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What else can I help you with?
Do you need more than 10 observations in linear regression?
This is a difficult question to answer. The pure answer is no. In reality, it depends on the level of randomness in the data. If you plot the data, it will give you an idea of the randomness. Even with 10 data points, 1 or 2 outliers can significantly change the regression equation. I am not aware of a rule of thumb on the minimum number of data points. Obviously, the more the better. Also, calculate the correlation coefficient. Be sure to follow the rules of regression. See the following website: http:/www.duke.edu/~rnau/testing.htm
What is the value for 10 factorial?
3,628,800
What is the value of the digit 7 in the number 18.79?
The digit 7 is in the tenths place, therefore its value is 7/10 (or seven tenths).
What happens to the place value of a number when you multiply by 10?
The place value increases to the next power of 10. A Unit becomes a Ten A Ten becomes a Hundred A Hundred becomes a Thousand and so on.
What is the value of the digit 1 in the number 18?
Oh, dude, the value of the digit 1 in the number 18 is like... 1! Yeah, I know, mind-blowing stuff. It's like the 1 is just hanging out there, doing its thing, making the number what it is. So, yeah, that's the deal with the value of 1 in 18.
What is an example of a coefficient?
In the equation 4x + 2 = 10, the coefficient of x is 4. This coefficient represents the value that multiplies the variable x.
What is the Value of Hall coefficient of germanium n type?
-(1.907±0.071)*10^-2 m^3/C
A material has a coefficient of volume expansion of 60×10^-6/degree celsius. What is its coefficient of expansion?
The coefficient of volume expansion is the triple of the linear expansion coefficient. So with a volume expansion coefficient of 60×10^-6/°C, the linear expansion coefficient would be 20×10^-6/°C.
What is the value of linear absorption coefficient for Gold?
The linear absorption coefficient for gold depends on the wavelength of the incident light. At a typical visible wavelength of around 550 nm, gold has a linear absorption coefficient of approximately 5.5 x 10^5 cm^-1.
Diffusion coefficient of sodium chloride in water?
The diffusion coefficient of sodium chloride in water typically ranges from 0.6 to 2.3 x 10^-9 m^2/s at 25°C. This value can vary depending on factors such as temperature and concentration.
What is Q-10?
temperature coefficient =10 degree celsius..
What are the parts of a number in scientific notation?
In scientific notation, a number is represented as the product of a coefficient and a power of 10. The coefficient is a decimal number between 1 and 10, and the power of 10 indicates the number of places the decimal point is moved. For example, in the number 4.2 x 10^3, "4.2" is the coefficient and "3" is the power of 10.
What is the numerical coefficient of the term 10x?
It is: 10
What is the coefficient of 10xy?
The numerical part, 10.
How do you simulate an AR1 process using Microsoft Excel?
This can be done fairly easily using Excel's random number generator and a couple of simple equations. First, you need to specify the auto correlation coefficient you wan to use and the mean value and error variance of the AR1 process. For example, you could say that the auto correlation coefficient is 0.5, and that the mean = 100 and the errors are ~ N ( 0, 102 ); i.e., the errors are normally distributed with a mean of 0 and a standard deviation of 10. Keep in mind that for the AR1 series to be stationary, the absolute value of the auto correlation coefficient must be less than 1. Then using Excel's random number generator you would generate an array of error terms equal in length to the AR1 series you want to create. This is done as follows: 1) Type the following formula into the first cell of the error array, say cell B11 = NORMINV(RAND(), 0, 10) [Note: The values 0 and 10 in the above equation represent the mean and standard deviation of the error distribution. Ordinarily errors have a mean of 0, but you should substitute a relative cell reference for the standard deviation so that it can be changed without re-entering the formula.] 2) Copy the formula to as many cells as you want to have terms in the AR1 series 3) Now, start the AR1 series in a separate array, say cell C11. Begin by making the first term equal to the mean value; for example, = 100. Then in cell C12 type = 100 + 0.5*C11 + B11 [Note: The values 100 and 0.5 in the above equation represent the mean and auto correlation values. As before you should substitute a relative cell reference so that they can be changed without re-entering the formula. Be careful to make sure that the cell containing the mean value is the same in both formulas.] 