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## Study Guide for the Review Exam

by: Danielle Backman

40

0

6

# Study Guide for the Review Exam Stat 307

Marketplace > Colorado State University > Statistics > Stat 307 > Study Guide for the Review Exam
Danielle Backman
CSU
GPA 3.71
Introduction to Biostatistics
Brett Hunter

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-provides examples and definitions of terms
COURSE
Introduction to Biostatistics
PROF.
Brett Hunter
TYPE
Study Guide
PAGES
6
WORDS
CONCEPTS
Statistics, Biostatistics
KARMA
50 ?

## Popular in Statistics

This 6 page Study Guide was uploaded by Danielle Backman on Monday January 25, 2016. The Study Guide belongs to Stat 307 at Colorado State University taught by Brett Hunter in Spring 2016. Since its upload, it has received 40 views. For similar materials see Introduction to Biostatistics in Statistics at Colorado State University.

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Date Created: 01/25/16
STAT 307 Review Exam Study Guide Statisticsstudy of the collection analysis interpretation presentation and organization of data While applying statistics to scientific industrial or societal issue statistical population or statistical model process is studied Variabilityalso called dispersion and shows how stretched or squeezed a distribution is Measures of variability include variance standard deviation and interquartile range Datafacts and figures from which conclusions can be drawn Different from info and statistics Descriptive Statisticsquantitatively describes main features of a collection of info Different from inferential bc it aims to summarize a sample rather than use the data to learn about the population that the sample of data is thought to represent PopulationThe entire pool from which a statistical sample is drawn The information obtained from the sample allows statisticians to develop hypotheses about the larger population Researchers gather information from a sample because of the difficulty of studying the entire population Sampleset of data collected andor selected from a statistical population by a defined procedure Censusa study of every unit everyone or everything in a population It is known as a complete enumeration which means a complete count Inferential Statisticsmakes inferences about populations using data drawn from the population Instead of using the entire population to gather the data the statistician will collect a sample or samples from the millions of residents and make inferences about the entire population using the sample Variablea quantity that has a changing value Any characteristics number or quantity that can be measured or counted A variable may also be called a data item Age sex business income and expenses country of birth capital expenditure class grades eye color for ex Univariate DataWhen we conduct a study that looks at only one variable we say that we are working with univariate data Bivariate Datastudy that looks at two variables Quantitativedata expressing a certain quantity amount or range Usually there are measurement units associated with the data eg metres in the case of the height of a person Numericaldata that has numerical value Qualitativea categorical measurement expressed not in terms of numbers but rather by means of a natural language description In statistics it is often used interchangeably with quotcategoricalquot data Categoricala variable that can take on one of a limited and usually fixed number of possible values thus assigning each individual to a particular group or quotcategoryquot Continuousa variable that has an infinite number of possible values In other words any value is possible for the variable Examples include a person s weight or gas prices Discretea variable that can only take on a certain number of values FOR EXdiscrete may be values 1 2 whereas continuous may be anything in between 1 and 2 Frequencyor absolute frequency of an event is the number of times the event occurred in an experiment or study Relative Frequencya measure of the number of times that an event occurs To compute relative frequency one obtains a frequency count for the total population and a frequency count for a subgroup of the population The relative frequency for the subgroup is Relative frequency Subgroup count Total count The above equation expresses relative frequency as a proportion It is also often expressed as a percentage Thus a relative frequency of 050 is equivalent to a percentage of 50 It should add to 1 Example the team won 9 games of 12 played so the frequency is a Freuwemv Distribugml 1 mm 9 but the relative frequency In 912 or 311 MW Upper WWW Freq cum Emma 7500 I Mi I Lin21 HM I 39quot A 39 n Frequency Distributiona table that 222212 nal n m n JWLJ UWT TU JW JW lliU 391 I1 2 0033024 0131543 0144233 1213 1322 534 Mean mm displays the frequency of various outcomes 3 0491543 0233033023333 MB 3330 43 39 3 33 4 0233033 0330533 0333335 1333 5333 1333 imam In a sample Each entry In the table contains 5 0330533 0435121 0423353 1333 3305 2435 3 0435121 0533345 0522333 2003 3313 3104 the frequency or count of the occurrences of 1 05mm mm 0mm m 334 m3 values Within a partIcular group or Interval 3 033413 0353334 0311432 1333 13320 4440 3 0353334 0353213 0305353 1343 15033 5022 and m th39S Ways the table summanzes the 10 0353213 0343342 030043 1303 13333 5553 distribution of vaiues in the sampie 11 0343343 1042233 0335005 1452 13125 3042 12 1042233 1133331 1033523 1333 13504 3501 13 1133331 1231315 1134053 