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STAT 1350

by: Kristen Motil

STAT 1350 STAT 1350 – 1000

Kristen Motil
GPA 3.7
Elementary stats
Michelle Duda

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About this Document

A study guide for midterm one. There's definitions, examples, and some questions with answers at the end.
Elementary stats
Michelle Duda
Study Guide
50 ?




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This 8 page Study Guide was uploaded by Kristen Motil on Thursday October 1, 2015. The Study Guide belongs to STAT 1350 – 1000 at Ohio State University taught by Michelle Duda in Summer 2015. Since its upload, it has received 24 views. For similar materials see Elementary stats in Statistics at Ohio State University.

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Date Created: 10/01/15
STAT 1350 MIDTERM 1 STUDY GUIDE Prof Duda 1 2 7 What is a population 0 Group that you want to study What is an individual 0 One unit of the population What is a sample 0 Collection of the individuals that you actually collect information from What is a sampling frame 0 List of individuals in a population that you will draw the sample from What is a variable 0 Characteristic of individual you want to measure varies from individual to individual quantitative numeric vs categorical labels What is the parameter 0 Characteristic of the population that you wish to measure or average almost always an unknown value populations too big What is a statistic Actual numeric result from sample Match 17 terms with examples Class roster phonebook American voters college students 65 of OSU students work Hair color major age 1 voter 1 student quot0 of OSU students who work g 1000 randomly selected voters nweoce EXAMPLE Suppose you want to know the proportion of residents of Columbus who have been to the Nationwide Arena You contact 100 people by randomly dialing phone numbers from the Ameritech phone book 72 people say yes 0 00000 Population of interest Sampling frame Sample Variable of interest Parameter Statistic 1 What makes a sampling method biased a If it systematically favors a particular outcome b If it systematically overestimates an outcome c If it systematically underestimates an outcome d All of the above 2 Why do statisticians use randomization to pick samples a Gives every individual an equal opportunity for selection b To minimize the possibility of bias c To overestimate the outcome d A and B are both correct What is an advantage of taking a census a Extremely time consuming b Extreme accuracy c Expensive d Refusals What is a disadvantage of taking a census a Extremely time consuming b Expensive c Refusals d All of the above Convenience sampling sample those who are easiest to sample 0 Tends to get similar subjects share a particular trait 0 Extremely biased researcher actively selects you Why would you take this type of sample Voluntary response sample general call for participation 0 Volunteer to participate What kind of feedback would the researcher typically receive from this type of sampling Stratified sampling break population into groups and sample randomly from each group 0 Age gender ethnicity rank in school ensures each group is represented Observational study most common is a survey no researcher intervention manipulation Experiment researcher intervention manipulation Sampling errors if I could sample everyone in the population these go away 0 Random sampling errors from sample to sample results will vary Chance variation from sample 0 Undercoverage systematically leave out a portion of the population Fault of the researcher Nonsampling errors even if I took a census these issues could still occur 0 Response bias incorrect response Can be accidental can be on purpose Fault of the subject 0 Nonresponse chosen for survey subject does not participate refusal can t find Offer incentives to overcome Match examples with type of error a The statistic for the first survey is 50 the statistic for the second survey is 75 b An individual is asked to take a survey but refuses because they don t have time c An individual is asked a question but lies because they are embarrassed 7 What are the 2 types of errors that can occur when we use a statistic to estimate a parameter a Bias and sample b Sample and statistic c Bias and variability d Sample and variability 8 What does a good sampling method consist of a Large bias large variability b Large bias small variability c Small bias small variability d Small bias large variability 9 What does a confidence statement about a population parameter consist of a Confidence level margin of error b Confidence level bias c Margin of error variability d Confidence level variability Confidence level researcher decides only way to be 100 is to take a census 0 Usually 80 90 95 most common or 99 o Margin of error measure of accuracy of variation from sample to sample 0 Random sampling error 0 EX 3 o 2 o Shortcut for calculating margin of error 1 o n sam le snze n P o For 95 confidence level 0 Confidence statement always draws conclusions about the population 0 When writing a confidence statement include confidence level population and the