Introduction to Probability and Statistics Using R
... This book was expanded from lecture materials I use in a one semester upper-division undergraduate course entitled Probability and Statistics at Youngstown State University. Those lecture materials, in turn, were based on notes that I transcribed as a graduate student at Bowling Green State Universi ...
... This book was expanded from lecture materials I use in a one semester upper-division undergraduate course entitled Probability and Statistics at Youngstown State University. Those lecture materials, in turn, were based on notes that I transcribed as a graduate student at Bowling Green State Universi ...
Chapter_12_13_1
... Commonly the explanatory variable is placed on the row and the response variable on the vertical. So for this data Gender would be explaining the attitude toward monetary support. Conditional percents for rows are comprised by the taking a cell total divided by the row total; for columns this condit ...
... Commonly the explanatory variable is placed on the row and the response variable on the vertical. So for this data Gender would be explaining the attitude toward monetary support. Conditional percents for rows are comprised by the taking a cell total divided by the row total; for columns this condit ...
Chapter 5
... is talking to a person, the other callers are placed on hold. When all lines are in use, others who are trying to call in get a busy signal. The probability that 0, 1, 2, 3, or 4 people will get through is shown in the distribution. Find the variance and standard deviation for the distribution. ...
... is talking to a person, the other callers are placed on hold. When all lines are in use, others who are trying to call in get a busy signal. The probability that 0, 1, 2, 3, or 4 people will get through is shown in the distribution. Find the variance and standard deviation for the distribution. ...
RESEARCH METHODS
... 1. If you hypothesized that two groups would be different, then the appropriate statistical test is a) directional. b) not significant. c) one-tailed. d) significant. e) two-tailed. 2. The middle score in a set of scores is called the a) mean. b) median. c) mode. d) arithmetic average. e) central te ...
... 1. If you hypothesized that two groups would be different, then the appropriate statistical test is a) directional. b) not significant. c) one-tailed. d) significant. e) two-tailed. 2. The middle score in a set of scores is called the a) mean. b) median. c) mode. d) arithmetic average. e) central te ...
slides ch 5
... Discrete Probability What happens if the outcomes of an experiment are not equally likely? In that case, we assign a probability p(s) to each outcome sS, where S is the sample space. Two conditions have to be met: (1): 0 p(s) 1 for each sS, and (2): sS p(s) = 1 This means, as we already kno ...
... Discrete Probability What happens if the outcomes of an experiment are not equally likely? In that case, we assign a probability p(s) to each outcome sS, where S is the sample space. Two conditions have to be met: (1): 0 p(s) 1 for each sS, and (2): sS p(s) = 1 This means, as we already kno ...
Applying the Moment Generating Functions to the Study of
... distribution? The sample observations are frequently expressed as numerical events that corresponds to the values of the random variables. Certain types of random variables occurs frequently in practice, so it is useful to know the probability for each value of a random variable. The probability of ...
... distribution? The sample observations are frequently expressed as numerical events that corresponds to the values of the random variables. Certain types of random variables occurs frequently in practice, so it is useful to know the probability for each value of a random variable. The probability of ...
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.