Problem #1 Neurological signs and symptoms of ciguatera poisoning as the start of treatment and 2.5 hours after treatment with mannitol.


 Rafe Lang
 1 years ago
 Views:
Transcription
1 Ho (null hypothesis) Ha (alternative hypothesis) Problem #1 Neurological signs and symptoms of ciguatera poisoning as the start of treatment and 2.5 hours after treatment with mannitol. Hypothesis: Ho: µ number of neurological signs and symptoms measured at baseline = µ number of neurological signs at 2.5 hours after treatment with mannitol. Ha: µ number of neurological signs and symptoms measured at baseline µ number of neurological signs at 2.5 hours after treatment with mannitol. Statistical Procedure, Tests and Assumptions: Test: Paired Sample Ttest Rationale: This test is being used as there are two observations being completed on the same test subject. Assumptions: 1. Normally distributed population. 2. Dependent observations 3. Random Samples  simple random sampling of the test subjects 4. Equality of Variances between the groups. Results: In reviewing the output data from SPSS the significance level is.021, which is less than.05 set, which suggests that the results are not happening by chance less than 5% of the time. We will reject the null hypothesis (which is µ number of neurological signs and symptoms measured at baseline = µ number of neurological signs at 2.5 hours after treatment with mannitol) and conclude that there is a difference in signs and symptoms after mannitol is administered; specifically with this data 2.5 hours.
2 You can see in the diagram above the mean of the signs and symptoms after 2.5 hours is less than the mean of the number of signs and symptoms at baseline. Inference: If the data was generalized to the rest of the population, it could be stated that the administration of the drug mannitol reduces the number of neurological signs and symptoms related to ciguatera poisoning. As stated in the results sections, Ho will be rejected as the data supports that there is a difference in signs and symptoms after mannitol is administered Problem #2 Wait times bases on time of day. Hypothesis: Ho: µ wait time in the morning = µ wait time in the afternoon = µ wait time in the evening. Ha: µ wait time of at least one group is to be different in the groups: morning; afternoon; or evening. Statistical Procedure, Tests and Assumptions: Test: Analysis of Variance Rationale: This test is being chosen as there is a categorical independent variable (time of day), as well as a continuous dependent variable (wait time). We are comparing multiple groups; more than two categorical variables are used in this test. We wish to measure the amount of variability within the groups and between the groups. Assumptions: 1. Normality of the data 2. Independence of observations of test subjects 3. Simple random samples are used 4. Equality of Variances groups have a constant variance within them. Results: In reviewing the output of the data after running the Analysis of Variance test, one will note that the sig. level is.000 for the group (time of day), which is less than.05. This tells us that this data is happening by chance less than 5% of the time, which dictates that we reject the null hypothesis (which states that each group is equal to the other) in favor of alternative hypothesis (which states that at least one group is different). Because the Test of BetweenSubjects Effects table does not depict the sig. level for each group, we will need to run Post Hoc tests, specifically the Tukey test, to ascertain which groups indicate a significant variance. After running the Tukey test, one will see that the morning group, compared to the other two groups has a sig. level lower than 5%; specifically.000 for this group. The sig. levels for the evening and afternoon groups are greater than.05. This indicates that the other groups are statistically equivalent to each other, excluding the morning group.
3
4 Inference: The Ho µ wait time in the morning = µ wait time in the afternoon = µ wait time in the evening will be rejected. The morning group has a higher wait time than the evening and afternoon groups. Problem #3 Mice exposure time to Nitrogen dioxide (N02) Hypothesis: Ho: µ percent of serum fluorescence on day 10 = µ percent of serum fluorescence on day 12= µ percent of serum fluorescence on day 14. Ha: at least one serum fluorescence level taken on day 10, day 12, or day 14 is to another. Statistical Procedure, Tests and Assumptions: Test: Randomized Block Design Rationale: This test is chosen as repeated measures/tests are being on the same test subjects and we need to remove the variability among subjects; also there are more than two groups involved in the data. Assumptions: 1. Normality  there is normal distribution of the data 2. Independence  each observation is independent of the other observation 3. Random Samples  simple random sampling of the test subjects 4. Equality of Variances groups have a constant variance within them Results: In reviewing the output results of the Randomized Block Design below, we note the significance level of.013 is less than the confidence level of.05 set. This indicates that there is a significant difference in the data related to number of days after exposure to Nitrogen dioxide and levels of serum fluorescence, thus the null hypothesis (which is µ percent of serum fluorescence on day 10 = µ percent of serum fluorescence on day 12= µ percent of serum fluorescence on day 14) is rejected. We can drill down to the specific effects by running Post Hoc Tukey Test. This will show each of the significance levels. In reviewing the values taken on each of the days (10, 12, and 14), it can be seen that the Sig. levels are all greater than.05 (alpha), which indicates that there is a great chance that the data is happening not by chance much of the time.
