formula comparative and interventional studies in statistics?
375229
Your question isn't very clear.  Try again.\n\nYou might be thinking of the different approaches that can be taken in descriptive studies and experimental studies.  Descriptive studies look for qualitative and quantitative differences between groups, and can be analyzed using very basic chi-square, analysis of variance, and regression techniques.  Such tests can establish associations and correlations among set of variables, but they cannot be used to evaluate causation, i.e. we could say that A is strongly correlated with B in a certain group, but we cannot say that A causes B or vice-versa.\n\nIn experimental studies, observations are made in such a way that statements about causality can be made, whether the outcome is a disease or a treament for that disease.  Experimental studies should always have at least two arms, one for the intervention or exposed group, and one for the control group.  The results can be analyzed in many different ways, including analysis of variance, multiple regression, logistic regression, proportional hazards models, and life table analysis.  The results permit definite statements about whether or not exposure A is likely to be a cause of disease B, or whether treatment A is more effective, less effective, or no different than treatment B or a placebo.\n\nI hope this helps.
228637
statistical methods are used to quantify quality in any process.  \n\nMinitab is a good software package for quality and charting that information.  Easy to use too!\n\nOf course Excel is the old standby.  Click on help to get a list of statistical functions.\n\nGood luck!
124967
Probability:\nA measure of how likely it is that some event will occur\n\nstatistics:\nA branch of applied mathematics concerned with the collection and interpretation of quantitative data. \n\nProbability theory is one of the tools that is used in statistics.\n\nhttp://www.futureaccountant.com/probability/\nhttp://www.schoolingkids.com/
268426
Gosh, I used that program years ago in grad school.  I don't remember it being too hard to pick up, especially since I started using it in conjunction with a statistics course.  I don't remember if there was a book that came with the program, but, I probably didn't read it anyway.  Our professor used the program during a few of our lectures, so everyone learned by seeing it in use.  Maybe your professor or the TA for the class can give a tutorial on the program.
276294
use the formula for confidence interval.. its found on most statistics book.. i would type the formula, but that wouldn't be helping you to be a better student..
251100
Yes!\n\nExperimental designs are efficient strategies for designing experiments using a minimum number of runs while enabling a maximum number of factors or parameters to be tested simultaneously. They also facilitate the statistical data analysis phase and the interpretation of results.\nCovers fundamental statistical methods for the Design of Experiments (DOE): accounting for various sources of variability, tools for minimizing and controlling variability, the comparison of two groups, the comparison of more than two groups, the Student's t-test, the Analysis of Variance (ANOVA), and the techniques of multiple comparisons for locating statistical differences.\n\n\nOne Way Analysis of Variance\n\nThe one way analysis of variance allows us to compare several groups of observations, all of which are independent but possibly with a different mean for each group. A test of great importance is whether or not all the means are equal.\n\n*********************\n\nThe observations all arise from one of several different groups (or have been exposed to one of several different treatments in an experiment). We are classifying 'one-way' according to the group or treatment.\n\n\nBasically ANOVA is just one tool to use, not unlike a t-test and design of experiments we are concerned with the analysis of data generated from an experiment. It is wise to take time and effort to organise the experiment properly to ensure that the right type of data, and enough of it, is available to answer the questions of interest as clearly and efficiently as possible. \n\nThe specific questions that the experiment is intended to answer must be clearly identified before carrying out the experiment. We should also attempt to identify known or expected sources of variability in the experimental units since one of the main aims of a designed experiment is to reduce the effect of these sources of variability on the answers to questions of interest. That is, we design the experiment in order to improve the precision of our answers.\n\nTreatment\n\nIn experiments, a treatment is something that researchers administer to experimantal units . For example, a corn field is divided into four, each part is 'treated' with a different fertiliser to see which produces the most corn; a teacher practices different teaching methods on different groups in her class to see which yields the best results; a doctor treats a patient with a skin condition with different creams to see which is most effective.\n\n*********************\n\nTreatments are administered to experimental units by 'level', where level implies amount or magnitude. For example, if the experimental units were given 5mg, 10mg, 15mg of a medication, those amounts would be three levels of the treatment. 'Level' is also used for categorical variables, such as Drugs A, B, and C, where the three are different kinds of drug, not different amounts of the same thing.\n\nFactor\n\nA factor of an experiment is a controlled independent variable; a variable whose levels are set by the experimenter.\n\n*********************\n\nA factor is a general type or category of treatments. Different treatments constitute different levels of a factor. For example, three different groups of runners are subjected to different training methods. The runners are the experimental units, the training methods, the treatments, where the three types of training methods constitute three levels of the factor 'type of training'.
