**FARMING SIMULATOR 2013 MODS DOWNLOAD UTORRENT**Your custom UIs is a pretty unique relative to your computer without settings dialogs, but have them automatically. Dear Support Team, - No general June 23, Juan. Understanding of networking improves reporting capabilities extended using High-Perf. Figure D Link very fast anywhere you the ability to see the and its additional. It's all regarding most common license.

Also, check out Ox , described in the Programming Languages section below. Data Desk -- first released in , is one of the oldest Mac programs still actively developed. Wizard -- for Macintosh only , provides all basic statistical tests, emphasizes easy-to-understand, graphically-based results. Includes equivalence- and non-inferiority testing for most tests. For Six Sigma work: gage linearity and accuracy analysis, multi-vari charts, life data regression for reliability analysis and accelerated life-testing, long-term and short-term capability assessment estimates.

Two free downloads are available: full-function but limited-time 30 days , and unlimited-time but limited-function no Save, no Print, not all analyses. Free 7-day evaluation versions. The free Version 12 Demo expires after 30 days. Prism Performs basic biostatistics, fits curves and creates publication quality scientific graphs in one complete package Mac and Windows.

Windows demo is fully-functional for 30 days, then disables printing, saving and exporting; Mac demo always disables these functions. CoStat -- an easy-to-use program for data manipulation and statistical analysis, from CoHort Software. Use a spreadsheet with any number of columns and rows of data: floating point, integer, date, time, degrees, text, etc. Has ANOVA, multiple comparisons of means, correlation, descriptive statistics, analysis of frequency data, miscellaneous tests of hypotheses, nonparametric tests, regression curve fitting , statistical tables, and utilities.

Has an auto-recorder and macro programming language. Callable from the command line, batch files, shell scripts, pipes, and other programs; can be used as the statistics engine for web applications. Free time-limited demo available.

Subset Packages: Each of these programs deals with a specific area of statistics such as power analysis or mulitvariate analysis , or carries out a specific test or computation. It has some simple routines and menus, but it is also programmable for more sophisticated analyses.

Its routines are pretty powerful. Data Preparator -- handles the "pre-processing" chores of getting a data file ready for analysis The free demo has all features enabled, and will handle up to cases. Statistics Problem Solver -- tutoring software that not only solves statistical problems, but also generates step-by-step solutions in order to help students understand how to solve statistical problems. Graphs can be customized in color, scale, resolution, etc.

Also calculates slope, area under the curve, tracing and matrix transformation. Calculus Problem Solver -- differentiates any arbitrary equation and outputs the result, providing detailed step-by-step solutions in a tutorial-like format. Can also initiate an interactive quiz in which you can solve differentiation while the computer corrects your solutions. ZeroRejects -- Implements the "Six Sigma" statistical process control methodology developed by Motorola.

The alpha and beta version are freely downloadable. WinSPC day free trial -- statistical process control software to:. Includes equivalence- and non-inferiority testing for most tests, Monte Carlo simulation for small samples; group sequential interim analyses.

Design-Ease and Design-Expert -- two programs from Stat-Ease that specialize in the design of experiments. Full-function day evaluation copies of both programs are available for download. Has a comprehensive web-based tutorial and reference manual. Factor -- a comprehensive factor analysis program. Provides univariate and multivariate descriptive statistics of input variables mean, variance, skewness, kurtosis , Var charts for ordinal variables, dispersion matrices user defined , covariance, pearson correlation, polychoric correlation matrix with optional Ridge estimates.

Provides mean, variance and histogram of fitted and standardized residuals, and automatic detection of large standardized residuals. You can download the Version 3. To obtain a free copy of the program and manual, send an e-mail to the custodians: Statistics-Chemometrics shell. Weka -- a collection of machine learning algorithms for data mining tasks, implemented in Java. Can be executed from a command-line environment, or from a graphical interface, or can either be called from your own Java code.

Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization, and is well-suited for developing new machine learning schemes. StatCalc -- a PC calculator that computes table values and other statistics for 34 probability distributions. Also includes some nonparametric table values , tolerance factors , and bivariate normal distribution.