5 Inference: The Ho: µ percent of serum fluorescence on day 10 = µ percent of serum fluorescence on day 12= µ percent of serum fluorescence on day 14 will be rejected. Yes, the effect does depend on exposure time. In looking at the data, the longer the exposure time, the lower the average serum fluorescence rate.
6 Problem #4 Average time to Accelerate Is the average time to accelerate from 0 to 60 across all cars significantly different than 15? Hypothesis: Ho: µ time for the cards to accelerate from 0 to 60 seconds = 15 seconds. Ha: µ time for the cards to accelerate from 0 to 60 seconds 15 seconds. Statistical Procedure, Tests and Assumptions: Test: One Sample Ttest Rationale: This test is being run as there is only one sample taken, the variable is continuous, and we are looking to see if the sample observations were drawn from a population with a specific mean value (15). Assumptions: 1. Continuous response variables on a ratio or interval scale. 2. Normal distribution. 3. Simple random sampling. 4. Observations are independent of one another. Results: In reviewing the output, the sig level is noted to be.000, which is less than.05 confidence level set; which suggests that the results are not happening by chance less than 5% of the time. The null hypothesis (time for the cars to accelerate from 0 to 60 seconds = 15 seconds) will not be rejected. In looking at the OneSample Statistic table, you can see that the average wait time is seconds.
7 Inference: The average time to accelerate from 0 to 60 seconds across all cars is NOT significantly different than 15. As previously indicated, the Ho will not be rejected. Problem #5 Prediction of time to accelerate from 0 60 (MPG, ED, HP, # Cyl) Hypothesis: Ho: There is no relationship between time to accelerate and miles per gallon (MPG), engine displacement (ED), horsepower (HP), and number of cylinders (#cyl) Ha: There is a relationship between time to accelerate miles per gallon (MPG), engine displacement (ED), horsepower (HP), and number of cylinders (#cyl) Statistical Procedure, Tests and Assumptions: Test: Linear Regression Rationale: We have a continuous dependent variable (acceleration time) with a set of independent variables (miles per gallon, engine displacement, horsepower, number of cylinders) and we are attempting to predict acceleration time from 0 to 60  based on the continuous variables listed above. Assumptions: 1. Normality: sample form a normal population; assumes errors are normally distributed. 2. Independence: samples are independent of one another. 3. Simple random sampling. 4. Equality of variance: the variation around regression equation is constant along the continuum. 5. Linearity: predictors are linearly related. In looking at the Model Summary, we see that the r² is.541. This indicates how much variation within acceleration is based on the independent variables (miles per gallon, engine displacement, horsepower, and number of cylinders). This number is significantly different than 0 and we are explaining about 54% of the variation.
8 In viewing the Anova table, we note that here is a sig. level of.000. This is less than.05, which indicates that the data is predicting something that what we are attempting to prove is better than just random guessing. If the sig. level was greater than.05, there would be no evidence to suggest that we were predicting anything, and we would not continue our review of the regression coefficients as there would be nothing to interpret. In reviewing the Coefficients table, we can analyze the relationship between acceleration (dependent variable) and miles per gallon, engine displacement, horsepower, number of cylinders (independent predictors). When looking at this table, we need to decide if we need all the predictors. It is noted that # of cyl significance level is.608. If we remove this predictor, we will not lose any predictability in the model. Upon removal, we will buy back a degree of freedom and obtain a more parsimonious (powerful) model.
9 Upon reviewing the Scatterplot, (residual plot) we are looking to be sure there is no distinctive pattern on the plot. No pattern is noted. If there is random variation above and below zero this indicates that linearity is an appropriate model. With this model, there is evidence to suggest that the variation is constant. After I reran the model upon removing the nonsignificant variable  # of cylinders when analyzing the coefficients table, it is noted that all sig. levels are less than.05 which indicates that we now have predictors that are of significance.
10 In reexamining the fit of the model, upon reviewing the Scatterplot, (residual plot) we are looking to be sure there is no distinctive pattern on the plot. No pattern is noted. If there is random variation above and below zero this indicates that linearity is an appropriate model. With this model, there is evidence to suggest that the variation is constant. After removing the nonsignificant variable, our regression equation now reads as: Acceleration Time = (mpg) *.010 (ed) * (hp) The Ho, that there is no relationship between time to accelerate and miles per gallon (MPG), engine displacement (ED), horsepower (HP), and number of cylinders (#cyl) is rejected, as there is an indication that there is a relationship between these predictors as evidenced by the model.
