Iq option 2 step authentication
If we could see that the results from each of the samples would be nearly the same and nearly correct. In some extreme cases e.then we would have confidence in the single sample that will actually be used. On the other hand, seeing that answers from the repeated samples were too variable for the needed accuracy would suggest that a different sampling plan perhaps with a larger sample size should be used. A sampling distribution is used to describe the distribution of outcomes that one would observe from replication of a particular sampling plan.
Know that to estimate means to esteem to give value to. Know that estimates computed from one sample will be different from estimates that would be computed from another sample. Understand that estimates are expected to differ from the population characteristics parameters that we are trying to estimate, but that the properties of sampling distributions allow us to quantify, probabilistically, how they will differ.
Understand that different statistics have different sampling distributions with distribution shape depending on a the specific statistic, b the sample size, and c the parent distribution. Understand the relationship between sample size and the distribution of sample estimates. Understand that the variability in a sampling distribution can be reduced by increasing the sample size. Outlier Removal. Robust statistical techniques are needed to cope with any undetected outliers; otherwise the result will be misleading.
Note that in large samples, many sampling distributions can be approximated with a normal distribution. Because of the potentially large variance, outliers could be the outcome of sampling. It s perfectly correct to have such an observation that legitimately belongs to the study group by definition. For example, the usual stepwise regression is often used for the selection of an appropriate subset of explanatory variables to use in model; however, it could be invalidated even by the presence of a few outliers.
Lognormally distributed data such as international exchange ratefor instance, will frequently exhibit such values. Therefore, you must be very careful and cautious before declaring an observation an outlier, find out why and how such observation occurred. It could even be an error at the data entering stage. First, construct the BoxPlot of your data. Form the Q1, Q2, and Q3 points which divide the samples into four equally sized groups. Q2 median Let IQR Q3 - Q1. Outliers are defined as those points outside the values Q3 k IQR and Q1-k IQR.
For most case one sets k 1. Another alternative is the following algorithm. a Compute s of whole sample. b Define a set of limits off the mean mean k smean - k s sigma Allow user to enter k. A typical value for k is 2. c Remove all sample values outside the limits. Now, iterate N times through the algorithm, each time replacing the sample set with the reduced samples after applying step c. Usually we need to iterate through this algorithm 4 times.
As mentioned earlier, a common standard is any observation falling beyond 1. 5 interquartile range i. 5 IQRs ranges above the third quartile or below the first quartile. The following SPSS program, helps you in determining the outliers. Outlier detection in the single population setting has been treated in detail in the literature. Quite often, however, one can argue that the detected outliers are not really outliers, but form a second population.
If this is the case, a cluster approach needs to be taken. Further Readings Hawkins D. It will be active areas of research to study the problem of how outliers can arise and be identified, when a cluster approach must be taken.Identification of OutliersChapman Hall, 1980. Rothamsted V. Barnett, and T. Lewis, Outliers in Statistical DataWiley, 1994. Least Squares Models. Realize that fitting the best line by eye is difficult, especially when there is a lot of residual variability in the data.
Know that there is a simple connection between the numerical coefficients in the regression equation and the slope and intercept of regression line. Know that a single summary statistic like a correlation coefficient or does not tell the whole story. A scatter plot is an essential complement to examining the relationship between the two variables. Know that the model checking is an essential part of the process of statistical modelling.
After all, conclusions based on models that do not properly describe an observed set of data will be invalid. Know the impact of violation of regression model assumptions i.conditions and possible solutions by analyzing the residuals. Least Median of Squares Models. What Is Sufficiency. A sufficient statistic t for a parameter q is a function of the sample data x1. xn, which contains all information in the sample about the parameter q.
More formally, sufficiency is defined in terms of the likelihood function for q. For a sufficient statistic t, the Likelihood L x1. xn q can be written as. Since the second term does not depend on qt is said to be a sufficient statistic for q. Another way of stating this for the usual problems is that one could construct a random process starting from the sufficient statistic, which will have exactly the same distribution as the full sample for all states of nature.
To illustrate, let the observations be independent Bernoulli trials with the same probability of success. Suppose that there are n trials, and that person A observes which observations are successes, and person B only finds out the number of successes. Then if B places these successes at random points without replication, the probability that B will now get any given set of successes is exactly the same as the probability that A will see that set, no matter what the true probability of success happens to be.
You Must Look at Your Scattergrams. All three sets have the same correlation and regression line. The important moral is look at your scattergrams. How to produce a numerical example where the two scatterplots show clearly different relationships strengths but yield the same covariance. Produce two sets of X, Y values that have different correlation s; 2. Perform the following steps.
Calculate the two covariances, say C1 and C2; 3. Suppose you want to make C2 equal to C1. Then you want to multiply C2 by C1 C2 ; 4. S yyou want two numbers one of them might be 1a and b iq option 2 step authentication that a. Multiply all values of X in set 2 by a, and all values of Y by b for the new variables, C r. An interesting numerical example showing two identical scatterplots but with differing covariance is the following Consider a data set of X, Y values, with covariance C1.
Now let V 2X, and W 3Y. The covariance of V and W will be 2 3 6 times C1, but the correlation between V and W is the same as the correlation between X and Y. Power of a Test. Power of a test is the probability of correctly rejecting a false null hypothesis. This probability is one minus the probability of making a Type II error b. Recall also that we choose the probability of making a Type I error when we set a and that if we decrease the probability of making a Type I error we increase the probability of making a Type II error.
