The History of Statistics

Origin of The Word
The word statistik comes from Italian word statista (meaning "statesman). It was first used by Gottfried Achenwall (1719-1772), a professor at Marlborough and Gottingen. Dr. E. A. W. Zimmerman introduced the word statistics into England. Its use was popularized by Sir John Sinclair in his work Statistical Account of Scotland 1791-1799. Long before the eighteenth century, people had been recording and using data.

Early Government Records
Official government statistics are as old as recorded history. The Old Testament contain several accounts of census taking. Governments of ancient Babylonia, Egypt, and Rome gathered detailed records of populations and resources. In the Middle Ages, governments began to register the ownership of land. In A.D. 762, Charlemagne asked for detailed descriptions of church-owned properties. Early in the ninth century, he completed a statistical enumeration of the serfs attached to the land. About 1086. William the Conqueror ordered the writing of Domesday Book, a record of the ownership, extent and value of the lands of England. This work was England's first statistical abstract.

An early prediction from statistics
Because of Henry VII's fear of the plague, England began to register its dead in 1532. About this same time, French law required the clergy to register baptisms, deaths and marriages. During an outbreak of the plague in the late 1500s, the English government started publishing weekly death statistics. This practice continued, and by 1632, these Bills of Mortality listed births and death by sex. In 1662, Captain John Graunt used 30 years of  these Bills to make predictions about the number of people who would die from various diseases and the proportions of male and female births that could be expected. Summarized in his work Natural and Political Observations... Made Upon the Bills of Mortality, Graunt's study was a pioneer effort in statistical analysis. For his achievement in using past records to predict future events, Graunt was made a member of the original Royal Society.
The history of the  .... (to be continued) :)
 

Simple Linear Correlation (Pearson r)

In the previous chapter, we have discuss about the meaning of Correlation (What is correlation?). Now we will further discuss about Simple Linear Correlation (in Bahasa it means Korelasi Linear Sederhana), known as Pearson r, because it was found by Carl Pearson, thus named Pearson Correlation. I post and publish this article to my blog, as a content of quantitative and statistical methods for management and distance learning business and management.

This article I quoted from statsoft... for further information you could visit their site, or maybe you have to is just take a sit, relax and try to understand about the topics...

Simple Linear Correlation (Pearson r). Pearson correlation (hereafter called correlation), assumes that the two variables are measured on at least interval scales (see Elementary Concepts), and it determines the extent to which values of the two variables are "proportional" to each other. The value of correlation (i.e., correlation coefficient) does not depend on the specific measurement units used; for example, the correlation between height and weight will be identical regardless of whether inches and pounds, or centimeters and kilograms are used as measurement units. Proportional means linearly related; that is, the correlation is high if it can be "summarized" by a straight line (sloped upwards or downwards).

Look at the picture below:

This line is called the regression line or least squares line, because it is determined such that the sum of the squared distances of all the data points from the line is the lowest possible. Note that the concept of squared distances will have important functional consequences on how the value of the correlation coefficient reacts to various specific arrangements of data (as we will later see).

How to Interpret the Values of Correlations. As mentioned before, the correlation coefficient (r) represents the linear relationship between two variables. If the correlation coefficient is squared, then the resulting value (r2, the coefficient of determination) will represent the proportion of common variation in the two variables (i.e., the "strength" or "magnitude" of the relationship). In order to evaluate the correlation between variables, it is important to know this "magnitude" or "strength" as well as the significance of the correlation.

Significance of Correlations. The significance level calculated for each correlation is a primary source of information about the reliability of the correlation. As explained before (see Elementary Concepts), the significance of a correlation coefficient of a particular magnitude will change depending on the size of the sample from which it was computed. The test of significance is based on the assumption that the distribution of the residual values (i.e., the deviations from the regression line) for the dependent variable y follows the normal distribution, and that the variability of the residual values is the same for all values of the independent variable x. However, Monte Carlo studies suggest that meeting those assumptions closely is not absolutely crucial if your sample size is not very small and when the departure from normality is not very large. It is impossible to formulate precise recommendations based on those Monte- Carlo results, but many researchers follow a rule of thumb that if your sample size is 50 or more then serious biases are unlikely, and if your sample size is over 100 then you should not be concerned at all with the normality assumptions. There are, however, much more common and serious threats to the validity of information that a correlation coefficient can provide; they are briefly discussed in the following paragraphs.

Okay my friends, I think enough for this time and I will continue it next time. Hopefully it can help you easily understand about simple linear correlation (Korelasi Linear Sederhana)... This is just a little notes about quantitative and statistical methods for management and distance learning business and management

"True" Mean and Confidence Interval

This article was taken from statsoft. I share it to you for distance learning business and management, so we could together learn and know it about True Mean and Confidence Interval. You can find this subject on Descriptive Statistics.
"True" Mean and Confidence Interval. Probably the most often used descriptive statistic is the mean. The mean is a particularly informative measure of the "central tendency" of the variable if it is reported along with its confidence intervals. As mentioned earlier, usually we are interested in statistics (such as the mean) from our sample only to the extent to which they can infer information about the population. The confidence intervals for the mean give us a range of values around the mean where we expect the "true" (population) mean is located (with a given level of certainty, see also Elementary Concepts). For example, if the mean in your sample is 23, and the lower and upper limits of the p=.05 confidence interval are 19 and 27 respectively, then you can conclude that there is a 95% probability that the population mean is greater than 19 and lower than 27. If you set the p-level to a smaller value, then the interval would become wider thereby increasing the "certainty" of the estimate, and vice versa; as we all know from the weather forecast, the more "vague" the prediction (i.e., wider the confidence interval), the more likely it will materialize. Note that the width of the confidence interval depends on the sample size and on the variation of data values. The larger the sample size, the more reliable its mean. The larger the variation, the less reliable the mean (see also Elementary Concepts). The calculation of confidence intervals is based on the assumption that the variable is normally distributed in the population. The estimate may not be valid if this assumption is not met, unless the sample size is large, say n=100 or more.