11 Problem #6 Self Esteem Prediction Hypothesis: Ho: There is no relationship between selfesteem and locus of control, alienation, and social ability collectively. Ha: There is a relationship between selfesteem and locus of control, alienation and social ability. Statistical Procedure, Tests and Assumptions: Test: Linear Regression Rationale: We have a continuous dependent variable (selfesteem) with a set of independent variables (locus of control, alienation, and social ability) and we are attempting to predict  associate selfesteem prediction  based on the continuous variables listed above. Assumptions: 6. Normality: sample form a normal population; assumes errors are normally distributed. 7. Independence: samples are independent of one another. 8. Simple random sampling. 9. Equality of variance: the variation around regression equation is constant along the continuum. 10. Linearity: predictors are linearly related. Results: In reviewing the Correlations matrix, one can see that there is a inverse relationship between selfesteem and locus of control (.516). There is a positive relationship between selfesteem and alienation (.701). There is a positive relationship between selfesteem and social ability (.422).
12 In reviewing the Model Summary table and looking at the multiple predictors R =.729 and this refers to the multiple correlations coefficient which refers to the total associations collectively between all of the independent variables and the dependent variable. R² =.531 and this reflects the proportion of variance accounted for by the model how much variation exists within selfesteem, based on the independent variables. We are explaining about 51% of the variation of selfesteem by locus of control, alienation, and social ability. The Anova Table is used in testing the null hypothesis that no relationship exists between the dependent variable and the set of independent variables. The significance level of.000 indicates we are doing more than just randomly guessing that there is a relationship. If the sig. level was greater than.05, we would stop the testing and analysis at this point as there would be no need to interpret the regression coefficient as there would be nothing to interpret we are not predicting anything. However, because the sig level is less than.05, the testing and analysis will continue because there is some indication that there is a relationship to begin with. The Coefficients table is the table that provides us with the most valuable information as it formally documents the relationship between the dependent variable and the set of independent predictors. The sig. levels for the variables are reviewed: Locus of Control =.608 Alienation =.000 Social Ability =.183
13 What we are looking to determine here is if we need to keep all of the independent variables. We look at each variable s significance level to determine if the variable should be kept. We want to determine if it tests the null hypothesis that is whether that predictor is significantly different from zero is it adding anything to the model. Alienation and social ability have a sig. level of less than.05, which indicates that there is evidence to reject the null hypothesis that they are significantly different from zero they are providing significant predicting power to the model. Because locus of control has a sig. level of.608, if we remove this independent predictor from the model, we will not lose any predictability in the dependent variable. We are trying to end up with a more parsimonious model and by removing the nonaffective predictors, this will release a degree of freedom back into the error term and make for a stronger prediction; our model will be more powerful. Scatter Plot Interpretation: In reviewing the scatter plot (residual plot) we are looking to be sure there is no distinctive pattern on the plot. No pattern is noted. If there is random variation above and below zero this indicates that linearity is an appropriate model. With this model, there is evidence to suggest that the variation is constant.
14 At this point, we are going to remove the predictor of locus of control and rerun the data as that predictor is not providing any predictability. In looking at the r² we see that the number has changed from.531 to now read.529. We currently have a more significant model as the table of coefficients only includes significant predictors. They are explaining about 53% of variation related to selfesteem scores based on the predictors of alienation and social ability. Based on the assessments scores of alienation and social ability, if we know that we have these particular predictors, we can predict what the selfesteem score will be. If we know the assessment scores of alienation and social ability, we can predict the assessment score for selfesteem. Before we interpret the data, we need to be sure that we adhere to particular model assumptions, thus we need to review the histogram and scatter plots. Both indicate there are no problems with the data sets. None suggested.
15 Inference: Can one predict selfesteem based on alienation and social ability? The regression equation will give us a predicted selfesteem score based on the scores of alienation and social ability.
16 Selfesteem = (alienation) (social ability) This would reflect a change in selfesteem scores based on the scores of alienation and social ability. For instance if social ability score went up 10, the selfesteem score would go up (on average) 1.7 and would = The Ho that there is no relationship between selfesteem and locus of control, alienation, and social ability collectively is rejected as the model indicates that there is a relationship. Problem #7 Ductal carcinoma and family history of breast cancer. Hypothesis: Ho: There is no relationship between family history of breast cancer and ductal cancer. Ha: there is a relationship between family history of breast cancer and ductal cancer. Statistical Procedure, Tests and Assumptions: Test: Chisquare (AKA: Cross Tab Analysis) Rationale: This test is being run as there is a categorical dependent variable and a categorical independent variable. Assumptions: 1. No normality or equality of variance assumption 2. Simple random sampling. 3. Observations are independent of one another. Results: In looking at the Case Processing Summary table we can see that data was entered correctly by viewing the totals N=210. By viewing the Cross Tabulation table it appears that SPSS is interpreting the data the right way (observe this by viewing the counts in each category). This table also indicates that positive family history breast cancer and positive for Ductal Carcinoma is 17%, while negative family history breast cancer and positive for Ductal Carcinoma is roughly 6%.