Power and Alpha. Power and the True Difference between Population Means Anytime we test whether a sample differs from a population or whether two sample come from 2 separate populations, there is the assumption that each of the populations we are comparing has it s own mean and standard deviation even if we do not know it. The distance between the two population means will affect the power of our test. Power as a Function of Sample Size and Variance You should iq option 2 step authentication that what really made the difference in the size of b is how much overlap there is in the two distributions.
When the means are close together the two distributions overlap a great deal compared to when the means are farther apart. Thus, anything that effects the extent the two distributions share common values will increase b the likelihood of making a Type II error. Sample size has an indirect effect on power because it affects the measure of variance we use to calculate the t-test statistic.
Thus, sample size is of interest because it modifies our estimate of the standard deviation. Since we are calculating the power of a test that involves the comparison of sample means, we will be more interested in the standard error the average difference in sample values than standard deviation or variance by itself. When n is large we will have a lower standard error than when n is small. In turn, when N is large well have a smaller b region than when n is small.
Pilot Studies When the needed estimates for sample size calculation is not available from existing database, a pilot study is needed for adequate estimation with a given precision. Further Readings Cohen J.Statistical Power Analysis for the Behavioral SciencesL. Erlbaum Associates, 1988. Thiemann, How Many Subjects. Provides basic sample size tablesexplanations, and power analysis.
Myors, Statistical Power AnalysisL. Erlbaum Associates, 1998. Provides a simple and general sample size determination for hypothesis tests. ANOVA Analysis of Variance. Thus, when the variability that we predict between the two groups is much greater than the variability we don t predict within each group then we will conclude that our treatments produce different results.
Levene s Test Suppose that the sample data does not support the homogeneity of variance assumption, however, there is a good reason that the variations in the population are almost the same, then in such a situation you may like to use the Levene s modified test In each group first compute the absolute deviation of the individual values from the median in that group. Apply the usual one way ANOVA on the set of deviation values and then interpret the results.
When a large enough random sample data are close to their mean i.the averagethen the population has a small deviation. If the data varies significantly from the mean, the data has a large deviation. In quality control measurement terms, you want to see that the sample is as close as possible to the mean and that the mean meets or exceed specifications.
A large sigma means that there is a large amount of variation within the data. A lower sigma value corresponds to a small variation, and therefore a controlled process with a good quality. The Six-Sigma means a measure of quality that strives for near perfection. Six-Sigma is a data-driven approach and methodology for eliminating defects to achieve six sigmas between lower and upper specification limits.
4 defects per million opportunities. Accordingly, to achieve Six-Sigma, e. Therefore, a Six-Sigma defect is defined for not meeting the customer s specifications. A Six-Sigma opportunity is then the total quantity of chances for a defect. One sigma means only 68 of products are acceptable; three sigma means 99. 7 are acceptable.
Six-Sigma is 99. Six-Sigma is a statistical measure expressing how close a product comes to its quality goal. 4 defects per million parts or opportunities. 9997 perfect or 3. The natural spread is 6 times the sample standard deviation. The natural spread is centered on the sample mean, and all weights in the sample fall within the natural spread, meaning the process will produce relatively few out-of-specification products. Six-Sigma does not necessarily imply 3 defective units per million made; it also signifies 3 defects per million opportunities when used to describe a process.
Some products may have tens of thousands of opportunities for defects per finished item, so the proportion of defective opportunities may actually be quite large. Six-Sigma Quality is a fundamental approach to delivering very high levels of customer satisfaction through disciplined use of data and statistical analysis for maximizing and sustaining business success.
What that means is that all business decisions are made based on statistical analysis, not instinct or past history. Using the Six-Sigma approach will result in a significant, quantifiable improvement.in a manufacturing process it must not produce more than 3. 6 sigma defect-free good enough. Here are some examples of what life would be like if 99.
9 were good enough 1 hour of unsafe drinking water every month 2 long or short landings at every American cities airport each day 400 letters per hour which never arrive at their destination 3,000 newborns accidentally falling from the hands of nurses or doctors each year 4,000 incorrect drug prescriptions per year 22,000 checks deducted from the wrong bank account each hour As you can see, sometimes 99. 9 good just isn t good enough. Is it truly necessary to go for zero defects. Here are some examples of what life would be still like at Six-Sigma, 99.
9997 defect-free 13 wrong drug prescriptions per year 10 newborns accidentally falling from the hands of nurses or doctors each year 1 lost article of mail per hour. Now we see why the quest for Six-Sigma quality is necessary. Six-Sigma is the application of statistical methods to business processes to improve operating efficiencies. It provides companies with a series of interventions and statistical tools that can lead to breakthrough profitability and quantum gains in quality.
Six-Sigma allows us to take a real world problem with many potential answers, and translate it to a math problem, which will have only one answer. We then convert that one mathematical solution back to a real world solution. Six-Sigma goes beyond defect reduction to emphasize business process improvement in general, which includes total cost reduction, cycle-time improvement, increased customer satisfaction, and any other metric important to the customer and the company.
An objective of Six-Sigma is to eliminate any waste in the organization s processes by creating a road map for changing data into knowledge, reducing the amount of stress companies experience when they are overwhelmed with day-to-day activities and proactively uncovering opportunities that impact the customer and the company itself.
The key to the Six-Sigma process is in eliminating defects. Organizations often waste time creating metrics that are not appropriate for the outputs being measured. Executives can get deceptive results if they force all projects to determine a one size fits all metric in order to compare the quality of products and services from various departments. From a managerial standpoint, having one universal tool seems beneficial; however, it is not always feasible. In the airline industry, the US Air Traffic Control System Command Center measures companies on their rate of on time departure.