1182335
Statistics Canada is the federal government's statistics department.  Good luck.
948494
The correct term is "order statistic" which is the set of values arranged in order of magnitude.  Also a set of successive components with identical values though is called a "run".\n\nProgression is term used in sequences and series.
920062
Standard deviation is a measure of the spread of a normally distributed variable. If the variable is not normally distributed, another measure should be used. \n\nRegression is a means of fitting an underlying function (usually a straight line) to a sample that shows a normal distribution around a central value at each point. Usually the independent variables to which the sample is being fitted are assumed not to have errors. For instance , you may fit say income (which varies) to age (which is known for each person in the sample).
1013660
Go to yahoo and it will help you do this homework  put in define \nstatistic and a parameter and it will tell you the difference.
1083815
"Descriptive statistics" are typically distinguished from inferential statistics. With descriptive statistics you are simply describing what is or what the data shows. With "inferential statistics", you are trying to reach conclusions that extend beyond the immediate data alone.\n\nScience deals with the natural world. Technology is the study of the natural laws which govern the universe.
654841
Follow the link below to see a chart comparing the two (and other terms).
1030781
By reading your question, it appears that you are first and foremost seeking to "define" mathmatically what it is that you are attempting to do to reach your results-- presumably so that you can study that area of statistics further and apply it to your experiment.\n\nWith that in mind, what you are dealing with in your experiment is called "Event Probability."  In Event Probability, you are dealing with concepts of "conditional probability", "frequency", and "relative frequency."  For instance, what you are doing is very similar to the flow charts that appear in many family's medical books with a "Decision Tree" that allows you to answer "Yes" and "No" questions along a Flow Chart to reach an ultimate probability that you have a particular illness.  In other words, you are creating an algorithm by measuring the "relative frequency" of events and compound events and drawing a conclusion from that information.  People who perform these experiments are called "frequentists."  For all intents and purposes, this is what actuaries do at insurance companies and financial firms.  I'd read about those areas of statistics first which should prove very helpful in providing you with a structure for reaching the most helpful and accurate conclusions from your experiment.
836301
Try this website for help with your statistics homework:\n\nhttp://www.statsoft.com/textbook/stathome.html
282573
Statics?  Statistics?
611755
Descriptive statistics is a branch of statistics that denotes any of the many techniques used to summarize a set of data. In a sense, we are using the data on members of a set to describe the set. The techniques are commonly classified as:\n\n- Graphical description in which we use graphs to summarize data. \n- Tabular description in which we use tables to summarize data. \n- Summary statistics in which we calculate certain values to summarize data. \n\nIn general, statistical data can be described as a list of subjects or units and the data associated with each of them. Although most research uses many data types for each unit, we will limit ourselves to just one data item each for this simple introduction.\n\nWe have two objectives for our summary:\n\n- We want to choose a statistic that shows how different units seem similar. Statistical textbooks call the solution to this objective, a measure of central tendency. \n- We want to choose another statistic that shows how they differ. This kind of statistic is often called a measure of statistical variability. \n\nWhen we are summarizing a quantity like length or weight or age, it is common to answer the first question with the arithmetic mean, the median, or the mode. Sometimes, we choose specific values from the cumulative distribution function called quantiles.\n\nThe most common measures of variability for quantitative data are the variance; its square root, the standard deviation; the range; interquartile range; and the absolute deviation.\n\nSteps in descriptive statistics\n- Collect data \n- Classify data \n- Summarize data \n- Present data \n- Proceed to inferential statistics if there is enough data to draw a conclusion.
64925
This is not the best place to ask for help. Meet your faculty advisor, or best - meet your Stats professor. Personally I think the best approach is to just take a book, and solve the problems in it to ace Stats.