17 The Person ChiSquare significance level of.029 is less then.05 which indicates that this is happening by chance less then 5% of the time. This is happening because there is a relationship between a positive family history of breast cancer and Ductal Carcinoma. In calculating the odds ratio having Ductal carcinoma based on whether you have a positive family history of breast cancer, you have 3.4 greater odds to have Ductal carcinoma if you have a positive family history of breast cancer.
18 Inference: The Ho that there is no relationship between family history of breast cancer and ductal cancer will be rejected as the data does suggest that there is a relationship between the two.
Regression Including the Interaction Between Quantitative Variables
Regression Including the Interaction Between Quantitative Variables The purpose of the study was to examine the interrelationships among social skills, the complexity of the social situation, and performance
More informationDr. Kelly Bradley Final Exam Summer {2 points} Name
{2 points} Name You MUST work alone no tutors; no help from classmates. Email me or see me with questions. You will receive a score of 0 if this rule is violated. This exam is being scored out of 00 points.
More informationLinear Regression in SAS
1 Suppose we wish to examine factors that predict patient s hemoglobin levels. Simulated data for six patients is used throughout this tutorial. data hgb_data; input id age race $ bmi hgb; cards; 21 25
More informationPitfalls in Linear Regression Analysis
Pitfalls in Linear Regression Analysis Due to the widespread availability of spreadsheet and statistical software for disposal, many of us do not really have a good understanding of how to use regression
More informationCHAPTER ONE CORRELATION
CHAPTER ONE CORRELATION 1.0 Introduction The first chapter focuses on the nature of statistical data of correlation. The aim of the series of exercises is to ensure the students are able to use SPSS to
More informationAdvanced ANOVA Procedures
Advanced ANOVA Procedures Session Lecture Outline:. An example. An example. Twoway ANOVA. An example. Twoway Repeated Measures ANOVA. MANOVA. ANalysis of CoVariance (): an ANOVA procedure whereby the
More informationInferential Statistics
Inferential Statistics and t  tests ScWk 242 Session 9 Slides Inferential Statistics Ø Inferential statistics are used to test hypotheses about the relationship between the independent and the dependent
More informationOverview of Lecture. Survey Methods & Design in Psychology. Correlational statistics vs tests of differences between groups
Survey Methods & Design in Psychology Lecture 10 ANOVA (2007) Lecturer: James Neill Overview of Lecture Testing mean differences ANOVA models Interactions Followup tests Effect sizes Parametric Tests
More informationStudy Guide #2: MULTIPLE REGRESSION in education
Study Guide #2: MULTIPLE REGRESSION in education What is Multiple Regression? When using Multiple Regression in education, researchers use the term independent variables to identify those variables that
More information10. LINEAR REGRESSION AND CORRELATION
1 10. LINEAR REGRESSION AND CORRELATION The contingency table describes an association between two nominal (categorical) variables (e.g., use of supplemental oxygen and mountaineer survival ). We have
More informationUnderstandable Statistics
Understandable Statistics correlated to the Advanced Placement Program Course Description for Statistics Prepared for Alabama CC2 6/2003 2003 Understandable Statistics 2003 correlated to the Advanced Placement
More informationData and Statistics 101: Key Concepts in the Collection, Analysis, and Application of Child Welfare Data
TECHNICAL REPORT Data and Statistics 101: Key Concepts in the Collection, Analysis, and Application of Child Welfare Data CONTENTS Executive Summary...1 Introduction...2 Overview of Data Analysis Concepts...2
More informationCHILD HEALTH AND DEVELOPMENT STUDY
CHILD HEALTH AND DEVELOPMENT STUDY 9. Diagnostics In this section various diagnostic tools will be used to evaluate the adequacy of the regression model with the five independent variables developed in
More informationThe ttest: Answers the question: is the difference between the two conditions in my experiment "real" or due to chance?
The ttest: Answers the question: is the difference between the two conditions in my experiment "real" or due to chance? Two versions: (a) Dependentmeans ttest: ( Matchedpairs" or "onesample" ttest).
