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by: Lydia Runte

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# INTROD STATISTICS EX ST 301

Lydia Runte
Clemson
GPA 3.89

James Espey

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COURSE
PROF.
James Espey
TYPE
Class Notes
PAGES
5
WORDS
KARMA
25 ?

## Popular in Statistics and Probability

This 5 page Class Notes was uploaded by Lydia Runte on Saturday September 26, 2015. The Class Notes belongs to EX ST 301 at Clemson University taught by James Espey in Fall. Since its upload, it has received 96 views. For similar materials see /class/214227/ex-st-301-clemson-university in Statistics and Probability at Clemson University.

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Date Created: 09/26/15
EX ST 301 Final Introduction Population a set of all measurements corresponding to each unit in the entire collection of units about which information is sought Sample subset of measurements selected from the population of interest Difference between population and population of units 0 Example For a class project you are asked to determine the average credit hours taken by Clemson students this semester Population credit hours taken by all Clemson students Population of units a Clemson students Unit a single entity whose characteristics are of interest Variable characteristic of an individual or object that may vary for different observations Statistic numerical characteristics calculated from a sample Parameter numerical characteristic of a population Symbols Descriptive Statistics Quantitative Data 0 Graphs Relative frequency histogram Stem and leaf display 390 Statistics Central tendency 4292012 61100 PM a Mean and median Variation a Sample Range deviation sample variance standard deviation Relative Standing n Percentile quartile o Qualitative Data 0 Graphs Bar chart 0 Statistics Total of successestotal of observations xn Probability and Random Variables o How to determine probabilities from sample spaces o Mutually exclusive two events A and B are mutually exclusive if when one event occurs the other cannot occur simultaneously o Independent Two events are independent if the occurrence or nonoccurrence of on of the events does not change the probability of the occurrence of the other event o Complement of an event A denoted by A is the event that A does not occur o Random Variable 0 Discrete random variable that can assume a countable number of distinct possible values Example number of heads in 3 tosses of a coin 0 Continuous a random variable that will assume an infinitely large number of values corresponding to the points on a line interval Example height of randomly selected Clemson student o Probability Distributions 0 Discrete a formula table or graph that specifies the probability associated with each possible value the discrete random variable can assume 0 Continuous given by an equation fx probability density function which shows the density of probability as it varies with the continuous random variable Normal Distribution and Sampling Distribution o Ztable 0 Find areas 0 Find an unknown Zvalue 0 Find unknown Xvalue o Central Limit Theorem 0 If random samples of n observations are drawn from a population with a finite mean and finite standard deviation then when n is large the same mean will be approximately normally distributed 0 The approximation becomes more accurate as n increases EX ST 301 Final 4292012 61100 PM Statistical inference for proportions means and correlation 0 Process of statistical inference 0 Population 9 sample 9 statistics 9 parameters 0 Null hypothesis statement that is assumed to be true unless sufficient evidence is gathered to reject the hypothesis 0 Alternative hypothesis statement that one wishes to support as being true i o ais the probability of making a Type I error 0 Also called level of significance 0 B is the probability of making a Type 11 error 0 How to make decisions and conclusions 0 Reject null hypothesis PLAR There is sufficient evidence for alternative hypothesis 0 Fail to reject null hypothesis P value gt0 There is insufficient evidence for alternative hypothesis Inference for proportions o How to distinguish between one and two samples 0 Confidence Interval CI 0 An interval that is likely to capture the value of the parameter being estimated 0 Consists of two values LCL and UCL o 1 1100 4292012 61100 PM

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