Discriminant Function Analysis

My article right now is about Discriminant Analysis. I quote it from the website, conclude and posting it to my blog. Hopefully it can be useful for all of us that studying statistics. Distance Learning Business and Management

General Purpose

Discriminant function analysis is used to determine which variables discriminate between two or more naturally occurring groups. For example, an educational researcher may want to investigate which variables discriminate between high school graduates who decide (1) to go to college, (2) to attend a trade or professional school, or (3) to seek no further training or education. For that purpose the researcher could collect data on numerous variables prior to students' graduation. After graduation, most students will naturally fall into one of the three categories. Discriminant Analysis could then be used to determine which variable(s) are the best predictors of students' subsequent educational choice.

A medical researcher may record different variables relating to patients' backgrounds in order to learn which variables best predict whether a patient is likely to recover completely (group 1), partially (group 2), or not at all (group 3). A biologist could record different characteristics of similar types (groups) of flowers, and then perform a discriminant function analysis to determine the set of characteristics that allows for the best discrimination between the types.

Computational Approach

Computationally, discriminant function analysis is very similar to analysis of variance (ANOVA). Let us consider a simple example. Suppose we measure height in a random sample of 50 males and 50 females. Females are, on the average, not as tall as males, and this difference will be reflected in the difference in means (for the variable Height). Therefore, variable height allows us to discriminate between males and females with a better than chance probability: if a person is tall, then he is likely to be a male, if a person is short, then she is likely to be a female.

We can generalize this reasoning to groups and variables that are less "trivial." For example, suppose we have two groups of high school graduates: Those who choose to attend college after graduation and those who do not. We could have measured students' stated intention to continue on to college one year prior to graduation. If the means for the two groups (those who actually went to college and those who did not) are different, then we can say that intention to attend college as stated one year prior to graduation allows us to discriminate between those who are and are not college bound (and this information may be used by career counselors to provide the appropriate guidance to the respective students).

To summarize the discussion so far, the basic idea underlying discriminant function analysis is to determine whether groups differ with regard to the mean of a variable, and then to use that variable to predict group membership (e.g., of new cases).

Analysis of Variance. Stated in this manner, the discriminant function problem can be rephrased as a one-way analysis of variance (ANOVA) problem. Specifically, one can ask whether or not two or more groups are significantly different from each other with respect to the mean of a particular variable. To learn more about how one can test for the statistical significance of differences between means in different groups you may want to read the Overview section to ANOVA/MANOVA. However, it should be clear that, if the means for a variable are significantly different in different groups, then we can say that this variable discriminates between the groups.

In the case of a single variable, the final significance test of whether or not a variable discriminates between groups is the F test. As described in Elementary Concepts and ANOVA /MANOVA, F is essentially computed as the ratio of the between-groups variance in the data over the pooled (average) within-group variance. If the between-group variance is significantly larger then there must be significant differences between means.

Multiple Variables. Usually, one includes several variables in a study in order to see which one(s) contribute to the discrimination between groups. In that case, we have a matrix of total variances and covariances; likewise, we have a matrix of pooled within-group variances and covariances. We can compare those two matrices via multivariate F tests in order to determined whether or not there are any significant differences (with regard to all variables) between groups. This procedure is identical to multivariate analysis of variance or MANOVA. As in MANOVA, one could first perform the multivariate test, and, if statistically significant, proceed to see which of the variables have significantly different means across the groups. Thus, even though the computations with multiple variables are more complex, the principal reasoning still applies, namely, that we are looking for variables that discriminate between groups, as evident in observed mean differences.

Stepwise Discriminant Analysis

Probably the most common application of discriminant function analysis is to include many measures in the study, in order to determine the ones that discriminate between groups. For example, an educational researcher interested in predicting high school graduates' choices for further education would probably include as many measures of personality, achievement motivation, academic performance, etc. as possible in order to learn which one(s) offer the best prediction.

Model. Put another way, we want to build a "model" of how we can best predict to which group a case belongs. In the following discussion we will use the term "in the model" in order to refer to variables that are included in the prediction of group membership, and we will refer to variables as being "not in the model" if they are not included.

Forward stepwise analysis. In stepwise discriminant function analysis, a model of discrimination is built step-by-step. Specifically, at each step all variables are reviewed and evaluated to determine which one will contribute most to the discrimination between groups. That variable will then be included in the model, and the process starts again.

Backward stepwise analysis. One can also step backwards; in that case all variables are included in the model and then, at each step, the variable that contributes least to the prediction of group membership is eliminated. Thus, as the result of a successful discriminant function analysis, one would only keep the "important" variables in the model, that is, those variables that contribute the most to the discrimination between groups.

F to enter, F to remove. The stepwise procedure is "guided" by the respective F to enter and F to remove values. The F value for a variable indicates its statistical significance in the discrimination between groups, that is, it is a measure of the extent to which a variable makes a unique contribution to the prediction of group membership. If you are familiar with stepwise multiple regression procedures, then you may interpret the F to enter/remove values in the same way as in stepwise regression.

Capitalizing on chance. A common misinterpretation of the results of stepwise discriminant analysis is to take statistical significance levels at face value. By nature, the stepwise procedures will capitalize on chance because they "pick and choose" the variables to be included in the model so as to yield maximum discrimination. Thus, when using the stepwise approach the researcher should be aware that the significance levels do not reflect the true alpha error rate, that is, the probability of erroneously rejecting H0 (the null hypothesis that there is no discrimination between groups).

Interpreting a Two-Group Discriminant Function

In the two-group case, discriminant function analysis can also be thought of as (and is analogous to) multiple regression (see Multiple Regression; the two-group discriminant analysis is also called Fisher linear discriminant analysis after Fisher, 1936; computationally all of these approaches are analogous). If we code the two groups in the analysis as 1 and 2, and use that variable as the dependent variable in a multiple regression analysis, then we would get results that are analogous to those we would obtain via Discriminant Analysis. In general, in the two-group case we fit a linear equation of the type:

Group = a + b1*x1 + b2*x2 + ... + bm*xm

where a is a constant and b1 through bm are regression coefficients. The interpretation of the results of a two-group problem is straightforward and closely follows the logic of multiple regression: Those variables with the largest (standardized) regression coefficients are the ones that contribute most to the prediction of group membership.

Discriminant Functions for Multiple Groups

When there are more than two groups, then we can estimate more than one discriminant function like the one presented above. For example, when there are three groups, we could estimate (1) a function for discriminating between group 1 and groups 2 and 3 combined, and (2) another function for discriminating between group 2 and group 3. For example, we could have one function that discriminates between those high school graduates that go to college and those who do not (but rather get a job or go to a professional or trade school), and a second function to discriminate between those graduates that go to a professional or trade school versus those who get a job. The b coefficients in those discriminant functions could then be interpreted as before.

