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## Stats 1350 Chapters 6 and 8

1 review
by: Nichelle Simmons

137

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6

# Stats 1350 Chapters 6 and 8 Stats 1350

Marketplace > Ohio State University > Math > Stats 1350 > Stats 1350 Chapters 6 and 8
Nichelle Simmons
OSU
GPA 3.9
STAT 1350
Corey Smith

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## About this Document

Chapter 6 and 8 notes for STATS 1350 OSU
COURSE
STAT 1350
PROF.
Corey Smith
TYPE
One Day of Notes
PAGES
6
WORDS
KARMA
25 ?

## 3

1 review
"You can bet I'll be grabbing Nichelle studyguide for finals. Couldn't have made it this week without your help!"
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## Popular in Math

This 6 page One Day of Notes was uploaded by Nichelle Simmons on Monday February 2, 2015. The One Day of Notes belongs to Stats 1350 at Ohio State University taught by Corey Smith in Winter2015. Since its upload, it has received 137 views. For similar materials see STAT 1350 in Math at Ohio State University.

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## Reviews for Stats 1350 Chapters 6 and 8

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-Axel Gottlieb

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Date Created: 02/02/15
Chapter 6 Experimental design in the real world 0 Completely randomized design of ce examplechapter 5 0 Randomized block design 0 Matched pairs design All 3 types of experiments listed above use randomization in different ways Completely randomized design CRD Of ce Example Definition All subjects are randomly assigned to a treatment group O Simplesteasiest to planperform 0 Can have many explanatory variables Block designs 0 Block a group of subjects that are known beforehand to be similar in a way that will affect the response age gender 0 Block Design the random assignment of subjects to treatment groups done separately within each block a form of control 0 Blocks can be large or small groups of subjects Examples income MajorGPA Example Randomized Block Design 0 Suppose we want to compare a drug to a placebo We believe the drug will help control blood pressure 0 We believe the drug might work differently in males and females so we decide to block based on gender to ensure that we control for gender and to ensure that males and females are each represented in the drug and placebo groups 0 Males and females within their respective blocks are randomly assigned to receive the drug or placebo Blocks are never rand0m Matched Pairs 0 De nition use two matched individuals and apply the treatment to both of them can be one person that received two treatments 0 If we match two different individuals we want to make sure we randomly assign each one to one of two treatments 0 If the same person is measured twice we want to ideally randomize the order of the treatments Example 1 Matched Pairs Design 0 From a 2002 article from the Journal of the American Veterinary Medicine Association 0 48 Labrador Retrievers from 7 litters were used in the study Dogs in each litter were paired at 6 weeks of age on the basis of seX and body weight and assigned at random to l of 2 feeding groups 0 Beginning at 8 weeks of age 1 dog in each pair was fed a roughly normal amount of food and the other dog in each pair was fed 75 of the amount of food that its pairmate had consumed the previous day 0 The same type of food was fed to both groups of dogs only the quantity provided was different 0 Here the pairs are dogs and the treatment is the different amount of food Each dog was matched with another dog based on some shared characteristics Example 2 Matched Pairs Design 0 We have 10 righthanded individuals and we want to determine if the right hand is generally stronger than the left in righthanded people 0 There is special apparatus that can be used to measure the strength of each hand Each person would be measured twice once with the right hand and once with the left hand 0 The order of measurements is randomized within each person some begin with a measurement of the right hand and others begin with a measurement of the left hand Are there age differences in incidental learning Eysenck 1974 Eysenck 197 4 sought to determine how age and the degree to which a person processes information affect memory A total of 50 younger between 18 and 30 years of age and 50 older between 55 and 65 years of age subjects were randomly assigned to one of ve learning groups The Counting group was asked to read through a list of 27 words and count the number of letters in each word The Rhyming group was asked to read each word and think of a word that rhymed with it The Adjective group was asked to give an adjective that could reasonably be used to modify each word in the list The Imagery group was instructed to form vivid images of each word The Intentional group was asked to memorize the words for later recall note that none of the other four groups was told they would later need to recall the words After the subjects had gone through the list three times they were asked to write down all the words they could remember What kind of experiment is this Block Design because we split by age Completely randomized design Randomized block design Block Age NOT RANDOM Matched pairs Observational study Swearing and Pain 0 Headline Stub your toe Say Sh You ll feel better 0 67 college students participated in an experiment 0 Each participant was asked to submerge hisher dominant hand in ice water and keep it there as long as possible while repeating his or her favorite curse word Each participant then repeated this task submerging the dominant hand in ice water while repeating a neutral word describing a table 0 The order of the tasks was randomized for each subject Matched Pairs Design Treatment swearing or not Response time in water Chapter 8 Learning Objectives 0 Determine whether a measurement is valid andor reliable Identify any bias and random error involved in a measurement Explain how calculating an average can improve reliability over individual measurements Questions about the variables in any statistical study 0 How exactly is the variable de ned 0 Is the variable a valid way to describe the property it claims to measure 0 How accurate are the measurements Valid measurement Measurement assigning a number to represent a property Valid Measurement if it s relevant or appropriate as a representation of a property Silly Examples PSI of football by it s circumference count of accidents on l7thCollege Rd Measured value true value parameter bias random error A measurement process has bias if the measurement systematically tends to overstate or understate the true property it measures EX keeping time With a stopwatch positive bias A measurement process has random error if repeated measurements on the same subject give different results EX counting bacteria temperature light affects bacteria Reliable measurement De nition a measurement Where the random error is small No measuring process is perfectly reliable Example The digital scale in your chemistry lab gives a reliable measurement of the sample you need for your experiment Reliability and validity Not the same thing Reliability has to do With how consistently the instrument measures Validity has to do With Whether that instrument was even the right choice to measure the variable you want to study It is hard to measure intelligence Let s do it the easy way measure circumference of the head just above the ears in inches and call the result intelligence Not only is the method easy it gives nearly the same number every time we repeat the measurement on the same person Measuring intelligence this way is reliable Not valid A stopwatch is used to measure the amount of time it takes a runner to complete one lap around a track The watch sometimes records a time that is accurate but other times the watch is too fast or two slow and does not give the runner s actual time Measuring time to complete a lap in this way is Not reliable Valid Improving Reliability The average of several repeated measurements of the same individual subject is more reliable than a single measurement Note related to the idea of reducing variability by taking a bigger sample More data better study strongmore valid conclusions

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