A help file is provided for each distribution. Scientific Calculator - ScienCalc program contains high-performance arithmetic, trigonometric, hyperbolic and transcendental calculation routines. All the function routines therein map directly to Intel FPU floating point machine instructions.

EqPlot -- Equation graph plotter program plots 2D graphs from equations. The application comprises algebraic, trigonometric, hyperbolic and transcendental functions. PCP Pattern Classification Program -- a machine-learning program for supervised classification of patterns vectors of measurements. Supports interactive keyboard-driven menus and batch processing.

Last updated in , and the website says it's no longer being developed. An augmented Windows version Aug. EXE - For comparisons of two independent groups or samples. The current version number is 3. EXE - For use in descriptive epidemiology including the appraisal of separate samples in comparative studies. EXE - Miscellaneous randomization, random sampling, adjustment of multiple-test p-values, appraisal of synergism, assessment of a scale, correlation-coefficient tools, large contingency tables, three-way tables, median polish and mean polish, appraisal of effect of unmeasured confounders.

EXE - Multiple logistic regression. The current version number is 1. EXE - For appraisal of differences and agreement between matched samples or observations. EXE - Multiple Poisson regression. EXE - An expression evaluator with storage of constants, interim results, and formulae and calculator for p values and their inverse , confidence intervals, and time spans.

The current version number is 4. Provides sophisticated methods in a friendly interface. TETRAD is limited to models The TETRAD programs describe causal models in three distinct parts or stages: a picture, representing a directed graph specifying hypothetical causal relations among the variables; a specification of the family of probability distributions and kinds of parameters associated with the graphical model; and a specification of the numerical values of those parameters.

EpiData -- a comprehensive yet simple tool for documented data entry. Overall frequency tables codebook and listing of data included, but no statistical analysis tools. Calculate sample size required for a given confidence interval, or confidence interval for a given sample size. Can handle finite populations.

Online calculator also available. Biomapper -- a kit of GIS and statistical tools designed to build habitat suitability HS models and maps for any kind of animal or plant. Deals with: preparing ecogeographical maps for use as input for ENFA e. Graphical displays include an automatic collection of elementary graphics corresponding to groups of rows or to columns in the data table, automatic k-table graphics and geographical mapping options, searching, zooming, selection of points, and display of data values on factor maps.

Simple and homogeneous user interface. Weibull Trend Toolkit -- Fits a Weibull distribution function like a normal distribution, but more flexible to a set of data points by matching the skewness of the data. Command-line interface versions available for major computer platform; a Windows version, WinBUGS, supports a graphical user interface, on-line monitoring and convergence diagnostics.

GUIDE is a multi-purpose machine learning algorithm for constructing classification and regression trees. Incredibly powerful and multi-featured program for data manipulation and analysis. Designed for econometrics, but useful in many other disciplines as well. Creates output modelss as LaTeX files, in tabular or equation format. Has an integrated scripting language: enter commands either via the gui or via script, command loop structure for Monte Carlo simulations and iterative estimation procedures, GUI controller for fine-tuning Gnuplot graphs, Link to GNU R for further data analysis.

Includes a sample US macro database. See also the gretl data page. Originally designed for survival models, but the language has evolved into a general-purpose tool for building and estimating general likelihood models. Joinpoint Trend Analysis Software from the National Cancer Institute -- for the analysis of trends using joinpoint models where several different lines are connected together at the "joinpoints.

Takes trend data e. Models may incorporate estimated variation for each point e. In addition, the models may also be linear on the log of the response e. The software also allows viewing one graph for each joinpoint model, from the model with the minimum number of joinpoints to the model with maximum number of joinpoints. DTREG generates classification and regression decision trees. It uses V-fold cross-valication with pruning to generate the optimal size tree, and it uses surrogate splitters to handle missing data.

A free demonstration copy is available for download. NLREG performs general nonlinear regression. NLREG will fit a general function, whose form you specify, to a set of data values. Origin -- technical graphics and data analysis software for Windows. Biostatistics and Epidemiology: Completely Free OpenEpi Version 2. Anderson Statistical Software Library -- A large collection of free statistical software almost 70 programs!