Below is an example of the deceptiveness of metrics. This would obviously be a critical measurement to customers the flying public. Whenever an airplane departs 15 minutes or more later than scheduled, that event is considered as a defect. Unfortunately, the government measures the airlines on whether the plane pulls away from the airport gate within 15 minutes of scheduled departure, not when it actually takes off. Airlines know this, so they pull away from the gate on time but let the plane sit on the runway as long as necessary before take off.
The result to the customer is still a late departure. This defect metric is therefore not an accurate representation of the desires of the customers who are impacted by the process. If this were a good descriptive metric, airlines would be measured by the actual delay experienced by passengers. This example shows the importance of having the right metrics for each process. The method above creates no incentive to reduce actual delays, so the customer and ultimately the industry still suffers.
With a Six-Sigma business strategy, we want to see a picture that describes the true output of a process over time, along with additional metrics, to give an insight as to where the management has to focus its improvement efforts for the customer. The Six Steps of Six-Sigma Loop Process The process is identified by the following five major activities for each project Identify the product or service you provide What do you do. Identify your customer base, and determine what they care about Who uses your products and services.
What is really important to them. Identify your needs What do you need to do your work. Define the process for doing your work How do you do your work. Ensure continuous improvement by measuring, analyzing, and controlling the improved process How perfectly are you doing your customer-focused work. Often each step can create dozens of individual improvement projects and can last for several months. It is important to go back to each step from time to time in order to determine actual data maybe with improved measurement systems.
Eliminate wasted efforts How can you do your work better. Once we know the answers to the above questions, we can begin to improve the process. The following case study will further explain the steps applied in Six-Sigma to Measure, Analyze, Improve, and Control a process to ensure customer satisfaction. The Six Sigma General Process and Its Implementation The Six-Sigma means a measure of quality that strives for near perfection. Six-Sigma is a data-driven approach and methodology for eliminating defects to achieve six-sigma s between lower and upper specification limits.
The implementation of the Six Sigma system starts normally with a few days workshop of the top level management of the organization. Only if the advantages of Six Sigma can be clearly stated and supported of the entire Management, then it makes sense to determine together the first project surrounding field and the pilot project team. The pilot project team members participate is a few days Six Sigma workshop to learn the system principals, the process, the tools and the methodology.
The project team meets to compiles main decisions and identifying key stakeholders in the pilot surrounding field. Within the next days the requirements of the stakeholders are collected for the main decision processes by face-to-face interviews. By now, the workshop of the top management must be ready for the next step. The next step for the project team is to decide which and how the achievements should be measured and then begin with the data collection and analysis.
Whenever the results are understood well then suggestions for improvement will be collected, analyzed, and prioritized based on the urgency and inter-dependencies. As the main outcome, the project team members will determine which improvements should be realized first. The activities must be carried out in parallel whenever possible by a network activity chart. The activity chart will become more and more realistic by a loop-process while spread the improvement throughout the organization.
In this phase it is important that rapid successes are obtained, in order to even the soil for other Six Sigma projects in the organization. The main objective of the Six-Sigma approach is the implementation of a measurement-based strategy that focuses on process improvement. The aim is variation reduction, which can be accomplished by Six-Sigma methodology. More and more processes will be included and employees are trained including Black Belts who are the six sigma masters, and the dependency of external advisors will be reduced.
The Six-Sigma is a business strategy aimed at the near-elimination of defects from every manufacturing, service and transactional process. The concept of Six-Sigma was introduced and popularized for reducing defect rate of manufactured electronic boards. Although the original goal of Six-Sigma was to focus on manufacturing process, today the marketing, purchasing, customer order, financial and health care processing functions also embarked on Six Sigma programs.
Motorola Inc. Case Motorola is a role model for modern manufacturers. There is a reason for this reputation. The maker of wireless communications products, semiconductors, and electronic equipment enjoys a stellar reputation for high-tech, high-quality products. A participative-management process emphasizing employee involvement is a key factor in Motorola s quality push.
In 1987, Motorola invested 44 million in employee training and education in a new quality program called Six-Sigma. Motorola measures its internal quality based on the number of defects in its products and processes. Motorola conceptualized Six-Sigma as a quality goal in the mid-1980. Their target was Six-Sigma quality, or 99. 9997 defect free products which is equivalent to 3. 4 defects or less per 1 million parts. Quality is a competitive advantage because Motorola s reputation opens markets.
When Motorola Inc. won the Malcolm Baldridge National Quality Award in 1988; it was in the early stages of a plan that, by 1992, would achieve Six-Sigma Quality. It is estimated that of 9. Shortly thereafter, many US firms were following Motorola s lead. Control Charts, and the CUSUM. 2 billion in 1989 sales, 480 million was saved as a result of Motorola s Six-Sigma program.
Developing quality control charts for variables X-Chart The following steps are required for developing quality control charts for variables. Decide what should be measured. Determine the sample size. Collect random sample and record the measurements counts. Calculate the average for each sample. Calculate the overall average. This is the average of all the sample averages X-double bar. Calculate the average range R-bar. Determine the upper control limit UCL and lower control limit LCL for the average and for the range.
Determine the range for each sample. Plot the chart. Determine if the average and range values are in statistical control. Take necessary action based on your interpretation of the charts. Developing control charts for attributes P-Chart Control charts for attributes are called P-charts. The following steps are required to set up P-charts. Determine what should be measured. Collect sample data and record the data.