Canonical analysis. When actually performing a multiple group discriminant analysis, we do not have to specify how to combine groups so as to form different discriminant functions. Rather, you can automatically determine some optimal combination of variables so that the first function provides the most overall discrimination between groups, the second provides second most, and so on. Moreover, the functions will be independent or orthogonal, that is, their contributions to the discrimination between groups will not overlap. Computationally, you will perform a canonical correlation analysis (see also Canonical Correlation) that will determine the successive functions and canonical roots (the term root refers to the eigenvalues that are associated with the respective canonical function). The maximum number of functions will be equal to the number of groups minus one, or the number of variables in the analysis, whichever is smaller.

Interpreting the discriminant functions. As before, we will get b (and standardized beta) coefficients for each variable in each discriminant (now also called canonical) function, and they can be interpreted as usual: the larger the standardized coefficient, the greater is the contribution of the respective variable to the discrimination between groups. (Note that we could also interpret the structure coefficients; see below.) However, these coefficients do not tell us between which of the groups the respective functions discriminate. We can identify the nature of the discrimination for each discriminant (canonical) function by looking at the means for the functions across groups. We can also visualize how the two functions discriminate between groups by plotting the individual scores for the two discriminant functions (see the example graph below).

In this example, Root (function) 1 seems to discriminate mostly between groups Setosa, and Virginic and Versicol combined. In the vertical direction (Root 2), a slight trend of Versicol points to fall below the center line (0) is apparent.

Factor structure matrix. Another way to determine which variables "mark" or define a particular discriminant function is to look at the factor structure. The factor structure coefficients are the correlations between the variables in the model and the discriminant functions; if you are familiar with factor analysis (see Factor Analysis) you may think of these correlations as factor loadings of the variables on each discriminant function.

Some authors have argued that these structure coefficients should be used when interpreting the substantive "meaning" of discriminant functions. The reasons given by those authors are that (1) supposedly the structure coefficients are more stable, and (2) they allow for the interpretation of factors (discriminant functions) in the manner that is analogous to factor analysis. However, subsequent Monte Carlo research (Barcikowski & Stevens, 1975; Huberty, 1975) has shown that the discriminant function coefficients and the structure coefficients are about equally unstable, unless the n is fairly large (e.g., if there are 20 times more cases than there are variables). The most important thing to remember is that the discriminant function coefficients denote the unique (partial) contribution of each variable to the discriminant function(s), while the structure coefficients denote the simple correlations between the variables and the function(s). If one wants to assign substantive "meaningful" labels to the discriminant functions (akin to the interpretation of factors in factor analysis), then the structure coefficients should be used (interpreted); if one wants to learn what is each variable's unique contribution to the discriminant function, use the discriminant function coefficients (weights).

Significance of discriminant functions. One can test the number of roots that add significantly to the discrimination between group. Only those found to be statistically significant should be used for interpretation; non-significant functions (roots) should be ignored.

Summary. To summarize, when interpreting multiple discriminant functions, which arise from analyses with more than two groups and more than one variable, one would first test the different functions for statistical significance, and only consider the significant functions for further examination. Next, we would look at the standardized b coefficients for each variable for each significant function. The larger the standardized b coefficient, the larger is the respective variable's unique contribution to the discrimination specified by the respective discriminant function. In order to derive substantive "meaningful" labels for the discriminant functions, one can also examine the factor structure matrix with the correlations between the variables and the discriminant functions. Finally, we would look at the means for the significant discriminant functions in order to determine between which groups the respective functions seem to discriminate.
Assumptions

As mentioned earlier, discriminant function analysis is computationally very similar to MANOVA, and all assumptions for MANOVA mentioned in ANOVA/MANOVA apply. In fact, you may use the wide range of diagnostics and statistical tests of assumption that are available to examine your data for the discriminant analysis.

Normal distribution. It is assumed that the data (for the variables) represent a sample from a multivariate normal distribution. You can examine whether or not variables are normally distributed with histograms of frequency distributions. However, note that violations of the normality assumption are usually not "fatal," meaning, that the resultant significance tests etc. are still "trustworthy." You may use specific tests for normality in addition to graphs.

Homogeneity of variances/covariances. It is assumed that the variance/covariance matrices of variables are homogeneous across groups. Again, minor deviations are not that important; however, before accepting final conclusions for an important study it is probably a good idea to review the within-groups variances and correlation matrices. In particular a scatterplot matrix can be produced and can be very useful for this purpose. When in doubt, try re-running the analyses excluding one or two groups that are of less interest. If the overall results (interpretations) hold up, you probably do not have a problem. You may also use the numerous tests available to examine whether or not this assumption is violated in your data. However, as mentioned in ANOVA/MANOVA, the multivariate Box M test for homogeneity of variances/covariances is particularly sensitive to deviations from multivariate normality, and should not be taken too "seriously."

Correlations between means and variances. The major "real" threat to the validity of significance tests occurs when the means for variables across groups are correlated with the variances (or standard deviations). Intuitively, if there is large variability in a group with particularly high means on some variables, then those high means are not reliable. However, the overall significance tests are based on pooled variances, that is, the average variance across all groups. Thus, the significance tests of the relatively larger means (with the large variances) would be based on the relatively smaller pooled variances, resulting erroneously in statistical significance. In practice, this pattern may occur if one group in the study contains a few extreme outliers, who have a large impact on the means, and also increase the variability. To guard against this problem, inspect the descriptive statistics, that is, the means and standard deviations or variances for such a correlation.

The matrix ill-conditioning problem. Another assumption of discriminant function analysis is that the variables that are used to discriminate between groups are not completely redundant. As part of the computations involved in discriminant analysis, you will invert the variance/covariance matrix of the variables in the model. If any one of the variables is completely redundant with the other variables then the matrix is said to be ill-conditioned, and it cannot be inverted. For example, if a variable is the sum of three other variables that are also in the model, then the matrix is ill-conditioned.