Anderson Cancer Center. Performs power, sample size, and related calculations needed to plan studies. Covers a wide variety of situations, including studies whose outcomes involve the Binomial, Poisson, Normal, and log-normal distributions, or are survival times or correlation coefficients.

Two populations can be compared using direct and indirect standardization, the SMR and CMF and by comparing two lifetables. Confidence intervals and statistical test are provided. There is an extensive helpfile in which everything is explained. Lifetables is listed in the Downloads section of the QuantitativeSkills web site.

Sample Size for Microarray Experiments -- compute how many samples needed for a microarray experiment to find genes that are differentially expressed between two kinds of samples e. This is a stand-alone Windows 95 through XP program that receives information about dose-limiting toxicities DLTs observed at some starting dose, and calculates the doses to be administered next. DLT information obtained at each dosing level guides the calculation of the next dose level.

Epi Info has been in existence for over 20 years and is currently available for Microsoft Windows. The program allows for data entry and analysis. Within the analysis module, analytic routines include t-tests, ANOVA, nonparametric statistics, cross tabulations and stratification with estimates of odds ratios, risk ratios, and risk differences, logistic regression conditional and unconditional , survival analysis Kaplan Meier and Cox proportional hazard , and analysis of complex survey data.

Limited support is available. The calculation of person-years allows flexible stratification by sex, and self-defined and unrestricted calendar periods and age groups, and can lag person-years to account for latency periods.

Developed by Eurostat to facilitate the application of these modern time series techniques to large-scale sets of time series and in the explicit consideration of the needs of production units in statistical institutes. Contains two main modules: seasonal adjustment and trend estimation with an automated procedure e.

Ideal for learning meta-analysis reproduces the data, calculations, and graphs of virtually all data sets from the most authoritative meta-analysis books, and lets you analyze your own data "by the book". Generates numerous plots: tandard and cumulative forest, p-value function, four funnel types, several funnel regression types, exclusion sensitivity, Galbraith, L'Abbe, Baujat, modeling sensitivity, and Trim-and-Fill.

Surveys, Testing, and Measurement: Completely Free CCOUNT -- a package for market research data cleaning, manipulation, cross tabulation and data analysis. IMPS Integrated Microcomputer Processing System -- performs the major tasks in survey and census data processing: data entry, data editing, tabulation, data dissemination, statistical analysis and data capture control. Stats 2. SABRE -- for the statistical analysis of multi-process random effect response data. Responses can be binary, ordinal, count and linear recurrent events; response sequences can be of different types.

Such multi-process data is common in many research areas, e. Sabre has been used intensively on many longitudinal datasets surveys either with recurrent information collected over time or with a clustered sampling scheme. Last released in Mac, K; Win anticipated in September. NewMDSX -- software for Multidimensional Scaling MDS , a term that refers to a family of models where the structure in a set of data is represented graphically by the relationships between a set of points in a space.

MDS can be used on a variety of data, using different models and allowing different assumptions about the level of measurement. SuperSurvey -- to design andimplement surveys, and to acquire, manage and analyze data from surveys. Optional Web Survey Module and Advanced Statistics Module curve fitting, multiple regression, logistic regression, factor, analysis of variance, discriminant function, cluster, and canonical correlation. Free version is limited to 1 survey, 10 questions, 25 total responses.

Rasch Measurement Software -- deals with the various nuances of constructing optimal rating scales from a number of usually dichotomous measurements, such as responses to questions in a survey or test. These may be freely downloaded, used, and distributed, and they do not expire. This Excel spreadsheet converts confidence intervals to p values, and this PDF file explains it's background and use. RegressIt - An Excel add-in for teaching and applied work.

Performs multivariate descriptive analysis and ordinary linear regression. Creates presentation-quality charts in native editable Excel format, intelligently formatted tables, high quality scatterplot matrices, parallel time series plots of many variables, summary statistics, and correlation matrices.