Determine the required sample size. Calculate the average percent defective for the process p. Determine the control limits by determining the upper control limit UCL and the lower control limit LCL values for the chart. Plot the data. Determine if the percent defectives are within control. Control charts are also used in industry to monitor processes that are far from Zero-Defect. However, among the powerful techniques is the counting of the cumulative conforming items between two nonconforming and its combined techniques based on cumulative sum and exponentially weighted moving average smoothing methods.
The general CUSUM is a statistical process control when measurements are multivariate. It is an effective tool in detecting a shift in the mean vector of the measurements, which is based on the cross-sectional antiranks of the measurements At each time point, the measurements, after being appropriately transformed, are ordered and their antiranks are recorded. When the process is in-control under some mild regularity conditions the antirank vector at each time point has a given distribution, which changes to some other distribution when the process is out-of-control and the components of the mean vector of the process are not all the same.
This latter shift, however, can be easily detected by a univariate CUSUM. Further Readings Breyfogle F. Therefore it detects shifts in all directions except the one that the components of the mean vector are all the same but not zero.Implementing Six Sigma Smarter Solutions Using Statistical Methods, Wiley, 1999. del Castillo E.Statistical Process and Adjustment Methods for Quality ControlWiley, 2002. Juran J, and A. Godfreym, Juran s Quality Handbook, McGraw-Hill, 1999. Kuralmani, Statistical Models and Control Charts for High Quality ProcessesKluwer, 2002.
Repeatability and Reproducibility.Concepts for R R StudiesASQ Quality Press, 1991. Lyday, Evaluating the Measurement ProcessStatistical Process Control Press, 1990. Further Readings Barrentine L. Statistical Instrument, Grab Sampling, and Passive Sampling Techniques. What is a statistical instrument. A statistical instrument is any process that aim at describing a phenomena by using any instrument or device, however the results may be used as a control tool. Examples of statistical instruments are questionnaire and surveys sampling.
What is grab sampling technique. The grab sampling technique is to take a relatively small sample over a very short period of time, the result obtained are usually instantaneous. However, the Passive Sampling is a technique where a sampling device is used for an extended time under similar conditions. Depending on the desirable iq option 2 step authentication investigation, the Passive Sampling may be a useful alternative or even more appropriate than grab sampling.
However, a passive sampling technique needs to be developed and tested in the field. line transect sampling, in which the distances sampled are distances of detected objects usually animals from the line along which the observer travels. Distance Sampling. point transect sampling, in which the distances sampled are distances of detected objects usually birds from the point at which the observer stands.
cue counting, in which the distances sampled are distances from a moving observer to each detected cue given by the objects of interest usually whales. trapping webs, in which the distances sampled are from the web center to trapped objects usually invertebrates or small terrestrial vertebrates. migration counts, in which the distances sampled are actually times of detection during the migration of objects usually whales past a watch point.
Many mark-recapture models have been developed over the past 40 years. Monitoring of biological populations is receiving increasing emphasis in many countries. Data from marked populations can be used for the estimation of survival probabilities, how these vary by age, sex and time, and how they correlate with external variables. Estimation of the finite rate of population change and fitness are still more difficult to address in a rigorous manner.
Estimation of immigration and emigration rates, population size and the proportion of age classes that enter the breeding population are often important and difficult to estimate with precision for free-ranging populations. Further Readings Buckland S. Burnham, and J. Laake, Distance Sampling Estimating Abundance of Biological PopulationsChapman and Hall, London, 1993. Borchers, and L. Thomas, Introduction to Distance SamplingOxford University Press, 2001.
Data Mining and Knowledge Discovery. The continuing rapid growth of on-line data and the widespread use of databases necessitate the development of techniques for extracting useful knowledge and for facilitating database access. The challenge of extracting knowledge from data is of common interest to several fields, including statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing.
The data mining process involves identifying an appropriate data set to mine or sift through to discover data content relationships. Data mining sometimes resembles the traditional scientific method of identifying a hypothesis and then testing it using an appropriate data set. Data mining tools include techniques like case-based reasoning, cluster analysis, data visualization, fuzzy query and analysis, and neural networks. Sometimes however data mining is reminiscent of what happens when data has been collected and no significant results were found and hence an ad hoc, exploratory analysis is conducted to find a significant relationship.
The combination of fast computers, cheap storage, and better communication makes it easier by the day to tease useful information out of everything from supermarket buying patterns to credit histories. For clever marketers, that knowledge can be worth as much as the stuff real miners dig from the ground. The process thus consists of three basic stages exploration, model building or pattern definition, and validation verification. Data mining as an analytic process designed to explore large amounts of typically business or market related data in search for consistent patterns and or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data.
What distinguishes data mining from conventional statistical data analysis is that data mining is usually done for the purpose of secondary analysis aimed at finding unsuspected relationships unrelated to the purposes for which the data were originally collected. Data warehousing as a process of organizing the storage of large, multivariate data sets in a way that facilitates the retrieval of information for analytic purposes.
Data mining is now a rather vague term, but the element that is common to most definitions is predictive modeling with large data sets as used by big companies. Therefore, data mining is the extraction of hidden predictive information from large databases. It is a powerful new technology with great potential, for example, to help marketing managers preemptively define the information market of tomorrow.
Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools. Data mining answers business questions that traditionally were too time-consuming to resolve. Data mining tools scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
Data mining techniques can be implemented rapidly on existing software and hardware platforms across the large companies to enhance the value of existing resources, and can be integrated with new products and systems as they are brought on-line. Knowledge discovery in databases aims at tearing down the last barrier in enterprises information flow, the data analysis step.
It is a label for an activity performed in a wide variety of application domains within the science and business communities, as well as for pleasure. When implemented on high performance client-server or parallel processing computers, data mining tools can analyze massive databases while a customer or analyst takes a coffee break, then deliver answers to questions such as, Which clients are most likely to respond to my next promotional mailing, and why.
The activity uses a large and heterogeneous data-set as a basis for synthesizing new and relevant knowledge. The knowledge is new because hidden relationships within the data are explicated, and or data is combined with prior knowledge to elucidate a given problem. The term relevant is used to emphasize that knowledge discovery is a goal-driven process in which knowledge is constructed to facilitate the solution to a problem.
Knowledge discovery maybe viewed as a process containing many tasks. Some of these tasks are well understood, while others depend on human judgment in an implicit matter. Further, the process is characterized by heavy iterations between the tasks. This is very similar to many creative engineering process, e.the development of dynamic models. In this reference mechanistic, or first principles based, models are emphasized, and the tasks involved in model development are defined by.
Initialize data collection and problem formulation. The initial data are collected, and some more or less precise formulation of the modeling problem is developed. Tools selection. The software tools to support modeling and allow simulation are selected. Conceptual modeling. The system to be modeled, e.a chemical reactor, a power generator, or a marine vessel, is abstracted at first.
Model representation. The essential compartments and the dominant phenomena occurring are identified and documented for later reuse. A representation of the system model is generated. Often, equations are used; however, a graphical block diagram or any other formalism may alternatively be used, depending on the modeling tools selected above.
Computer implementation. The model representation is implemented using the means provided by the modeling system of the software employed. These may range from general programming languages to equation-based modeling languages or graphical block-oriented interfaces. The model implementation is verified to really capture the intent of the modeler. No simulations for the actual problem to be solved are carried out for this purpose. Reasonable initial values are provided or computed, the numerical solution process is debugged.
The results of the simulation are validated against some reference, ideally against experimental data. The modeling process, the model, and the simulation results during validation and application of the model are documented. The model is used in some model-based process engineering problem solving task. For other model types, like neural network models where data-driven knowledge is utilized, the modeling process will be somewhat different.
Some of the tasks like the conceptual modeling phase, will vanish. Typical application areas for dynamic models are control, prediction, planning, and fault detection and diagnosis. A major deficiency of today s methods is the lack of ability to utilize a wide variety of knowledge. Model application. As an example, a black-box model structure has very limited abilities to utilize first principles knowledge on a problem. this has provided a basis for developing different hybrid schemes.
Two hybrid schemes will highlight the discussion. First, it will be shown how a mechanistic model can be combined with a black-box model to represent a pH neutralization system efficiently. Second, the combination of continuous and discrete control inputs is considered, utilizing a two-tank example as case. The hybrid approach may be viewed as a means to integrate different types of knowledge, i.being able to utilize a heterogeneous knowledge base to derive a model.
Standard practice today is that almost any methods and software can treat large homogeneous data-sets. A typical example of a homogeneous data-set is time-series data from some system, e.temperature, pressure, and compositions measurements over some time frame provided by the instrumentation and control system of a chemical reactor. If textual information of a qualitative nature is provided by plant personnel, the data becomes heterogeneous.
The above discussion will form the basis for analyzing the interaction between knowledge discovery, and modeling and identification of dynamic models. In particular, we will be interested in identifying how concepts from knowledge discovery can enrich state-of-the- art within control, prediction, planning, and fault detection and diagnosis of dynamic systems. Further Readings Marco D.Building and Managing the Meta Data Repository A Full Lifecycle GuideJohn Wiley, 2000. Thuraisingham B.Data Mining Technologies, Techniques, Tools, and TrendsCRC Press, 1998.
Westphal Ch. Different approaches to handle this heterogeneous case are considered. Blaxton, Data Mining Solutions Methods and Tools for Solving Real-World ProblemsJohn Wiley, 1998. Neural Networks Applications. The classical approaches are the feedforward neural networks, trained using back-propagation, which remain the most widespread and efficient technique to implement supervised learning. Applications include data mining, and stock market predictions. Further Readings Schurmann J.Pattern Classification A Unified View of Statistical and Neural ApproachesJohn Wiley Sons, 1996.
Bayes and Empirical Bayes Methods. Bayes and EB methods can be implemented using modern Markov chain Monte Carlo MCMC computational methods. The main steps are preprocess the data, the appropriate selection of variables, postprocessing of the results, and a final validation of the global strategy. Properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice. This in turn motivates their use in advanced high-dimensional model settings e.longitudinal data or spatio-temporal mapping modelswhere a Bayesian model implemented via MCMC often provides the only feasible approach that incorporates all relevant model features.
Smith, Bayesian TheoryWiley, 2000. Louis, Bayes and Empirical Bayes Methods for Data AnalysisChapman and Hall, 1996.Bayesian Statistical ModellingWiley, 2001. Markovian Memory Theory. Memory Theory and time series share the additive property and inside a single term there can be multiplication, but like general regression methods this does not always mean that they are all using M Theory.
One may use standard time series methods in the initial phase of modeling things, but instead proceed as follows using M Theory s Cross-Term Dimensional Analysis CTDA. Suppose that you postulate a model y af x - bg z ch u where f, g, h are some functions and x, z, u are what are usually referred to as independent variables. Notice the minus sign - to the left of b and the sign to the left of c and implicitly to the left of a, where a, b, iq option 2 step authentication are positive constants.