statistic the margin of error 70 statistic 3 MOE 67 to 73 10What is a response variable 0 The dependent variable variable that is being influenced measuring at end 11What is an explanatory variable 0 The independent variable doing the influencing introduced at beginning 12What is a treatment 0 Specific combination of the explanatory variables that you will apply to subjects EXAMPLE You want to determine how temperature 400 F 425 F and 450 F and duration 45 or 60 minutes affect the texture of the cake 0 Response variable texture of the cake Explanatory variable temperature and duration Treatments 50 minutes at 325 350 and 375 60 minutes at 325 350 and 375 13 What is an observational study 0 Any study where researcher simply records or observes behavior 0 Limitation no cause and effect 14What is an experiment 0 Any study where researcher actually introduces a treatment 0 Major advantage cause and effect 15What is a lurking variable 0 A variable that has an important effect on the relationship among the variables in a study but is not included among the variables being studied 16Which type of study do lurking variables exist a Experiment b Observational c Both 17 What does confounding mean 0 When two variables explanatory or lurking impact on the response cannot be distinguished 18 What are the two types of matched pairs 0 2 measurements on same individual randomize order 0 2 individuals and match as closely as possible gender age ethnicity etc randomly assign one treatment to each 19What constitutes a welldesigned experiment a Randomization b Control group c Replication repetition d All of the above 20What is the placebo effect 0 When subjects respond favorably to treatment due to the expectation of a cure can t distinguish which effect treatment or placebo influenced the patient responses 21 What is a double blinding experiment 0 When neither the subjects nor those who work with them to collect the data know which treatment a subject is receiving 22What is a completely randomized experiment 0 When all of the subjects are randomly allocated among all of the treatments 23What is a block design experiment 0 If knowing beforehand that either gender ethnicity or age will react differently to a treatment the subjects are divided into groups accordingly known as blocks assignment to treatment is random but assignment to block is not 24What is a measurement 0 Involves assigning a number to represent a property or characteristic of a person or thing resulting in a numerical variable 25 Errors in measurement can arise as a result of a Bias validity b Bias variation c Variation reliability d Reliability validity 26 Measured value True value bias random error reliability 27 What does a set of data consist of a Information about individuals reliability b Reliability validity c Information about individuals organized into variables d Bias validity 28What does the distribution of a variable tell us 0 List of all possible values of variable 0 How frequently variable takes on those values 29What is a line graph 0 Graphing quantitative data y over time x 30What are two types of graphs used with categorical data a Pie chart and histogram b Pie chart and bar graph c Bar graph and histogram d Bar graph histogram and pie chart 31 What chart can be used for quantitative data a Bar graph b Stem plot c Box plot d Histogram e All of the above f B C and D 32 Describing shape 0 Number of modes peaks of distribution 0 Unimodal bimodal 0 Symmetry area to the left and right of the midpoint of distribution are mirror images of each other Skewness distribution has a long tail 0 Right skewed 0 Left skewed 33 Describing center 0 Where the majority of the data lies midpoint Z L L ofvalue Z 0 Median middle order matters 0 Mode mass fragment 34 Describing spread Range of distribution 0 Standard deviation 0 Quartilespercentiles 35What are outliers Values that fall outside the overall pattern 0 Meanaverage Tc 0 Shape distribution 0 Symmetric mean median o Rightskewed mean gt median JR ittean imitate o Leftskewed mean lt median 5 F l d il ltrate 36What is standard deviation s or SD 0 Typical distance from the mean o Variances2 X1 56 2X2 5C 2Xn 5c 2 n l Units are same as original measurements 0 Use mean to measure center use SD to measure spread 0 Works well with symmetric graphs and nofew outliers When all values are the same s0 s can never be negative 37What is the five number summary 0 Way to measure spread 0 Min max 0102 median 03 38What is the kth percentile K o is at or below your value ex 80th percentile 39What does the middle box in a box plot represent a 25 of data b 50 of data c 75 of data 40 How do you find the IQR 39 IQR Q3 Q1 41 Do outliers influence the IQR a Yes b No 42What is the outlier check Determines if there are any outlier values in data set 0 01 15IQR 02 15IQR Any numbers outside this range are considered outliers 43What does a distribution describe Shape Center Spread Outliers All of the above 9095 ANSWERS 7begadfc D D B A very easy and cheap very strong opinions mostly negative random sampling error nonresponse response bias gooogoo39movcngwm L L N l mwwomwowoogtoo


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