Tolerance values. In order to guard against matrix ill-conditioning, constantly check the so-called tolerance value for each variable. This tolerance value is computed as 1 minus R-square of the respective variable with all other variables included in the current model. Thus, it is the proportion of variance that is unique to the respective variable. You may also refer to Multiple Regression to learn more about multiple regression and the interpretation of the tolerance value. In general, when a variable is almost completely redundant (and, therefore, the matrix ill-conditioning problem is likely to occur), the tolerance value for that variable will approach 0.

Classification

Another major purpose to which discriminant analysis is applied is the issue of predictive classification of cases. Once a model has been finalized and the discriminant functions have been derived, how well can we predict to which group a particular case belongs?

A priori and post hoc predictions. Before going into the details of different estimation procedures, we would like to make sure that this difference is clear. Obviously, if we estimate, based on some data set, the discriminant functions that best discriminate between groups, and then use the same data to evaluate how accurate our prediction is, then we are very much capitalizing on chance. In general, one will always get a worse classification when predicting cases that were not used for the estimation of the discriminant function. Put another way, post hoc predictions are always better than a priori predictions. (The trouble with predicting the future a priori is that one does not know what will happen; it is much easier to find ways to predict what we already know has happened.) Therefore, one should never base one's confidence regarding the correct classification of future observations on the same data set from which the discriminant functions were derived; rather, if one wants to classify cases predictively, it is necessary to collect new data to "try out" (cross-validate) the utility of the discriminant functions.

Classification functions. These are not to be confused with the discriminant functions. The classification functions can be used to determine to which group each case most likely belongs. There are as many classification functions as there are groups. Each function allows us to compute classification scores for each case for each group, by applying the formula:

Si = ci + wi1*x1 + wi2*x2 + ... + wim*xm

In this formula, the subscript i denotes the respective group; the subscripts 1, 2, ..., m denote the m variables; ci is a constant for the i'th group, wij is the weight for the j'th variable in the computation of the classification score for the i'th group; xj is the observed value for the respective case for the j'th variable. Si is the resultant classification score.

We can use the classification functions to directly compute classification scores for some new observations.

Classification of cases. Once we have computed the classification scores for a case, it is easy to decide how to classify the case: in general we classify the case as belonging to the group for which it has the highest classification score (unless the a priori classification probabilities are widely disparate; see below). Thus, if we were to study high school students' post-graduation career/educational choices (e.g., attending college, attending a professional or trade school, or getting a job) based on several variables assessed one year prior to graduation, we could use the classification functions to predict what each student is most likely to do after graduation. However, we would also like to know the probability that the student will make the predicted choice. Those probabilities are called posterior probabilities, and can also be computed. However, to understand how those probabilities are derived, let us first consider the so-called Mahalanobis distances.

Mahalanobis distances. You may have read about these distances in other parts of the manual. In general, the Mahalanobis distance is a measure of distance between two points in the space defined by two or more correlated variables. For example, if there are two variables that are uncorrelated, then we could plot points (cases) in a standard two-dimensional scatterplot; the Mahalanobis distances between the points would then be identical to the Euclidean distance; that is, the distance as, for example, measured by a ruler. If there are three uncorrelated variables, we could also simply use a ruler (in a 3-D plot) to determine the distances between points. If there are more than 3 variables, we cannot represent the distances in a plot any more. Also, when the variables are correlated, then the axes in the plots can be thought of as being non-orthogonal; that is, they would not be positioned in right angles to each other. In those cases, the simple Euclidean distance is not an appropriate measure, while the Mahalanobis distance will adequately account for the correlations.

Mahalanobis distances and classification. For each group in our sample, we can determine the location of the point that represents the means for all variables in the multivariate space defined by the variables in the model. These points are called group centroids. For each case we can then compute the Mahalanobis distances (of the respective case) from each of the group centroids. Again, we would classify the case as belonging to the group to which it is closest, that is, where the Mahalanobis distance is smallest.

Posterior classification probabilities. Using the Mahalanobis distances to do the classification, we can now derive probabilities. The probability that a case belongs to a particular group is basically proportional to the Mahalanobis distance from that group centroid (it is not exactly proportional because we assume a multivariate normal distribution around each centroid). Because we compute the location of each case from our prior knowledge of the values for that case on the variables in the model, these probabilities are called posterior probabilities. In summary, the posterior probability is the probability, based on our knowledge of the values of other variables, that the respective case belongs to a particular group. Some software packages will automatically compute those probabilities for all cases (or for selected cases only for cross-validation studies).

A priori classification probabilities. There is one additional factor that needs to be considered when classifying cases. Sometimes, we know ahead of time that there are more observations in one group than in any other; thus, the a priori probability that a case belongs to that group is higher. For example, if we know ahead of time that 60% of the graduates from our high school usually go to college (20% go to a professional school, and another 20% get a job), then we should adjust our prediction accordingly: a priori, and all other things being equal, it is more likely that a student will attend college that choose either of the other two options. You can specify different a priori probabilities, which will then be used to adjust the classification of cases (and the computation of posterior probabilities) accordingly.

In practice, the researcher needs to ask him or herself whether the unequal number of cases in different groups in the sample is a reflection of the true distribution in the population, or whether it is only the (random) result of the sampling procedure. In the former case, we would set the a priori probabilities to be proportional to the sizes of the groups in our sample, in the latter case we would specify the a priori probabilities as being equal in each group. The specification of different a priori probabilities can greatly affect the accuracy of the prediction.

Summary of the prediction. A common result that one looks at in order to determine how well the current classification functions predict group membership of cases is the classification matrix. The classification matrix shows the number of cases that were correctly classified (on the diagonal of the matrix) and those that were misclassified.

Another word of caution. To reiterate, post hoc predicting of what has happened in the past is not that difficult. It is not uncommon to obtain very good classification if one uses the same cases from which the classification functions were computed. In order to get an idea of how well the current classification functions "perform," one must classify (a priori) different cases, that is, cases that were not used to estimate the classification functions. You can include or exclude cases from the computations; thus, the classification matrix can be computed for "old" cases as well as "new" cases. Only the classification of new cases allows us to assess the predictive validity of the classification functions (see also cross-validation); the classification of old cases only provides a useful diagnostic tool to identify outliers or areas where the classification function seems to be less adequate.

Summary. In general Discriminant Analysis is a very useful tool (1) for detecting the variables that allow the researcher to discriminate between different (naturally occurring) groups, and (2) for classifying cases into different groups with a better than chance accuracy.