The variable y is usually referred to as a dependent variable. According to M Theory, not only do f, g, and h influence cause y, but g influences causes f and h at least to some extent. In fact, M Theory can formulate this in terms of probable influence as well as deterministic influence. All this generalizes to the case where the functions f, g, h depend on two or more variables, e.f x, wg z, t, retc. One can reverse this process. If it works, one has found something that mainstream regression and time series may fail to detect.
If one thinks that f influences g and h and y but that h and g only influence y and not f, then express the equation of y in the above form. Of course, path analysis and Lisrel and partial least squares also claim to have causal abilities, but only in the standard regression sense of freezing so-called independent variables as givens and not in the M Theory sense which allows them to vary with y. In fact, Bayesian probability statistics methods and M Theory methods use respectively ratios like y x and differences like y - x 1 in their equations, and in the Bayesian model x is fixed but in the M Theory model x can vary.
If one looks carefully, one will notice that the Bayesian model blows up at x 0 because division by 0 is impossible, visit the The Zero Saga pagebut also near x 0 since an artificially enormous increase is introduced - precisely near rare events. That is one of the reasons why M Theory is more successful for rare and or highly influenced influencing events, while Bayesian and mainstream methods work fairly well for frequent common and or low influence even independent and or low dependence events.
Further Readings Kursunuglu B. Perlmutter, Quantum Gravity, Generalized Theory of Gravitation, and Superstring Theory-Based UnificationKluwer Academic Plenum, New York 2000. Likelihood Methods. The decision-oriented methods treat statistics as a matter of action, rather than inference, and attempt to take utilities as well as probabilities into account in selecting actions; the inference-oriented methods treat inference as a goal apart from any action to be taken.
Fisher s fiducial method is included because it is so famous, but the modern consensus is that it lacks justification. The hybrid row could be more properly labeled as hypocritical -- these methods talk some Decision talk but walk the Inference walk. Now it is true, under certain assumptions, some distinct schools advocate highly similar calculations, and just talk about them or justify them differently. Some seem to think this is tiresome or impractical.
One may disagree, for three reasons. First, how one justifies calculations goes to the heart of what the calculations actually MEAN; second, it is easier to teach things that actually make sense which is one reason that standard practice is hard to teach ; and third, methods that do coincide or nearly so for some problems may diverge sharply for others. The difficulty with the subjective Bayesian approach is that prior knowledge is represented by a probability distribution, and this is more of a commitment than warranted under conditions of partial ignorance.
Uniform or improper priors are just as bad in some respects as anything other sort of prior. Edwards, in particular, uses logarithm of normalized likelihood as a measure of support for a hypothesis. The methods in the Inference, Inverse cell all attempt to escape this difficulty by presenting alternative representations of partial ignorance. Prior information can be included in the form of a prior support log likelihood function; a flat support represents complete prior ignorance.
One place where likelihood methods would deviate sharply from standard practice is in a comparison between a sharp and a diffuse hypothesis. Consider H0 X. N 0, 100 diffuse and H1 X. N 1, 1 standard deviation 10 times smaller. In standard methods, observing X 2 would be undiagnostic, since it is not in a sensible tail rejection interval or region for either hypothesis.
But while X 2 is not inconsistent with H0, it is much better explained by H1--the likelihood ratio is about 6. 2 in favor of H1. In Edwards methods, H1 would have higher support than H0, by the amount log 6. If these were the only two hypotheses, the Neyman-Pearson lemma would also lead one to a test based on likelihood ratio, but Edwards methods are more broadly applicable. I do not want to appear to advocate likelihood methods. I could give a long discussion of their limitations and of alternatives that share some of their advantages but avoid their limitations.
Data mining is the process of extracting knowledge from data. They are practical currently widely used in genetics and are based on a careful and profound analysis of inference. A Meta-analysis deals with a set of RESULTs to give an overall RESULT that is presumably comprehensive and valid. But it is definitely a mistake to dismiss such methods lightly. I recall a case in physics, in which, after a phenomenon had been observed in air, emulsion data was examined.
As it happens, there was no significant practical, not statistical in the theory, and also no error in the data. We really need to between the term statistically significantand the usual word significant. It is very important to distinction between statistically significant and generally significant, see Discover Magazine July, 1987The Case of Falling Nightwatchmen, by Sapolsky.
In this article, Sapolsky uses the example to point out the very important distinction between statistically significant and generally significant A diminution of velocity at impact may be statistically significant, but not of importance to the falling nightwatchman. Be careful about the word significant. It has a technical meaning, not a commonsense one.
It is NOT automatically synonymous with important. A person or group can be statistically significantly taller than the average for the population, but still not be a candidate for your basketball team. Whether the difference is substantively not merely statistically significant is dependent on the problem which is being studied. There is also graphical technique to assess robustness of meta-analysis results. We should carry out the meta-analysis dropping consecutively one study, that is if we have N studies we should do N meta-analysis using N-1 studies in each one.
After that we plot these N estimates on the y axis and compare them with a straight line that represent the overall estimate using all the studies. Topics in Meta-analysis includes Odds ratios; Relative risk; Risk difference; Effect size; Incidence rate difference and ratio; Plots and exact confidence intervals. Further Readings Glass, et al.Meta-Analysis in Social ResearchMcGraw Hill, 1987 Cooper H.Handbook of Research SynthesisRussell Sage Foundation, New York, 1994.
