CHAPTERS 4-6: Hypothesis Tests
... “Is the set of analyses appropriate? large enough sample? random sample?” • If an article claims their result was “not statistically significant”, they may try to imply that means there is no real difference, which may not be true! It could be they simply had too small of a sample size to detect the ...
... “Is the set of analyses appropriate? large enough sample? random sample?” • If an article claims their result was “not statistically significant”, they may try to imply that means there is no real difference, which may not be true! It could be they simply had too small of a sample size to detect the ...
Sampling Distribution of Means WS
... 3) A soft-drink bottle vendor claims that its production process yields bottles with a mean internal strength of 157 psi (pounds per square inch) and a standard deviation of 3 psi and is normally distributed. As part of its vendor surveillance, a bottler strikes an agreement with the vendor that per ...
... 3) A soft-drink bottle vendor claims that its production process yields bottles with a mean internal strength of 157 psi (pounds per square inch) and a standard deviation of 3 psi and is normally distributed. As part of its vendor surveillance, a bottler strikes an agreement with the vendor that per ...
Exam 3 Spring 12 - NCSU Statistics
... D) We are 90% confident that the sample proportion of the 159 sampled students who prefer American cars falls between .332 and .460. E) Between 0.332 and 0.446 of American cars are preferred by college students. 4) In one region of the country, the distribution of September energy consumption for si ...
... D) We are 90% confident that the sample proportion of the 159 sampled students who prefer American cars falls between .332 and .460. E) Between 0.332 and 0.446 of American cars are preferred by college students. 4) In one region of the country, the distribution of September energy consumption for si ...
Chapter Two
... Two main branches of statistics assist your decisions in different ways. Descriptive Statistics Inferential Statistics Inferential statistics are used to analyze data after you have conducted an experiment to determine whether your independent variable had a significant effect. ...
... Two main branches of statistics assist your decisions in different ways. Descriptive Statistics Inferential Statistics Inferential statistics are used to analyze data after you have conducted an experiment to determine whether your independent variable had a significant effect. ...
Lecture 7 - Stony Brook AMS
... Diseases D1, D2, and D3 cause symptom A with probabilities 0.5, 0.7, and 0.8, respectively. If 5% of a population have disease D1, 2% of a population have disease D2, and 3.5% of a population have disease D3, what percent of the population have symptom A? Assume that the only possible causes of symp ...
... Diseases D1, D2, and D3 cause symptom A with probabilities 0.5, 0.7, and 0.8, respectively. If 5% of a population have disease D1, 2% of a population have disease D2, and 3.5% of a population have disease D3, what percent of the population have symptom A? Assume that the only possible causes of symp ...
Hypothesis Testing - Large - Department of Management and
... actually true. Type I Error: Rejecting the null hypothesis, H0, when it is actually true. Type II Error: Accepting the null hypothesis, H0, when it is actually false. ...
... actually true. Type I Error: Rejecting the null hypothesis, H0, when it is actually true. Type II Error: Accepting the null hypothesis, H0, when it is actually false. ...
Practical 4 - Queen Mary University of London
... • use Minitab like a book of statistical tables to calculate quantities like P(2 ≤ X ≤ 6); • verify particular cases of the claims that hypergeometric random variables are approximately binomial and that binomial random variables are approximately Poisson. 1 (The probability mass function of a binom ...
... • use Minitab like a book of statistical tables to calculate quantities like P(2 ≤ X ≤ 6); • verify particular cases of the claims that hypergeometric random variables are approximately binomial and that binomial random variables are approximately Poisson. 1 (The probability mass function of a binom ...
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.