Easily explore variations on models, apply nonlinear and time transformations to variables, test model assumptions, and generate out-of-sample forecasts. SimulAr -- Provides a very elegant point-and-click graphical interface that makes it easy to generate random variables correlated or uncorrelated from twenty different distributions, run Monte-Carlo simulations, and generate extensive tabulations and elegant graphical displays of the results.

EZAnalyze -- enhances Excel Mac and PC by adding "point and click" functionality for analyzing data and creating graphs no formula entry required. Does all basic "descriptive statistics" mean, median, standard deviation, and range , and "disaggregates" data breaks it down by categories , with results shown as tables or disaggregation graphs".

Advanced features: correlation; one-sample, independent samples, and paired samples t-tests; chi square; and single factor ANOVA. Update Available! EZ-R Stats -- supports a variety of analytical techniques, such as: Benford's law, univariate stats, cross-tabs, histograms. Simplifies the analysis of large volumes of data, enhances audit planning by better characterizing data, identifies potential audit exceptions and facilitates reporting and analysis. Marko Lucijanic's Excel spreadsheet to perform Log Rank test on survival data, and his article.

SSC-Stat -- an Excel add-in designed to strengthen those areas where the spreadsheet package is already strong, principally in the areas of data management, graphics and descriptive statistics. SSC-Stat is especially useful for datasets in which there are columns indicating different groups. Menu features within SSC-Stat can:.

Each spreadsheet gives a graph of the distribution, along with the value of various parameters, for whatever shape and scale parameters you specify. You can also download a file containing all 22 spreadsheets. Sample-size calculator for cluster randomized controlled trials , which are used when the outcomes are not completely independent of each other. This independence assumption is violated in cluster randomized trials because subjects within any one cluster are more likely to respond in a similar manner.

A measure of this similarity is known as the intra-correlation coefficient ICC. Because of the lack of independence, sample sizes have to be increased. This web site contains two tools to aid the design of cluster trials — a database of ICCs and a sample size calculator along with instruction manuals. Exact confidence intervals for samples from the Binomial and Poisson distributions -- an Excel spreadsheet with several built-in functions for calculating probabilities and confidence intervals.

Smith , of Virginia Tech. A user-friendly add-in for Excel to draw a biplot display a graph of row and column markers from data that forms a two-way table based on results from principal components analysis, correspondence analysis, canonical discriminant analysis, metric multidimensional scaling, redundancy analysis, canonical correlation analysis or canonical correspondence analysis. Allows for a variety of transformations of the data prior to the singular value decomposition and scaling of the markers following the decomposition.

Lifetable -- does a full abridged current life table analysis to obtain the life expectancy of a population. From the Downloads section of the QuantitativeSkills web site. A third spreadsheet concerns a method for two clusters by Donner and Klar. You will have to insert your own data by overwriting the tables in the second total number of positive responses and third total number of negative responses or fourth column total number.

A step-by-step guide to data analysis with separate workbooks for handling data with different numbers and types of variables. XLStatistics is not an Excel add-in and all the working and code is visible. A free version for analysis of 1- and 2-variable data is available. XLSTAT -- an Excel add-in for PC and MAC that holds more than statistical features including data visualization, multivariate data analysis, modeling, machine learning, statistical tests as well as field-oriented solutions: features for sensory data analysis preference mapping , time series analysis forecasting , marketing conjoint analysis, PLS structural equation modeling , biostatistics survival analysis, OMICs data analysis and more.

It proposes a free day trial of all features as well as a free version. Statistics -- executes programs written in the easy-to-learn Resampling Stats statistical simulation language. You write a short, simple program in the language, describing the process behind a probability or statistics problem. Statistics then executes your Resampling Stats model thousands of times, each time with different random numbers or samples, keeping track of the results.

When the program completes, you have your answer. Runs on Windows, Mac, Lunux -- any system that supports Java. R -- a programming language and environment for statistical computing and graphics. Similar to S or S-plus will run most S code unchanged.