Industrial Data Modeling. Further Readings Montgomery D. Runger, Applied Statistics and Probability for EngineersWiley, 1998.Introduction to Probability and Statistics for Engineers and ScientistsAcademic Press, 1999. Prediction Interval. Since we don t actually know s 2we need to use t in evaluating the test statistic. The appropriate Prediction Interval for Y is. This is similar to construction of interval for individual prediction in regression analysis. Fitting Data to a Broken Line.
y a b x, for x less than or equal c y a - d c d b x, for x greater than or equal to c. A simple solution is a brute force search across the values of c. Once c is known, estimating a, b, and d is trivial through the use of indicator variables. One may use x-c as your independent variable, rather than x, for computational convenience. Now, just fix c at a fine grid of x values in the range of your data, estimate a, b, and d, and then note what the mean squared error is.
Select the value of c that minimizes the mean squared error. Unfortunately, you won t be able to get confidence intervals involving c, and the confidence intervals for the remaining parameters will be conditional on the value of c. Further Readings For more details, see Applied Regression Analysisby Draper and Smith, Wiley 1981, Chapter 5, section 5. 4 on use of dummy variables. How to Determine if Two Regression Lines Are Parallel.
Ho slope group 1 slope group 0 is equivalent to Ho b 3 0. Use t-test from variables-in-the equation table to test this hypothesis. Constrained Regression Model. I agree that it s initially counter-intuitive see belowbut here are two reasons why it s true. The variance of the slope estimate for the constrained model is s 2 S X i 2where X i are actual X values and s 2 is estimated from the residuals.
The variance of the slope estimate for the unconstrained model with intercept is s 2 S x i 2where x i are deviations from the mean, and s 2 is still estimated from the residuals. So, the constrained model can have a larger s 2 mean square error residual and standard error of estimate but a smaller standard error of the slope because the denominator is larger. r 2 also behaves very strangely in the constrained model; by the conventional formula, it can be negative; by the formula used by most computer packages, it is generally larger than the unconstrained r 2 because it is dealing with deviations from 0, not deviations from the mean.
This is because, in effect, constraining the intercept to 0 forces us to act as if the mean of X and the mean of Y both were 0. Once you recognize that the s. of the slope isn t really a measure of overall fit, the result starts to make a lot of sense. Assume that all your X and Y are positive. If you re forced to fit the regression line through the origin or any other point there will be less wiggle in how you can fit the line to the data than there would be if both ends could move.
Consider a bunch of points that are ALL way out, far from zero, then if you Force the regression through zero, that line will be very close to all the points, and pass through origin, with LITTLE ERROR. And little precision, and little validity. Therefore, no-intercept model is hardly ever appropriate. Semiparametric and Non-parametric modeling. and the unknown e is interpreted as error term.
The most simple model for this problem is the linear regression model, an often used generalization is the Generalized Linear Model GLM. where G is called the link function. All these models lead to the problem of estimating a multivariate regression. Parametric regression estimation has the disadvantage, that by the parametric form certain properties of the resulting estimate are already implied. Nonparametric techniques allow diagnostics of the data without this restriction.
However, this requires large sample sizes and causes problems in graphical visualization. Semiparametric methods are a compromise between both they support a nonparametric modeling of certain features and profit from the simplicity of parametric methods. Further Readings Hдrdle W. Klinke, and B. Turlach, XploRe An Interactive Statistical Computing EnvironmentSpringer, New York, 1995. Moderation and Mediation. Discriminant and Classification.
We often need to classify individuals into two or more populations based on a set of observed discriminating variables. Methods of classification are used when discriminating variables are. quantitative and approximately normally distributed; quantitative but possibly nonnormal; categorical; or a combination of quantitative and categorical. It is important to know when and how to apply linear and quadratic discriminant analysis, nearest neighbor discriminant analysis, logistic regression, categorical modeling, classification and regression trees, and cluster analysis to solve the classification problem.
SAS has all the routines you need to for proper use of these classifications. Relevant topics are Matrix operations, Fisher s Discriminant Analysis, Nearest Neighbor Discriminant Analysis, Logistic Regression and Categorical Modeling for classification, and Cluster Analysis. For example, two related methods which are distribution free are the k-nearest neighbor classifier and the kernel density estimation approach.
In both methods, there are several problems of importance the choice of smoothing parameter s or k, and choice of appropriate metrics or selection of variables. These problems can iq option 2 step authentication addressed by cross-validation methods, but this is computationally slow. An analysis of the relationship with a neural net approach LVQ should yield faster methods. Further Readings Cherkassky V, and F. Mulier, Learning from Data Concepts, Theory, and MethodsJohn Wiley Sons, 1998.
Mallick, and A. Smith, Bayesian Methods for Nonlinear Classification and RegressionWiley, 2002. Index of Similarity in Classification. A rather computationally involved for determining a similarity index I is due to Fisher, where I is the solution to the following equation. e aI e bI 1 e a b-j I. The index of similarity could be used as a distance so that the minimum distance corresponds to the maximum similarity.
Further Readings Hayek L. Buzas, Surveying Natural PopulationsColumbia University Press, NY, 1996. Generalized Linear and Logistic Models. Hre is how to obtain degree of freedom number for the 2 log-likelihood, in a logistic regression. Degrees of freedom pertain to the dimension of the vector of parameters for a given model. Suppose we know that a model ln p 1-p Bo B1x B2y B3w fits a set of data.
