A negative value indicates a distribution which is more peaked than normal, and a positive kurtosis indicates a shape flatter than normal. An extreme positive kurtosis indicates a distribution where more of the values are located in the tails of the distribution rather than around the mean (Grad pad, 2013). A kurtosis value of +/-1 is considered very good for most psychometric uses, but +/-2 is also usually acceptable (Grad pad, 2013). The above graph shows GPA with a kurtosis of -.811; awhile the final kurtosis is -33.2. The extent to which a distribution of values deviates from symmetry around the mean is the skewness.
There is a small difference in the packing density for the 2 distributions, and the log-normal system has a higher packing density than the Gaussian system.1 There are certain properties of a PSD that may influence the packing density. The characteristic s that has the most influence on RCP is the skew of the distribution and polydispersity. Polydispersity is when the particles are not the same size, shape and mass. Figure 1: A diagram showing the two extreme forms of skewness.4 For a negative skew, shown in figure 1, the effect of polydispersity does not have as much of an effect on the packing density as the positive skew. A negative skew, larger proportion of large particles, will have most of the volume occupied by the larger particles.
Cronbach’s alpha was first developed by Lee Cronbach in order to measure the internal consistency of a test or measurement. The internal consistency of a test is the degree of which all the test items measure the same concept. Alpha is measured from -1 to 1 with 1 being the optimal internal consistency (Tavakol & Dennick, 2011) but having a value above 0.7 is accepted and said to be of very little threat from random and chance errors. (Terre Blanche et al., 2006). The higher the alpha scores the more internal consistency the test items have and therefore the more reliable the test is.
c. individuals who score low on one variable tend to score low on a second. d. high scores on the x variable are associated with low scores on the y variable. __A?__ 12. Which of the following correlations represents the strongest relationship between two variables? a.
Eqns 7.2, 7.3 ? 3) Aliasing is a phenomena that leads to a misinterpretation of data. When the sample rate on a continuous signal is less than twice the highest measured frequency, the discrete series that results appears to have a freq... ... middle of paper ... ...nsform making for a smoother average across the frequency outputs. 12) The most important factors when discussing data acquisition are range, sample rate, and resolution. First off, range and resolution are collaborative because of quantization error.
According to the conductometric titration, the concentration of Ba(OH)2 (aq) was 0.196 M. Calculations based on gravimetric analysis revealed a concentration of 0.0669 M. Evidently, there is a high degree of imprecision between the values determined by each technique. It appears however that the gravimetric analysis was more accurate. The standard deviation for BaSO4 mass was 0.035 and the confidence interval was ±0.0256 g. This illustrates that there is 90% certainty that the actual mass of the BaSO4 precipitate was within 0.0256 g of the calculated mean (0.156 g). It should be noted that an outlier (1.45 g precipitate) was removed from the gravimetric analysis calculations due to being 9.29 times greater than the average. The standard deviation for end point volume – the basis of calculations for the conductometric titration – was 6.616.
In MRA gives good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies process. This approach is helpful when the signal at hand has high frequency material for short durations and low frequency components for long time. 4.5 Wavelet Properties Various properties of wavelet transforms (WT) is described by followed: In Regularity The window for a function is the smallest space-set outside which function is identically zero. In order of the polynomial that can be approximated is determined through number of vanishing moments of wavelets and is useful for compression
It is a very unstable measure as it only depends on two extreme measurements – the lowest and the highest values. Another measure of dispersion that is less sensitive to extreme values is the standard deviation. The standard deviation of a data set makes use of the individual amount that each data value deviates from the mean. Standard Deviation of Ungrouped Data The standard deviation is by far considered the most important measure of variability as compared to the other measures. To find the standard deviation of a set of ungrouped data, we perform the following steps: (assume that a sample is given) Find the sample mean of the given x values.
0.35). We can establish it is a negative, significant relationship as the r value is negative (-0.92). The relationship supports my hypothesis as it indicates that those who average higher on the procrastination score tend to have lower test score. The P value is as 0.35 which indicates a moderate chance of a random occurrence. Surprisingly, academic performance and maladaptive perfectionism are considered invalid.
-4.710556 Durbin-Watson stat 2.099147 Since we are talking about asset returns, a standard GARCH model may not be the best choice as we would expect there to be asymmetry in the volatility (Brooks 2008, p. 404). The EGARCH model would allow negative shocks to have a larger effect on the conditional variance than positive ones. As we can see in the below Eviews output, it is the case that negative shocks have a larger effect because the coefficient C(4) is negative. Because we are estimating the log of the conditional variance, unlike the standard GARCH model, it can be more difficult to interpret the exact meaning of all the parameters. Dependent Variable: RLSP500 Method: ML - ARCH (Marquardt) - Normal distribution Date: 07/29/12 Time: 20:08 Sample (adjusted): 1/10/2005 1/31/2011 Included observations: 317 after adjustments Convergence achieved after 35 iterations Presample variance: backcast (parameter = 0.7) LOG(GARCH) = C(2) + C(3)*ABS(RESID(-1)/@SQRT(GARCH(-1))) + C(4) *RESID(-1)/@SQRT(GARCH(-1)) + C(5)*LOG(GARCH(-1)) Variable Coefficient Std.