Provides a wide variety of statistical linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, Well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

The R environment includes:. Medical students themselves [ 5 , 6 ] understand the need to learn statistics, especially as an essential language to understand the medical literature and as a tool that helps in clinical decision-making. Colditz and Emerson [ 1 ] identified an emerging need for the development of a biostatistical core curriculum for medical students.

Alas, this need has yet to be fulfilled. In contrast to those who advocate for adding biostatistics to medical curricula, Simpson [ 8 ] believes it is anachronistic and unrelated to the real aim of clinical decision-making. This debate is ongoing, and no side has had the final word about what and how we should teach biostatistics [ 9 — 11 ].

Mintz and Ostbye [ 12 ] believe that even though the biostatistics field is considered important, it is not rewarding for those who teach it to medical students. The most prevalent method of teaching biostatistics to medical students is still the lecture system, in which the main emphasis is on mathematical formulas, rather than their proper usage [ 13 ].

Looney et al. Appleton [ 15 ] believes that the emphasis in medical statistics teaching is erroneous—the courses are too long and detailed and are of no real relevance to the students. Mintz and Ostbye [ 12 ] agree with that and identified key concepts that are essential for any statistics usage. Dixon [ 16 ] also believes a core curriculum in biostatistics is feasible after he evaluated 36 different courses across the UK. Clayden [ 7 ] described several essential components: coordination between biostatistics experts and medical educators, use of computer programs, and proper evaluation formats to evaluate their success.

Dixon [ 16 ] also calls for the use and evaluation of computer-based biostatistics tutoring. Ostbye [ 17 ] created such a course in the s and found it to be efficient and cost-effective. Knapp and Miller III [ 18 ] thought that teaching biostatistics to medical students with computers was both feasible and effective because of the widespread use of computers in medical practice, as well as the availability of user-friendly statistical programs.

Although they studied research clinicians rather than medical students, Hutton Jr. Henshaw [ 20 ] has mapped the main challenges regarding teaching biostatistics to nurses, and we followed his footsteps in mapping the main problems regarding teaching computerized biostatistics to medical students. We believe, just as Henshaw does, that the proper way to help students become familiarized and knowledgeable in the field of biostatistics is helping them build their certainty and ability to manipulate data analysis.

As do Ambrosius and Manatunga [ 21 ], we do not want or need our medical students to be trained statisticians; however, we do believe that, in order to practice evidence-based medicine, one of the many essential skills they must learn is how to speak the language of statistics. Newcombe [ 22 ] believes that there has been no real evaluation process of biostatistics in the UK, and that formal debriefing of students regarding content and feelings about a course is insufficient.

Stone and Qualters [ 23 ] agree and call for a better evaluative process of medical education outcomes as part of the professionalism concept that is so popular today. In the present study we compared the skills before and after an intervention project as others have done before us [ 20 , 21 ]. Similar to Rosenbaum and Kreiter [ 25 ], we evaluated the effect of using computerized learning by using evaluative questionnaires before and after the workshop. Similar to Garg et al. Our primary assumption is that the ability to understand and use computerized data analysis is feasible for medical students and that we can evaluate it.

The main themes of the course evolved upon questions such as what is data analysis? How can we use computers for that? How can we deal with descriptive and analytical statistics, as well as understand computerized statistical output? The course was structured as a two-week, hour workshop, which was designed for medical students in their 4th year of studies in a 6 year program , who had an introductory course in biostatistics during their first year of medical school.

The workshop included the following: classroom lectures; working with real data files from lecturers prior studies —coding and variable definitions; simulations; and use of computer data analysis. For students to be exposed to the benefits of multi-disciplinary statistical applications, faculty included the following: epidemiologists, biostatistics experts, and advanced years medical students. At the end of the course, students were evaluated through their exposure to an unknown data file, on which they were to do the data analysis.

The course is given during the first semester. We also wanted to see if the course was successful in developing and enhancing a new skill, beyond the subjective level of the personal experiences of the students. We also evaluated whether the course was able to dispel the anxiety and feeling of incompetence so often associated with data analysis in general and with its computerized part.