1235 20333 3313 14 1 231315 1325339 1233533 1033 21322 3234 15 1 32534 1 4201364 1 333102 933 22305 3602 16 1420364 1514333 1463626 333 23633 33 94 L 13 1514333 1609412 156215 350 3144 39v 1 13151531 Illa391 33 33313331 32113113332311325 131332 5 g t I L 393 f 3 iiiIquot r31 3 F331 i I 34 3 3 21 3 3911 11 33 3 4 323 13 T43 1150 A 33 633 31 33 39 4 33 3 3923 Relative Frequency Distributionthe frequency of W the class divided by the total number of frequencies m g g I i of the class and is generally expresses as a quotquot 39 A 3 i 3 3 percentage 3313 31 a 00 Chart I39quot 1 a Bar E Itiilrr thzu39u 33110151 1 H be I HLIJnIIIIJEl 01 PI39EJEI a F 33136 1936 35311 3333 3353 ELIEET Comparative Bar Chart w 4 5 if r i n u 2 Girl KW 1 n Ellili lli rl In Multiple Bar Chart 14mm 1mm lfilf fi 3m anon 7 icing anon Elmooft39sjquot 3 4 EJE39EEEE Import 33 Export L Dotplottype ofgraphic display used to compare frequency nu i I 5 counts within categories or groups E iii39iil am Wing3 Pie Chartdivided into slices to illustrate numerical proportions Histogramrepresents a frequency distribution by means of rectangles whose widths represent class intervals and whose areas are proportional to the corresponding frequencies the height of each is the average frequency density for the interval Cumulative Relative Frequency Distributiona ff135E deemJ e summary of frequency proportion J below a given eveThe relationship between cumulative frequency and relative cumulative frequency is cumulative relative frequencycumulative frequencysample size Unimodala probability distribution that has a single mode Bimodala probability distribution that has multiple modes Skewa measure of the asymmetry of the probability distribution of a realvalued random variable about its mean The skewness value can be positive or negative or even undefined Positive Skewmeangtmediangtmode Negative Skewmeanltmedianltmode Symmetricmeanmedianmode Mim ai j mzm 39 H 7 r LF E 39 Scatterplota graphic tool used to display the relationship I g between two quantitative variables A scatterplot consists of an X axis the horizontal axis a Y axis the vertical axis and a series of dots Each dot on the scatterplot represents one observation from a data set Wfi glffi Time Series Plota graph that you can use to evaluate patterns and behavior in data over time A time series plot displays observations on the yaxis against equally spaced time intervals on the xaxis a In ll I I la a Lil I it In Ill Ear 1H E f Hi l i r it ll i ill A a u l I I if a a t I 39uIItlg I HI 392 Boxplota graphical rendition of statistical data based E 39 k 39t on the minimum first quartile median third quartile and quot39 maximum The term quotbox plotquot comes from the fact that I a a 1 Eff an the graph looks like a rectangle with lines extending up tiltitti ruua rtrilr itquot III ltt quotJ rst quartile 39 minimum from the top and bottom Sample Meanan unbiased estimator for the population mean The notation is therefore sometimes used with the hat indicating that this quantity is an estimator x or 9symbols for sample mean Population meanthe true mean of the entire population of l 51 the data set while a sample mean is the mean of a small l 139s WE lli sample of the population W lit E William psymbol for population mean ME 395 Sample Medianthe number separating the higher half of a mm data sample a population or a probability distribution from M 5 Hi the lower half n 3 3 Ha Sample Proportionproportion means estimate and it is w E V a the point estimate of the population proportion is given by will 31 his we 39ra 339 L a 33922 H the sample Api or Ap Population proportion pi or pp represents the sample proportion and pi represents the population proportion Sum 2sum of all the values in a range of series Rangevalues in between min and max value Deviationhow far a value is from the mean Sum of Squares Sample Variancethe avg squared deviation a sample observation is from the mean sA2refers to the sample variance Sample Standard Deviation srefers to standard deviation of a sample Population variancethe avg squared deviations a population observation is from the mean 0A2refers to the variance of a population Population Standard Deviation orefers to the standard deviation of a population Lower Quartileseparates the lower 25 of data from the upper 75 Is the median of the values between the lowest value that is not an outlier and the median Q1another name for lower quartile Upper Quartileseparates the lower 75 of data from the upper 25 Is the median of the values in between the median and the upper most value that is not an outlier Q3another name for upper quartile Interquartile Rangethe range of the central 50 of data this is important for determining outliers IQRcalculated by subtracting QSQf Outliera value is an outlier if it is more than 15IQR away from the nearest quartile upper or lower FiveNumber Summarycommonly used to give a box plot and includes the minimum 01 median 03 and maximum values Comparative Box plotcompares 2 sets of data 2 box plots on the same axis zscorea way to compare values from 2 different data sets and tells us how many standard deviations a given observation is from it s mean Calculated by observationmean standard deviation 2 score Standardized Scorea set of scores that have equal means and standard deviations and allow for comparison Percentilemore specific than quartiles allows you to look at the 33rd for ex The r percentile separates the bottom r of the data from the upper 1r

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