Source: Statsoft

Testing Hypothesis and Sampling on Research Methods

Download the newest and free e-book about testing hypothesis and sampling on research methods, kindly please click here.
If you have any comments, please post your comment in this blogs. I will reply and answer it as soon as possible. Just another my free e-book on Distance Learning Business and Management. Quantitative and Statistics Methods for Management.

Practicing Time Series on Minitab

Howdy my friends... This time I produldy present my newest e-book about Practicing Time Series on Minitab. I hope this e-book can helps you more understand about Time Series.
To download The Time Series on Minitab e-book, kindly please click HERE

Tutorial Installing Smartpls Software

Based on the information that contained on the smartpls websites, known that SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). The partial least squares (PLS)-method is used for the LVP-analysis in this software.


In the download area, the first beta-version is accessible (free of charge). A registration is required! The following new features are presented in the new release SmartPLS 2.0 (beta):

• a completely re-engineered software application using the JAVA Eclipse Platform,

• the option to easily extend the functionality of SmartPLS by JAVA Eclipse Plug-ins, and

• a SmartPLS community to discuss all software and PLS related topics with other users and experts.

This is very useful software for do some research, especially if you want to do research in management. How to get this software? Based on information on the site, these are 4 steps how to get smartpls 2:

Step 1
Register with your TRUE IDENTITY in the SmartPLS Forum. You receive an E-Mail with your USSERNAME and PASSWORD.

Step 2
Your registration is CHECKED by the administrators. If approved (this is usually the case) your profile is ACTIVATED and you receive another E-Mail with your ACTIVATION KEY for the SmartPLS 2 software application.

Step 3
Log into the SmartPLS Forum (with your USSERNAME and PASSWORD) and get the SmartPLS 2 software application, sample data files, sample models as well as a video based user manual in the DOWNLOAD area.

Step 4
INSTALL/UNZIP the application on your computer system and START it. For the first start, you need to copy and paste your ACTIVATION KEY (that you received via E-Mail) into the SmartPLS 2 activation window.

Why register?

The SmartPLS community platform hosts different forums in order to provide information on SmartPLS and PLS-based LVP. These forums are not accessible for non registered persons. We insist on a “real world” user identity to ensure the professional and profound exchange of information.

Since summer 2010, the SmartPLS software application for PLS path modeling has more than 20,000 registered users.

If you want to register it now, kindly please click here
Till I post this article, I still try to understanding this software. One important thing before you run this application, you should have installing Java Environment on your computer or notebook. I try to search the handbook of smartpls on the internet. The best handbook of smartpls I will share it on my blog as soon as possible. Thanks

Introduction to Quantitative Analysis

After completing this chapter, you will be able to:

• Describe the quantitative analysis approach
• Understand the application of quantitative analysis in a real situation
• Describe the use of modeling in quantitative analysis
• Use computers and spreadsheet models to perform quantitative analysis
• Discuss possible problems in using quantitative analysis
• Perform a break-even analysis

Introduction

• Mathematical tools have been used for thousands of years
  Quantitative analysis can be applied to a wide variety of problems
  It’s not enough to just know the mathematics of a technique
  One must understand the specific applicability of the technique, its limitations, and its assumptions

Examples of Quantitative Analyses

• Dzikri Cell company saved over $150 million using forecasting and scheduling quantitative analysis models
• XYZ television increased revenues by over $200 million by using quantitative analysis to develop better sales plans
• Ali Baba Airlines saved over $40 million using quantitative analysis models to quickly recover from weather delays and other disruptions

What is Quantitative Analysis?
Quantitative analysis is a scientific approach to managerial decision making whereby raw data are processed and manipulated resulting in meaningful information

• Quantitative factors might be different investment alternatives, interest rates, inventory levels, demand, or labor cost
• Qualitative factors such as the weather, state and federal legislation, and technology breakthroughs should also be considered

Information may be difficult to quantify but can affect the decision-making process
Defining the Problem
Need to develop a clear and concise statement that gives direction and meaning to the following steps
This may be the most important and difficult step

• It is essential to go beyond symptoms and identify true causes
• May be necessary to concentrate on only a few of the problems – selecting the right problems is very important
• Specific and measurable objectives may have to be developed

Developing a Model
Quantitative analysis models are realistic, solvable, and understandable mathematical representations of a situation

• Models generally contain variables (controllable and uncontrollable) and parameters
• Controllable variables are generally the decision variables and are generally unknown
• Parameters are known quantities that are a part of the problem

Acquiring Input Data

Input data must be accurate – GIGO rule
Data may come from a variety of sources such as company reports, company documents, interviews, on-site direct measurement, or statistical sampling

Training Ourselves Patience

Have you ever heard the term "Patience People loved the Lord"? I am sure you must have heard it before. My article today is about patience and how to train ourselves patience. I though this article from various sources on the internet. Hopefully this post can give benefit to me and to you.
Patience is an important trait that allows us to persevere and remain focused in matters of daily living. Here are some exercises to help you train your patience.
Steps
First Exercise:
1. Take a piece of cardboard, about the size of a large sheet of 8x11 in paper.
2. Cut a hole in the cardboard, about the size of an ink-stain (about an inch in diameter)
3. Seat yourself comfortably, then take the cardboard by the lower rim between your thumb and index finger, the arm well extended out from the body
4. Look through the ink-stain on the paper so the stain is exactly enclosed, with none of the white surface appearing to the eye. Focus all your attention on the hole you made, and let not a speck of white appear to your eyes.
5. For the first few times you practice this, do not exceed twenty seconds. After you gain more experience, you may extend the time, little by little. Do not exceed 20 seconds on your first few trials.
6. The key is to be very exact in this experiment, with no speck of white appearing to the eye during the exercise

Second Exercise:
1. Similar to the First Exercise, only this time take a sheet of paper instead of a piece of cardboard.
2. Do as in the First Exercise. You must try to keep the paper stiff while trying to focus your eyes on the hole. If the paper bends, try the exercise over again. Do the same exercise as in the First Exercise, but with the additional challenge of keeping the paper stiff.