The course is mandatory, so the subjects of the study were all 4th year students during the academic year , at the Faculty of Health Sciences of Ben-Gurion University of The Negev, Israel. We used 6-point Likert scale questionnaires to evaluate our subjects. They were paper based and anonymous and were designed especially for our study. These questionnaires were based on prior studies [ 26 , 27 ] that deployed similar aspects of teaching and evaluation. Content validity was checked by three expert evaluators.

There were 11 multiple choice objective questions. Each question had a set of answers, only one of which was true, and each answer was validated by three experts. We then counted the number of correct answers. The second part of the questionnaire was mainly subjective and referred to the feelings and perceived capabilities of the responders.

The second subjective part included 18 questions such as As a physician, to what extent do you think there is a need to learn statistics? Or, In general, how do you evaluate your ability to use statistics? The third part was mainly objective and evaluated content knowledge such as: What is the proper way to evaluate the association between 2 nominal variables?

With answers like chi square, Kappa, and Spearman Rho. On the first day of lectures, students completed the precourse questionnaires, which helped us gather key demographic data and information regarding their general opinions towards statistics, data analysis, and the course. The precourse questionnaire also included content specific questions in the application of biostatistics tests see Table 3. At the end of the course, students completed postcourse questionnaires regarding the course as was done in the past , as well as a questionnaire complementary to those they completed at the beginning of the course—there were questionnaires regarding feelings of readiness and competence in data analysis, how they intend to manage such an analysis in their own thesis and questions regarding how long they think these skills will last.

In the analytical part, we had to eliminate sources of error that might have biased our results and so we discarded the subjects who completed the study questionnaires after hearing an introductory lecture on statistics, making them familiar with the study questions and thus helping them find the right answers—8 subjects were discarded. We also had to discard those who did not answer both the before and the after questionnaires, so we could have a truly paired analysis of our results.

We analyzed the data first by using descriptive statistics mean and SD, graphs and then moved to analytical statistics using paired parametric e. From the 61 students enrolled, 34 precourse and 56 postcourse questionnaires were collected. Of the 34 precourse questionnaires, eight were completed by students who had listened to an introductory lecture on biostatistics and thus could have been biased.

Of the questionnaires answered, Most The average age of responders was Looking at our data, one gets the impression that our subjects believe that they are quite proficient with computer use and data analysis and lack any fear of computers or statistics Table 1. But this is far from true, because this involves both before and after evaluations. When performing a subgroup analysis, one gets a different understanding see Table 2 for the subgroup analysis. One can see from these results that some of the attitudes towards statistics and computer use have changed and one can assume that the course had an effect on the attitudes of our students, even though not in all fields examined.

Our students became more aware of the need to learn and use statistics a graphic visualization of this is depicted in Figure 1 ; they wanted to get more training in that field, the course enhanced their capabilities in that area, and they became more proficient in the use of computerized statistics as well as their ability to read articles using uni- and multivariate analysis. One surprise was in the fear of computers, which became a bit more prominent after the course, even though this was not statistically significant.

The third part of our questionnaire was an objective one—when we did a subgroup analysis we found that most of the correct answers were after the course , compared with only 0. A visual presentation of the magnitude of this change is seen in Figure 2. For comparing the pre- and posttest, we ended up with 31 subjects who have filled all questionnaires—this is due to the fact that not all students who completed the questionnaires both pre- and postcourse were able for example, 2 subjects had given birth during the course, etc.

The results are shown in Table 2. We found the following comparisons to be significant: the extent to which they as physicians need to learn statistics, the need to get a thorough education in statistics as a physician, the ability to use computerized statistics, their ability to read articles using univariate analysis, and their ability to read articles using multivariate analysis. All these changed in the hypothesized direction.

A similar trend was found in the objective part see Table 3. For all the questions, we found a significant change; all were in the hypothesized direction—higher percentage of correct answers. We also compared the total scores on the pre- and postcourse quizzes and found a significant difference among our pairs. It is worth mentioning that two questions showed little improvement at the end of the course—a question about survival and a question about multivariate analysis, both of which are more advanced and complicated statistical issues.

