We at have developed this official mobile app of our website, for the convinience of our viewers. Based on the ORMs at each population Ag, Pb, and As were often associated with Au mineralization.We at have developed this official mobile app of our website. It revealed that Te and Hg can be considered as pathfinder elements for Au at the host rock. On the other hand, after 23 steps of optimization process at the host rock population, an ORM was conducted by Ag>Te>Hg>Pb>Mg>Al>Sb>As represented in descending order of t-values. In addition, an ORM for vein population was extracted for Sb>Al>As>Mg>Pb>Cu>Ag elements with the R2 up to 0.99. Therefore, 12 samples (cluster 2) with the maximum distance from centroid, indicates the intensity of vein polymetallic mineralization in the deposit. According to the epigenetic vein genesis of Glojeh polymetallic deposit, determination of spatial patterns and elemental associations accompanied by anomaly separation were conducted by K-means cluster and robust factor analysis method based on centered log-ratio (clr) transformed data. After 18 steps, an optimized reduced model (ORM) was constructed and ranked in order of importance as Pb>Ag>P>Hg>Mn>Nb>U>Sr>Sn>As>Cu, with the lowest confidence level (CL) of 92% for Cu. Backward elimination technique is applied to reduce the number of variables in the model through all the borehole data. This method concerns directly to the application of t-test (TINV and TDIST to analyses of variables in the model) and F-test (FDIST, F-statistic to compare different models) analysis. The LINEST is a model which is based on multiple linear regression and refers to a branch of applied statistics. Thus LINEST and controlling function were applied to improve the accuracy and the quality of the model. Various genesis of epithermal veins as well as host rock cause complication in the modeling process. Universities should address students' attitudes toward quantitative methods at the curriculum level. Implications for practice and/or policy: Knowledge acquisition of quantitative methods depends on instructional choices and students' overall academic performance. Students' attitudes toward quantitative methods courses change slowly similar with any other attitudesĬomputer‐assisted instruction is effective in cultural settings other than those in the western world Student attitudes have lower effect on knowledge acquisition than the introduction of computer‐assisted instruction or grade point average What this paper adds: Computer‐assisted instruction had higher impact on knowledge acquisition than students' overall academic performance Students' attitudes toward quantitative methods are overall negativeĬomputer‐assisted learning helps improve student attitudes in some cases Students' attitudes toward the subject matter are important for their knowledge acquisition What is already known about this topic: Computer‐assisted learning environments are more effective in teaching statistics than traditional lecture instructions For this purpose, the general syntax of the LINEST function is as. 1 Screen Shot 1: Next, use the LINEST function to obtain the least squares estimates for the intercept and slope parameters, based on the random sample just drawn. 2, is an estimate of the true regression line depicted in Fig. The fitted regression line, which runs between the two groups of points in Fig. 2) was drawn from the probability distribution function of food expenditure for income level x = $2000 (in the back in Fig. The second group of points from the scatter plot (on the right in Fig. 2) was drawn from the probability distribution function of food expenditure for income level x = $1000 (in the front in Fig. The first group of points from the scatter plot (on the left in Fig. The random sample that we obtain 6 is presented on Screen Shot 1 and its scatter plot on Fig. We Copy the formula from B6 into B7:B25 and from B26 into B27:B45. In cells B6 and B26 we enter the equations shown in Table 2. Having these, the final step is to generate a random weekly food expenditure value drawn from households with one of two weekly income ( x = $1000 or x = $2000) via the known linear function: y = b 1 + b 2 x + e, where b 1 = 100 and b 2 = 0.10.
interest here lies specifically in the normal distribution of random errors, e, with mean m = 0 and standard deviation of s = 50.
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