The t-distribution and the normal distribution are not exactly the same. The t-distribution is approximately normal, but since the sample size is so small, it is not exact. But n increases (sample size), degrees of freedom also increase (remember, df = n - 1) and the distribution of t becomes closer and closer to a normal distribution. Check out this picture for a visual explanation: http://www.uwsp.edu/PSYCH/stat/10/Image87.gif
As n increases, the distribution becomes more normal per the central limit theorem.
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.
9.8 m/s2 ---------------------- Yes this is the average value of acceleration due to gravity near by the surface of the earth. As we go higher and higher level this g value decreases and becomes almost negligible. Same way as we go deeper and deeper the g value decreases and at the centre of the earth its value becomes zero.
For statistical tests based on (Student's) t-distribution you use the t-table. This is appropriate for small sample sizes - up to around 30. For larger samples (or degrees of freedom), the t-distribution becomes very close to the Standard Normal distribution so you use the z-tables.
No- skewness parameter declines with increased degrees of freedom. skewness = sqrt(8/k) see link
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
Yes, in the absence of air resistance, objects of different masses will fall at the same rate and reach the ground at the same time if released from the same height. This is because the acceleration due to gravity is constant and acting on both objects equally.
If slate is sufficiently heated and compressed it becomes phyllite.
According to the central limit theorem, as the sample size gets larger, the sampling distribution becomes closer to the Gaussian (Normal) regardless of the distribution of the original population. Equivalently, the sampling distribution of the means of a number of samples also becomes closer to the Gaussian distribution. This is the justification for using the Gaussian distribution for statistical procedures such as estimation and hypothesis testing.
As n increases, the distribution becomes more normal per the central limit theorem.
The t-distribution is symmetric so the question is irrelevant.The t-distribution is symmetric so the question is irrelevant.The t-distribution is symmetric so the question is irrelevant.The t-distribution is symmetric so the question is irrelevant.
An object have greater gravitational pull closer from earth. As we get farther from earth, the gravitational pull becomes weaker. That is why objects sufficiently away from the earth do not fall on it.
The mass of the star.
Generally speaking - if you lower the temperature of a gas, it becomes more dense. If the temperature is lowered sufficiently it will start to condense into a liquid.
The Earth's atmosphere gets thinner and thinner as we go away from the earth. It becomes negligible at a point in space. Beyond that, there is no atmosphere, only vaccum.
There really isn't much difference between a zygote, embryo, and fetus. A zygote forms after fertilization and becomes an embryo, which later becomes a fetus.
The central limit theorem basically states that as the sample size gets large enough, the sampling distribution becomes more normal regardless of the population distribution.