stats2
... Chi-square allows you to test whether an observed sample distribution fits some given distribution. Are the groups in your cross tab independent? Z and t tests are used to determine if the means or proportions of two samples are significantly different. Simple correlation measures the relationship o ...
... Chi-square allows you to test whether an observed sample distribution fits some given distribution. Are the groups in your cross tab independent? Z and t tests are used to determine if the means or proportions of two samples are significantly different. Simple correlation measures the relationship o ...
Chapter 7 – The Normal Probability Distribution
... Branches of statistics: Descriptive, Inferential Types of data: 1) Attribute, or qualitative 2) Numerical, or quantitative (discrete or continuous) Representative sample v. census Simple random sample of size n; how to select such a sample Sampling error v. nonsampling error Designed experiment v. o ...
... Branches of statistics: Descriptive, Inferential Types of data: 1) Attribute, or qualitative 2) Numerical, or quantitative (discrete or continuous) Representative sample v. census Simple random sample of size n; how to select such a sample Sampling error v. nonsampling error Designed experiment v. o ...
Test Code MS (Short answer type) 2009 Syllabus for Mathematics
... 225 customers to reserve for a flight. What is the probability that the flight will be at least 95% full? (Use any appropriate approximation and clearly state the relevant result/s.) 11. A simple random sample of size n = n1 + n2 is drawn without replacement from a finite population of size N . Furt ...
... 225 customers to reserve for a flight. What is the probability that the flight will be at least 95% full? (Use any appropriate approximation and clearly state the relevant result/s.) 11. A simple random sample of size n = n1 + n2 is drawn without replacement from a finite population of size N . Furt ...
Probability and statistics - Université catholique de Louvain
... variables, classical probability laws, random vectors, transformation of random variables, limit theorems. - Foundations of statistical inference : point estimation and fitting of distributions, estimation by confidence intervals, hypothesis testing, tests and confidence intervals for one or two sam ...
... variables, classical probability laws, random vectors, transformation of random variables, limit theorems. - Foundations of statistical inference : point estimation and fitting of distributions, estimation by confidence intervals, hypothesis testing, tests and confidence intervals for one or two sam ...
What hypothesis tests are not: a response to Colegrave and Ruxton
... or even negative. For example, the likelihood that the effect has a value 0.10 or less (the shaded area in each figure) is 0.115 for the first confidence interval versus 0.291 for the second, wider, one. The major point I made in my 1999 article was that the testing of statistical hypotheses (as opp ...
... or even negative. For example, the likelihood that the effect has a value 0.10 or less (the shaded area in each figure) is 0.115 for the first confidence interval versus 0.291 for the second, wider, one. The major point I made in my 1999 article was that the testing of statistical hypotheses (as opp ...
2inferstatsbasicconcepts_tcm4-134112
... They have come to the conclusion that, if the probability that an event could occur by chance is less than 1 in 20, they say the result is significant. ie: the result is not just a chance event. ...
... They have come to the conclusion that, if the probability that an event could occur by chance is less than 1 in 20, they say the result is significant. ie: the result is not just a chance event. ...
Module Title: Statistics
... The first part of the course includes a review of basic concepts of probability theory and a short introduction in Descriptive Statistics. The second part, presents the fundamental concepts of statistics used to inference effectively on the characteristics of a population based on samples. A random ...
... The first part of the course includes a review of basic concepts of probability theory and a short introduction in Descriptive Statistics. The second part, presents the fundamental concepts of statistics used to inference effectively on the characteristics of a population based on samples. A random ...
LOYOLA COLLEGE (AUTONOMOUS), CHENNAI 600 034. M.Sc. DEGREE EXAMINATION
... (ii) Obtain the MLE of the probability that a randomly selected family has atleast 3 boys. b) The following data relates to the results of a genetical experiment: Gene type Relative frequencies Observed frequencies I ...
... (ii) Obtain the MLE of the probability that a randomly selected family has atleast 3 boys. b) The following data relates to the results of a genetical experiment: Gene type Relative frequencies Observed frequencies I ...
Statistics
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as ""all persons living in a country"" or ""every atom composing a crystal"". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methodologies are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and a synthetic data drawn from idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a ""false positive"") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a ""false negative""). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data and/or censoring may result in biased estimates and specific techniques have been developed to address these problems.Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data.