In this case the vector B Bo,B1, B2, B3 is an element of 4 dimensional Euclidean space, or R 4. Suppose we want to test the hypothesis Ho B3 0. We are imposing a restriction on our parameter space. The vector of parameters must be of the form B B Bo,B1, B2, 0. This vector is an element of a subspace of R 4. Namely, B4 0 or the X-axis. The likelihood ration statistic has the form. 2 log-likelihood 2 log maximum unrestricted likelihood maximum restricted likelihood 2 log maximum unrestricted likelihood -2 log maximum restricted likelihood.
Which is unrestricted B vector 4-dimensions or degrees of freedom - restricted B vector 3 dimensions or degrees of freedom 1 degree of freedom which is the difference vector B B-B 0,0,0,B4 one dimensional subspace of R 4. The standard textbook is Generalized Linear Models by McCullagh and Nelder Chapman Hall, 1989. Other SPSS Commands SAS Commands.
Further Readings Harrell F, Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival AnalysisSpringer Verlag, 2001. Lemeshow, Applied Logistic RegressionWiley, 2000.Multivariable Analysis A Practical Guide for CliniciansCambridge University Press, 1999. Kleinbaum D.Logistic Regression A Self-Learning TextSpringer Verlag, 1994.Logistic Regression A PrimerSage, 2000.
Survival Analysis. The methods of survival analysis are applicable not only in studies of patient survival, but also studies examining adverse events in clinical trials, time to discontinuation of treatment, duration in community care before re-hospitalisation, contraceptive and fertility studies etc. If you ve ever used regression analysis on longitudinal event data, you ve probably come up against two intractable problems.
Censoring Nearly every sample contains some cases that do not experience an event. If the dependent variable is the time of the event, what do you do with these censored cases. Time-dependent covariate Many explanatory variables like income or blood pressure change in value over time. How do you put such variables in a regression analysis. Makeshift solutions to these questions can lead to severe biases. Survival methods are explicitly designed to deal with censoring and time-dependent covariates in a statistically correct way.
Originally developed by biostatisticians, these methods have become popular in sociology, demography, psychology, economics, political science, and marketing. In Short, survival Analysis is a group of statistical methods for analysis and interpretation of survival data. Even though survival analysis can be used in a wide variety of applications e.
insurance, engineering, and sociologythe main application is for analyzing clinical trials data. Survival and hazard functions, the methods of estimating parameters and testing hypotheses that are the main part of analyses of survival data. Main topics relevant to survival data analysis are Survival and hazard functions, Types of censoring, Estimation of survival and hazard functions the Kaplan-Meier and life table estimators, Simple life tables, Peto s Logrank with trend test and hazard ratios and Wilcoxon test, can be stratifiedWei-Lachin, Comparison of survival functions The logrank and Mantel-Haenszel tests, The proportional hazards model time independent and time dependent covariates, The logistic regression model, and Methods for determining sample sizes.
In the last few years the survival analysis software available in several of the standard statistical packages has experienced a major increment in functionality, and is no longer limited to the triad of Kaplan-Meier curves, logrank tests, and simple Cox models. Further Readings Hosmer D. Lemeshow, Applied Survival Analysis Regression Modeling of Time to Event DataWiley, 1999. Swanepoel, and N. Veraverbeke, The modified bootstrap error process for Kaplan-Meier quantiles, Statistics Probability Letters58, 31-39, 2002.Survival Analysis A Self-Learning TextSpringer-Verlag, New York, 1996.Statistical Methods for Survival Data AnalysisWiley, 1992.
Grambsch, Modeling Survival Data Extending the Cox ModelSpringer 2000. This book provides thorough discussion on Cox PH model. Since the first author is also the author of the survival package in S-PLUS R, the book can be used closely with the packages in addition to SAS. Association Among Nominal Variables. Spearman s Correlation, and Kendall s tau Application. Two measures are Spearman s rank order correlation, and Kendall s tau. Further Readings For more details see, e.
Is Var1 ordered the same as Var2.Fundamental Statistics for the Behavioral Sciencesby David C. Howell, Duxbury Pr. Repeated Measures and Longitudinal Data. For those items yielding a score on a scale, the conventional t-test for correlated samples would be appropriate, or the Wilcoxon signed-ranks test. What Is a Systematic Review.
There are few important questions in health care which can be informed by consulting the result of a single empirical study. Systematic reviews attempt to provide answers to such problems by identifying and appraising all available studies within the relevant focus and synthesizing their results, all according to explicit methodologies. The review process places special emphasis on assessing and maximizing the value of data, both in issues of reducing bias and minimizing random error. The systematic review method is most suitably applied to questions of patient treatment and management, although it has also been applied to answer questions regarding the value of diagnostic test results, likely prognoses and the cost-effectiveness of health care.
Information Theory. Shannon defined a measure of entropy as. that, when applied to an information source, could determine the capacity of the channel required to transmit the source as encoded binary digits. Shannon s measure of entropy is taken as a measure of the information contained in a message. This is unlike to the portion of the message that is strictly determined hence predictable by inherent structures. Entropy as defined by Shannon is closely related to entropy as defined by physicists in statistical thermodynamics.
This work was the inspiration for adopting the term entropy in information theory. Other useful measures of information include mutual information which is a measure of the correlation between two event sets. Mutual information is defined for two events X and Y as. M X, Y H X, Y - H X - H Y. where H X, Y is the join entropy defined as.
Coments:25.01.2020 : 04:10 Shasar:
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21.01.2020 : 22:23 Shakall:
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