cm). Three standard deviations account for 99.7% of the sample population being studied, assuming the distribution is normal (bell-shaped).
Are normally distributed with a mean of 68 inches and a standard deviation of 2 inches What is the probability that the height of a randomly selected female college basketball player is more than 66?
84% To solve this problem, you must first realize that 66 inches is one standard deviation below the mean. The empirical rule states that 34% will be between the mean and 1 standard deviation below the mean. We are looking for the prob. of the height being greater than 66 inches, which is then 50% (for the entire right side of the distribution) + 34%
Suppose a normal random variable has a mean of 72 inches and a standard deviation of 2 inches Suppose the random variable X measures the height of adult males in a certain city One may therefore con?
Suppose a normal random variable has a mean of 72 inches and a standard deviation of 2 inches. Suppose the random variable X measures the height of adult males in a certain city. One may therefore conclude that approximately 84% of the men in this population are shorter than?
The standard abbreviation for height is h?
The standard abbreviation for height is ht.
Is normal distribution symmetrical?
Yes. By definition. A normal distribution has a bell-shaped density curve described by its mean and standard deviation. The density curve is symmetrical(i.e., an exact reflection of form on opposite sides of a dividing line), and centered about (divided by) its mean, with its spread (width) determined by its standard deviation. Additionally, the mean, median, and mode of the distribution are equal and located at the peak (i.e., height of the curve).