Third Exercise:
1. Trace a straight horizontal line on a wall
2. Fill a glass of water to a third of an inch of the brim, and hold it toward the bottom, so that the water will be on a level with the line traced on the wall
3. You must ensure that the surface of the water will not deviate from the line
4. Hold the glass at arm's length from the body till you fell tired.
5. With practice, this exercise can be done without the slightest stirring of water. Gradually increase the duration you can do this

Fourth Exercise:
1. Fill the glass now to the very top and repeat the Third Exercise. Now fill the glass full of water...
2. If the water overflows, try again

Fifth Exercise:
1. After filling the glass to the brim, hold it up from the bottom by the thumb and fourth finger for two seconds. Fill the glass full to the brim
2. Move it slowly from right to left, keeping its brim level with the line on the wall.

After five exercises that I inform you, now I will inform you, some tips are useful in trying the exercises that I has been give to you.
TIPS:
1. The very first few times you try these exercises, you might fail. Do not let it discourage you. Try again.
2. Perseverance in trying these exercises will eventually help you overcome your nervousness, as you will have much occasion dealing with this during your repeated trials

Alright, I think enough for this times. Hopefully I can continue again next time, sharing knowledge to you all, as a part of my purposes to make this Distance Learning Business and Management blogs.

Linguistic devices (Part 1)

Linguistic devices
Like most languages in the world, Indonesian language (Bahasa Indonesia) written in Latin characters. There are two known forms of the Latin alphabet, ie roman and italic letters. Latin characters can be displayed in a thin, thick and capital.
Roman letters always stand up straight so that the handwriting is so often referred to as "printed". In the world of printing and typing this letter shapes that always used the principle of obedience. Unless otherwise specified, roman letters (especially those who look skinny), almost always can be used for anything else.
Italics, or italic known, is shown in italic letters and shapes, such as handwriting. Italics are also called cursive letters. If typed or handwritten, the slope is marked by a single bottom line. Italics are used in the nine following:
1. Foreign words and phrases in many languages to survive spelling: ad hoc, et al, in vitro
2. Constants and variables that are unknown in mathematics. For example (x, y, l)
3. Name of ship or satellite. For example KRI Macan Tutul, Apollo 11
4. The word or term that was introduced for a special discussion, for example kakas, citraan
5. The word or phrase that is emphasized, for example ... it is not justified (hal itu tidak dibenarkan)
6. notice of cross-references in the index: see (lihat), see also (lihat juga)
7. Titles of books or periodicals mentioned in the article body: Biological
8. Artificial sounds: From the nest of the bird's chirping sound tu-ju-pu-lu-tu-ju-pu-lu
9. scientific names, such as genus, species, varieties and form creature: Salacca zalacca var, amboinese. however, the scientific name of taxon above genus level was not written with italic letters: Felidae, Moraceae, Mucorales.

Thus we learn the material this time I publish on my Distance Learning Business and Management blogs, hopefully we can continue on a future occasion.

A Robbery incident with hypnotic mode in Bandar Lampung Indonesia

Some day I saw the news on television that is on TVOne, about a robbery incident with hypnotic mode. Television broadcast CCTV footage of robbery incident that occurred in Bandar Lampung in a very clear mini market.Seen on tape, there are some people who entered a supermarket in groups, then do a hypnotist to employees at the supermarket. As a result, the employees had become unconscious, so obey all the desire of these criminals. Flock criminals asked employees of mini market to spend money in the safe box, not only that, the criminals also took some stuff from his store.

I read the latest news on the internet aware that Metro Jaya Regional Police are still tracing herd suspected of committing a series of robberies with a hypnotic method some time ago in Bandar Lampung. They are citizens of foreign origin of Syria and Iran, named Memed Sahdi and Hedi Waridan. "Both men," said Head of Public Relations Polda Metro Jaya, Kombes Pol. Boy Rafli Amar on Thursday, August 26, 2010.

While undergoing the process of police investigation, two suspects chose silence. "Still want to talk, do not acknowledge as existing players such as the CCTV footage. So, we still find out," as presented by the Boy. Meanwhile, a woman who suspects caught on CCTV camera wearing a white veil, still chased by the police. Its identity was known to the authorities. His name initials 'A'.
To be spared from evil hypnosis, we must continuously enhance our faith in God. For a Muslim, this can be done with the dhikr of Allah SWT always, pray and ask God Almighty that we should always be protected from harm. Dhikr is a personal shield for a Muslim. Indonesia Hot News so this time I publish on the blog of Business and Management Distance Learning

Fast Learning and Understanding Science With Online Tutorials

Education is so important for our life, so we have to study well for our bright future. Studying science in biology, chemistry and natural science is very interesting and fun. However, not infrequently we hear the other person feel difficulty in understanding it. I think this is fair enough, because sometimes on some of the discussion was very difficult and need guidance to learn well and exactly right. Some of student complained about the difficulty of doing science homework, so they desperately need science homework help so they can complete learning tasks so getting satisfactory value from their teachers or lecturers. Some of my friends also had told me that they sometimes have difficulties in understanding science. Even they also say if they really want to learn not only about science but also about balancing equations because it is very interesting science to be studied and understood.
I personally also love the science of learning about the Ionic compounds, I often read books that can explain about it. But sometimes I also find it difficult to understand, because the study relied only accompanied by a book without a reliable tutor, I feel pretty difficult. My partner is somewhat different, he told me that he is interested to learn Nitric acid. My colleague told me that she finds very enjoyable way of learning, is online learning guided by expert tutors. My colleague advised me to visit the site tutorvista.com. He said this site has an excellent tutorial services and view an interesting site. I was curious and tried to visit this site several days later. Wow very interesting concept of educational tutorials that they offer to their customers. Where previously I had difficulty in learning, then I am sure through the tutorial services from this site, all my difficulties in learning will be resolved.
My lecturer once asked me about the term Homogeneous mixture, then after I visited this site, I can do an online consultation so that I can find answers to questions submitted by my lecturer. Amazing, this site is truly amazing ... not only that .. This site can also give information to me about Heterogeneous mixture. View of the tremendous benefits offered by this site to the tutorials you who need guidance in education from tutors who are skilled, I highly recommend you to immediately visit the site. However, it is before you decide to join you must have really read in detail all the rules they set in those sites. I think with this online tutorial, you will get fast learning and understanding science that absolutely will makes your future brighter.. I hope so, Insya Allah.