We believe that the qualitative aspect of our course is of importance, and thus we also surveyed the written remarks of our students. A theme that repeated was that even though the contents of the course are not easy and even scary, the need to do a realistic test is essential, because only then could they confront the learned material and realize it is not as frightening as first perceived.

Another important theme was the importance and efficacy of integrating clinical contents and real life studies, as the students saw those as anchors to their lives and a way to clarify the relevance of the course to their professional career. As mentioned, we believe that the proper way to help students become familiarized and knowledgeable in the field of biostatistics is helping them build their confidence in their ability to manipulate data analysis.

Our results suggest that this has been accomplished and that our students feel more at ease conducting and interpreting statistical data and procedures. This is seen both in the subjective part of our study, as well as in the objective part in which the improvement was very impressive. As Ambrosius and Manatunga [ 21 ] did, we introduced our students to a new language—the statistical language, a language that hopefully will help them understand data analysis and common statistical procedures done by others or by themselves in the future.

With it came an aim of the study itself—can we accomplish this, and is this change measurable? We found that after the course students felt more competent and more ready to tackle the research project that is part of the requirements for their M. Another issue we pondered was whether students understand and use computerized data analysis, and if can we evaluate this process; the answer to these questions is a profound yes—our comparisons of pre- and postcourse questionnaires demonstrated that students felt that their ability to run data analyses on their own had improved.

They had no problem answering the study questionnaires as they went along this was our personal impression both as lecturers of the course and as the study coordinators , so we can conclude that an evaluation of the process is both feasible and efficient. As the qualitative analysis has shown us, the need to do a realistic test is essential, because only then could they confront the learned material, and the importance and efficacy of integrating clinical contents and real life studies was essential for making the course a successful one.

We can sum up by saying that the course was successful in developing and enhancing a new skill—computerized data analysis. This was achieved not only at the subjective level of feelings of readiness and competence. We believe that the course was able to dispel the anxiety and feeling of incompetence so often associated with data analysis in general, and especially with its deep involvement of computers.

This was also achieved in the objective level of finding the right statistical procedure for a complex set of situations, which are practical, real life situations. Our findings align with other studies. Since the s, Knapp and Miller III [ 18 ] thought that teaching biostatistics to medical students with computers was both feasible and effective because of the widespread use of computers in medical practice, as well as the availability of user-friendly statistical programs.

Ostbye [ 17 ] has shown, that such a course is effective in achieving the study aims which were teaching students statistics without the awe that usually engulf it just as we did. Hutton Jr. Hewett and Porpora [ 24 ] reported their experience in a course in which the students are not only taught with the aid of computerized lessons, but they have to do an entire project, including data analysis, similar to what has been found in our study.

Medical students themselves [ 5 , 6 ] do understand the need to learn biostatistics, a theme that was also shown in our study. It seems that one of the keys to a successful computer-based course is its being intriguing and interesting enough for the students. As Melnyk and Fineout-Overholt [ 27 ] have shown, you have to ask the right question. A qualitative theme that arose from our debriefing was the importance and efficacy of integrating clinical contents and real life studies, as the students saw those as anchors to their lives and a way to clarify the relevance of the course to their professional career.

If the students find the course to be relevant to their lives it is more appealing and less intimidating, and so the real challenge is to find the relevant cases, studies, and articles so they can immerse themselves in them. One issue that must be stated is the issue of feasibility and costs involved in such a course. One might ponder why not to combine our 1st and 4th year statistics courses into one and thus reduce costs and shorten time until we have new physicians to work.

This question was asked a dozen times by our administrators who wanted to reduce costs. But, our motto in the Ben-Gurion Medical School talks about the spiral of learning. This means you get acquainted as an undergraduate with all kinds of medical terms which you will learn as time goes by to understand more thoroughly as your knowledge base broadens.

This also means that with time you become acquainted with more advanced teaching methods, and thus you are able to expand your knowledge and know how to be a better clinician and researcher. This approach has been used in our institute for 3 decades and has become world renowned [ 28 — 30 ]. No study is devoid of shortcomings, and ours has its own.

First and foremost, we still lack the answer to the question of whether the newly acquired skill is long lasting.

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