4) You should then copy that formula down the entire array for the AR1 series. To make sure you have done everything correctly you can check your AR1 series as follows: The mean of an AR1 process = mean/(1 - auto correlation coefficient). In this case, with a mean of 100 and an auto correlation coefficient of 0.5 the mean value = 200. Thus, if you take the average of the AR1 array, it should be approximately = 200. The variance of an AR1 process = error variance/(1 - autocorr2 ). In this case with a variance of 100 and an auto correlation coefficient of 0.5 we would get 133.33 for a variance or about 11.55 for a standard deviation. Thus if you take the standard deviation of the AR1 series you should get 11.55. Hope this helps. This can be done fairly easily using Excel's random number generator and a couple of simple equations. First, you need to specify the auto correlation coefficient you wan to use and the mean value and error variance of the AR1 process. For example, you could say that the auto correlation coefficient is 0.5, and that the mean = 100 and the errors are ~ N ( 0, 102 ); i.e., the errors are normally distributed with a mean of 0 and a standard deviation of 10. Keep in mind that for the AR1 series to be stationary, the absolute value of the auto correlation coefficient must be less than 1. Then using Excel's random number generator you would generate an array of error terms equal in length to the AR1 series you want to create. This is done as follows: 1) Type the following formula into the first cell of the error array, say cell B11 = NORMINV(RAND(), 0, 10) [Note: The values 0 and 10 in the above equation represent the mean and standard deviation of the error distribution. Ordinarily errors have a mean of 0, but you should substitute a relative cell reference for the standard deviation so that it can be changed without re-entering the formula.] 2) Copy the formula to as many cells as you want to have terms in the AR1 series 3) Now, start the AR1 series in a separate array, say cell C11. Begin by making the first term equal to the mean value; for example, = 100. Then in cell C12 type = 100 + 0.5*C11 + B11 [Note: The values 100 and 0.5 in the above equation represent the mean and auto correlation values. As before you should substitute a relative cell reference so that they can be changed without re-entering the formula. Be careful to make sure that the cell containing the mean value is the same in both formulas.] 4) You should then copy that formula down the entire array for the AR1 series. To make sure you have done everything correctly you can check your AR1 series as follows: The mean of an AR1 process = mean/(1 - auto correlation coefficient). In this case, with a mean of 100 and an auto correlation coefficient of 0.5 the mean value = 200. Thus, if you take the average of the AR1 array, it should be approximately = 200. The variance of an AR1 process = error variance/(1 - autocorr2 ). In this case with a variance of 100 and an auto correlation coefficient of 0.5 we would get 133.33 for a variance or about 11.55 for a standard deviation. Thus if you take the standard deviation of the AR1 series you should get 11.55. Hope this helps.
What Simplify and determine the coefficient of (-x)(5y)(-2x).?
The simplified term is 10x2y so that the coefficient is 10.
How do we know if a correlation is significant or not?
There are several statistical measures of correlation: some require only a nominal scale, that is, data classified according to two criteria; others require an ordinal scale, which is the ability to determine whether one measurement is bigger or smaller than another; others require an interval scale, which allows you to determine the difference in values but not the ratio between them. [A good example of the latter is temperature measured in any scale other than Kelvin: the difference between 10 degrees C and 15 degrees C is 5 C degrees, but 15 C is not 1.5 times as warm as 10 C.]The contingency coefficient, which is suitable for nominal data, has a chi-squared distribution.The Spearman rank correlation, requiring ordinal data, has its own distribution for small data sets but as the number of units increases to n, the distribution approaches Student's t-distribution with n-2 degrees of freedom.The Kendall rank correlation coefficient can be used in identical situations and gives the same measure of significance. However, the Kendall coefficient can also be used to test partial correlation - whether the correlation between two variables is "genuine" or whether it arises because both variables are actually correlated to a third variable.The Pearson's product moment correlation coefficient (PMCC) is the most powerful but requires measurement on an interval scale as well as an underlying bivariate Normal distribution.The significance levels of these correlation measures are tabulated for testing.A simple "rule of thumb" for testing the significance of PMCC is that values below -0.7 or above 0.7 are highly significant. Values in the ranges (-0.7, -0.3) and (0.3, 0.7) are moderate, and values between -0.3 and +0.3 are not significant.