Online Tutorial, Practical and Fun

We often hear and see people who complain and find it difficult to learn and understand the quantitative sciences such as mathematics, algebra, calculus and physics. This may be because the field of quantitative science requires high concentration and high intelligence or intellectual. Difficulty in learning and understanding person field of quantitative science such as the above, I think is caused by several things, that is as follows:
1. Boring teaching methods
2. Teachers who are not professional
3. Learning tool or a means of limited education
Currently the development of computer information technology and the Internet, growing very rapidly. I think this can have positive impacts on the system and ways of learning a quantitative science (mathematics, algebra, calculus and physics), becomes easier, more enjoyable, and relatively cheaper cost. One way of learning and the educational system currently being developed is a method of distance education using the Internet. A few days ago I get information about a site that organizes online education in the field of quantitative science. When I get that information, I immediately visited and read some of the information contained on the site. The site was named tutornext.com. From the domain name alone we may be able to guess that the site is a site which provides services in various fields of science tutorial education. This website provides Math Help those of us who need guidance in science and math tutorials, in a more effective and more efficient. Math Help Through this service, we will be guided by expert tutors and professionals in the field of mathematics, so that Math Problems that we face will be resolved properly.
Students and students who are educated, can not be separated from the homework that is assigned study given by a teacher or lecturer. Tutornext.com site also provides Homework Help, a service that is very modern. The students and students who have difficulty in doing his homework, will be ameliorated by effective and efficient so that homework can be completed properly. Not only a math tutorial services and homework are offered by this site, tutorial services in other sciences such as algebra, calculus, and physics is also an advantage of educational tutorial services on this site.
Great and amazing ... we can learn a lot of science with just using the services provided by this site. If you need algebra help then you will get help in the field of algebra from professional tutors from this site. This site guarantees to provide their best services to assist your learning. Not only the algebra, and even if you need calculus help, then you will get the best tutorial services from this site. I watched the look of this site in detail, and I think this site has an attractive appearance and easy to understand. Very impressive ... I got fascinated by seeing the benefits of the program of education tutorial services offered by this site, even they also can give physics help services with professional and satisfying its customers. I highly recommend you to visit this site, and find as much information in accordance with your needs.

A Small Note About Tsuki, am I right?

A few days ago, I was curious at a Yahoo Messenger's account from my smart student Amelia. Amelia has a short name Amelia_Tsuki, I wondered who exactly was Tsuki?. As usual, I find out the information on the internet. After a few minutes, finally I found a little note about Tsuki. A small note about "Rabbit in the Moon", have a nice time to read this simple article.

When I started reading stories about Tsuki, I remember with a Japanese cartoon movie titled "Sailormoon". The main actors of "Sailormoon" is a girl who has the name Usagi Tsukino (in Japanese read Tsukino Usagi). I just realized that the Usagi always have items with pictures of rabbits, it was because Usagi is a rabbit in Japanese.

Usagi Tsukino is an ordinary girl before she was given the power to be Sailormoon. And as Sailormoon, she and the other sailor friends struggled to defend the truth, with the power of the moon. The famous words of Usagi / Sailormoon is "By the power of the moon, will punish you!".

At the end of the story, Sailormoon transformed into Princess Serenity (eventually became Queen Serenity), daughter of the Kingdom of the Moon. For information, Serenity is the name of the largest lakes on the Moon.

As shown, the name and location of characters in this story are related. The family name is Usagi Tsuki (Moon), he is a Sailormoon, and transformed into Princess Serenity. So what to do with Usagi (rabbit)? This question actually has arisen in my mind ten years ago, and I immediately knew the answer given by a friend.

Usagi's name is Tsukino Usagi. If we ignore the form of the kanji letters (frankly, I do not know how the form of starch from Tsukino Usagi) we will see a fragment of the word Tsuki no Usagi, or, if interpreted in Bahasa Indonesian to Rabbit Moon. Japanese people always see the RABBIT in MONTHS. Do not believe? please you see for yourself!. I've included pictures so you can look at the moon closely to find a rabbit in there.

When the Japanese see a rabbit in the moon, the Maduraneese to see a widow who was sewing in there. The widow is a mother who abandoned her child, her mother missed her son so he decided to look for her son. However, although the mother is looking for it everywhere, her son never found. Finally, he decided to sit down and sew on the moon, waiting for his son saw it and returned home to her.

Indeed cultural differences lead to differences in circulating myths. However, it does not matter as long as we can understand the moral message brought by the myths it.

Well ... now I say, "good-looking rabbit and a widow who was sewing in the month.

Note small and light to answer the curiosity of a small name Amelia_Tsuki. Was this article is correct?
Distance Learning Business and Management 

Secrets of Starting Your Own Businesses

Science will care for us, while money may not be able to keep us, that's the message our parents first. Whether any of these skills, which obviously you have to have it. You will be more useful and appreciated by society because they have expertise.

As a simple example, if you master computer then you can empower your abilities and skills, not just to survive, but possibly as a permanent business opportunity or home business that you can develop and you can even inheritance to their children and grandchildren someday. Home business opportunity, why not? But at least you will not be a problem for anyone, let alone expect any help from such countries as the crisis hit in 1998, where the government is forced to create a social safety net policies which as we all know, the edges become sources of corruption the people who irresponsible.

Okay if you just think this is now making home business opportunities are limited in order to survive and to remain productive while looking for business opportunities or a career in one company that you think suits your interests and skills you possess. Business opportunities that you can do based on your ability in the field of computers is with a private tutor to various schools, institutions, companies or individuals. Your ability in a particular subject you can also take advantage of a small capital home-based opportunities. Moreover, you have mastered a subject which is generally considered difficult. It is also an opportunity and a small capital business opportunities. Being a small capital home-based business tentor subjects Mathematics, Physics, Chemistry, music, computer and English is required. Please feel free to learn and open it again when school textbook first. Being tentor also not much need of capital. The most crucial for you have is a communication tool that can be reached wherever you are. Besides, with a small capital you have, you certainly have to prepare the syllabus and curriculum may also be in accordance with the needs of your customers.

Then how do I attract customers your home business? There may be a good idea to contact someone you know well from the school / college, start a blog and put an ad there, put an ad in a certain daily newspaper. Spread the brochure to the school, institution, or company. In a strategy to develop housing opportunities small capital, you can also put flyers in stores, food stalls, public announcements walls, and so forth. Create a need, people will not be needed if you do not even advertise it with just a small capital.

