Math notes 2nd 9wks pdf_1
... Intervals – Should not be too big or too small. They must be consecutive. They cannot overlap. Intervals should be equal. (Sometimes the last interval is not equal but this is not standard.) Frequency charts show where there are gaps or clusters of data. Easier to visualize data. Tally – must match ...
... Intervals – Should not be too big or too small. They must be consecutive. They cannot overlap. Intervals should be equal. (Sometimes the last interval is not equal but this is not standard.) Frequency charts show where there are gaps or clusters of data. Easier to visualize data. Tally – must match ...
K.K. Gan Physics 416 Problem Set 1 Due Monday, April 9, 2012
... b) Assuming that the experimenters expected, on average, two neutrino interactions per 24 hours what is the probability of observing eight or more neutrino interactions in a ten minute time interval (this is what was observed!)? 8) If a constant c is added to each xi in a sample (i = 1, n) such that ...
... b) Assuming that the experimenters expected, on average, two neutrino interactions per 24 hours what is the probability of observing eight or more neutrino interactions in a ten minute time interval (this is what was observed!)? 8) If a constant c is added to each xi in a sample (i = 1, n) such that ...
Exercise 4
... is a probability measure on (Ω, F ). 3. A laboratory blood test is 95% effective in detecting a certain disease when it is present. However, the test also yields a ‘false positive’ result for 2% of healthy people tested. If 0.1% of the population actually have the disease, what is the probability th ...
... is a probability measure on (Ω, F ). 3. A laboratory blood test is 95% effective in detecting a certain disease when it is present. However, the test also yields a ‘false positive’ result for 2% of healthy people tested. If 0.1% of the population actually have the disease, what is the probability th ...
Brain size and Intelligence - Neas
... perfect example of a non-textbook situation where the results don't always turn out perfectly. The limited size of the data set creates problems of validity and accuracy, especially when splitting the data by gender. That is, a greater number of data points will increase the validity of the regressi ...
... perfect example of a non-textbook situation where the results don't always turn out perfectly. The limited size of the data set creates problems of validity and accuracy, especially when splitting the data by gender. That is, a greater number of data points will increase the validity of the regressi ...
Document
... the probability P( An ) P( xn ) • Since N in this situation, P( An ) 0 • Thus, the probability of a discrete event defined on a continuous sample space is 0 • Events can occur even if their probability is 0 • Not the same as the impossible event ...
... the probability P( An ) P( xn ) • Since N in this situation, P( An ) 0 • Thus, the probability of a discrete event defined on a continuous sample space is 0 • Events can occur even if their probability is 0 • Not the same as the impossible event ...
sample means
... Sample proportions arise most often when we are interested in categorical variables. We then ask questions like “What proportion of U.S. adults have watched Survivor II?” Because sample means are just averages of observations, they are among the most common statistic. ...
... Sample proportions arise most often when we are interested in categorical variables. We then ask questions like “What proportion of U.S. adults have watched Survivor II?” Because sample means are just averages of observations, they are among the most common statistic. ...
5.1 Probability overview (Answer in notes)
... Students in a college statistics class wanted to find out how common it is for young adults to have their ears pierced. They recorded data on two variables – gender and whether the student had a pierced ear – for all 178 people in the class, 90 males and 88 females. Create a two-way table, given t ...
... Students in a college statistics class wanted to find out how common it is for young adults to have their ears pierced. They recorded data on two variables – gender and whether the student had a pierced ear – for all 178 people in the class, 90 males and 88 females. Create a two-way table, given t ...
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.