When you get a good response, it is not likely you'll be able to provide home-based business opportunities small capital to your colleagues who are also experiencing the same problem with you. Therefore, in this small capital businesses create a network that will be very beneficial for you and your colleagues. This simple example, if you are asked to give lessons in the hours that conflict with your schedule, then you can deliver Tell someone is available at the time. Thus, with prior agreement of course, you can get the fee from your colleagues. That means as a kind of cooperation mutually beneficial symbiosis or mutual ism as a term in the field of Biology. But it would be great for thinking like this; remember a wise messages; reap what you sow.
That's all for this time, I will continue the lessons about Distance Learning Business and Management next time 

The best dictionary of American English

If we learn about English, we are also learning about reading comprehension. Recently I read a book about reading comprehension that included a lot of section. Some of the section is designed to measure our ability to read and understand short passages similar in topic and style to those that students are likely to encounter in North American universities and colleges. I often was facing a diagnostic pre-test about American English, so that why I need the best dictionary of American English. I need it because I recently read books that English literate
To understand the story I really need an American dictionary, because the story was written on American English style. Nowadays I have discussed a nice topic about the game of Mount Everest puzzle. My friends are the owner of the game and I often borrow it. I discuss it with my friends and especially about crossword puzzle that included on the game.  My friends told me that to get easier learning about American English that often being there in any literate, we also have to know about audio dictionary. With helps from audio dictionary we can easier to learn and knowing about American English.
My friends told me that they know a good dictionary of American English. on the internet that will helps us understand about American English by online. It is amazing for me to get more exploration from the expert and I can get a perfect answer from any of my questions about the most searched words. So I strongly recommend to you to visit Lexiology.com you will find all what you want about the best dictionary of American English ever have.

Free Statistics Help from a professional tutor

For some students, probably learning a few objects could be so difficult so they have problems with it. A few objects that students recently feel so difficult to study are about statistics. If that happen to you or when you have trouble with statistic is one thing you must do it of neglect. However, this would not be a solution to your problem when you ignore it. For that you should try to improve your statistics skills, why I said that because the statistics is the object lesson is really important. You might not get a passing grade statistics academic if you are lazy to learn it. That's why nowadays many people who decide to learn statistics outside of school hours by various methods. Some people said that to learn statistics outside of school hours is known as distance learning. As a student if we had a problem with one of object that we learning about, or if we feel that learning statistics is too difficult, it is mean that we need statistics help from a professional statistics tutor.

Maybe sometimes we do not understand the explanation of our teacher in front of the class. For that we can have the own tutor to learn and the tutor is must be a professional tutor.  Statistics are gives us a knowledge about grouping and displaying data, measures of central tendency and dispersion, probability, estimation, testing hypotheses and many more. It is so impossible if we do not have statistics problems. That’s why we need a professional statistics tutor, it can gives us solution about all of our statistics questions. 

It is so amazing if we could get a professional statistics tutor that can give us the solution about our statistics questions with a perfect statistics answer directly from the expert. Last night when I browse the internet, I found an amazing and incredibly website that could be our solution about statistics help. Wow, it is so amazing. That website could give us a perfect statistics answer and they also give us free statistics helps. I have a big hope that with the helps from the exact expert, we can more easier to learn statistics and we will get a better score in the school. That's why I strongly recommend you to visit tutorvista.com and discover all the amazing of learning statistics on that site.

Schedule Planning For My Student

Do you want to download schedule planning for your study?

It just need a simple steps, Please Click Here

Note: the schedules were still draft, for further information you can contact secretariat, thanks. See you on the next Distance Learning Business and Management

Mario Teguh Golden Ways - The Power Of Imaginary Regret

Watch the lesson from Mario Teguh (famous motivator instructor in Indonesia). This lesson gives us the easy ways to understand about The Power of Imaginary Regret. Hopefully we can take the goodness from this lesson and never stop to study on any way including by distance learning business and management ways.

The Power of Imaginary Regret (Part 1)

 

The Power of Imaginary Regret (Part 2)



The Power of Imaginary Regret (Part 3)



The Power of Imaginary Regret (Part 4)



The Power of Imaginary Regret (Part 5)

Distance Learning TOEFL Test (Part 1)

Lets learn TOEFL by Distance Learning Business and Management. I named this lesson is Distance Learning TOEFL Test.
First one please allow me to write about TOEFL Test Introduction.
From a few source that I search it on the web i found this nice description about TOEFL. The TOEFL test is taken by students who are interested in furthering their school (academic) careers and want to demonstrate a proficiency in North American English. The competitiveness of school admissions dictates a good TOEFL score as a minimum to even be considered by some colleges. The TOEFL test covers 3 content areas, that 3 content areas are: Writing, Reading, and Listening. On TOEFL test You will not find social studies, chemistry, physics and biology, unless a few of these topics are covered indirectly on the sections. Hmmm.. interesting right???

All right now we continue our lesson. The TOEFL test is designed to be one of the first hurdles in your school (academic) career. Consequently, the questions focus on your ability to apply knowledge that you have learned in past experiences related to the English Language. The TOEFL test requires that you understand the underlying concepts of the English language.
Many stare at limited funding and the overwhelming task of studying to score high on the TOEFL test. My article was created to help all of us with the concept of distance learning business and management and I named Distance Learning TOEFL Test, overcome the challenge of the TOEFL test. The key TOEFL testing tips are stated as follows:

1. I hope with the methods of Distance learning business and management will improve your writing skills.
2. Not just writing skills, with Distance learning business and management you will also review reading passages that frequently occur on the TOEFL test.
3. Improve your vocabulary, getting better and better
4. Be familiar with the format of the TOEFL test, this could be useful for you when you face the test.
5. Practice intensely for the listening test on the TOEFL test. Practice makes perfect right???

The hours of studying and class work are finally worth it when you gain acceptance into the school or job of your choice (You get what you want because of your own ability, hard work and smart)
Please take your time to review all of my articles written about the TOEFL test and the pitfalls that some students fall into with the test. I really hope, you can avoid the mistakes that others make when preparing for the exam and will find the following information to be helpful and informative on dealing with the TOEFL test. Good luck... see you next chapter... and keep your spirits lights turn on, never give up!