More information11/24/2017. Do not imply a causeandeffect relationship
Correlational research is used to describe the relationship between two or more naturally occurring variables. Is age related to political conservativism? Are highly extraverted people less afraid of rejection
More informationStatistical reports Regression, 2010
Statistical reports Regression, 2010 Niels Richard Hansen June 10, 2010 This document gives some guidelines on how to write a report on a statistical analysis. The document is organized into sections that
More informationCorrelation and Regression
Dublin Institute of Technology ARROW@DIT Books/Book Chapters School of Management 201210 Correlation and Regression Donal O'Brien Dublin Institute of Technology, donal.obrien@dit.ie Pamela Sharkey Scott
More informationChapter 11. Experimental Design: OneWay Independent Samples Design
111 Chapter 11. Experimental Design: OneWay Independent Samples Design Advantages and Limitations Comparing Two Groups Comparing t Test to ANOVA Independent Samples t Test Independent Samples ANOVA Comparing
More informationYSU Students. STATS 3743 Dr. HuangHwa Andy Chang Term Project 2 May 2002
YSU Students STATS 3743 Dr. HuangHwa Andy Chang Term Project May 00 Anthony Koulianos, Chemical Engineer Kyle Unger, Chemical Engineer Vasilia Vamvakis, Chemical Engineer I. Executive Summary It is common
More informationChoosing a Significance Test. Student Resource Sheet
Choosing a Significance Test Student Resource Sheet Choosing Your Test Choosing an appropriate type of significance test is a very important consideration in analyzing data. If an inappropriate test is
More informationTHE STATSWHISPERER. Introduction to this Issue. Doing Your Data Analysis INSIDE THIS ISSUE
Spring 20 11, Volume 1, Issue 1 THE STATSWHISPERER The StatsWhisperer Newsletter is published by staff at StatsWhisperer. Visit us at: www.statswhisperer.com Introduction to this Issue The current issue
More informationTwoWay Independent ANOVA
TwoWay Independent ANOVA Analysis of Variance (ANOVA) a common and robust statistical test that you can use to compare the mean scores collected from different conditions or groups in an experiment. There
More informationAnalysis of Variance: repeated measures
Analysis of Variance: repeated measures Tests for comparing three or more groups or conditions: (a) Nonparametric tests: Independent measures: KruskalWallis. Repeated measures: Friedman s. (b) Parametric
More information(a) 50% of the shows have a rating greater than: impossible to tell
q 1. Here is a histogram of the Distribution of grades on a quiz. How many students took the quiz? What percentage of students scored below a 60 on the quiz? (Assume lefthand endpoints are included in
More informationBusiness Research Methods. Introduction to Data Analysis
Business Research Methods Introduction to Data Analysis Data Analysis Process STAGES OF DATA ANALYSIS EDITING CODING DATA ENTRY ERROR CHECKING AND VERIFICATION DATA ANALYSIS Introduction Preparation of
More informationChapter 14: More Powerful Statistical Methods
Chapter 14: More Powerful Statistical Methods Most questions will be on correlation and regression analysis, but I would like you to know just basically what cluster analysis, factor analysis, and conjoint
More informationSmall Group Presentations
Admin Assignment 1 due next Tuesday at 3pm in the Psychology course centre. Matrix Quiz during the first hour of next lecture. Assignment 2 due 13 May at 10am. I will upload and distribute these at the
More informationStatistics as a Tool. A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations.
Statistics as a Tool A set of tools for collecting, organizing, presenting and analyzing numerical facts or observations. Descriptive Statistics Numerical facts or observations that are organized describe
More informationLecture Outline. Biost 517 Applied Biostatistics I. Purpose of Descriptive Statistics. Purpose of Descriptive Statistics
Biost 517 Applied Biostatistics I Scott S. Emerson, M.D., Ph.D. Professor of Biostatistics University of Washington Lecture 3: Overview of Descriptive Statistics October 3, 2005 Lecture Outline Purpose
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationChapter 1: Exploring Data
Chapter 1: Exploring Data Key Vocabulary:! individual! variable! frequency table! relative frequency table! distribution! pie chart! bar graph! twoway table! marginal distributions! conditional distributions!
More informationEPS 625 INTERMEDIATE STATISTICS TWOWAY ANOVA INCLASS EXAMPLE (FLEXIBILITY)
EPS 625 INTERMEDIATE STATISTICS TOAY ANOVA INCLASS EXAMPLE (FLEXIBILITY) A researcher conducts a study to evaluate the effects of the length of an exercise program on the flexibility of female and male
More informationAP Statistics. Semester One Review Part 1 Chapters 15
AP Statistics Semester One Review Part 1 Chapters 15 AP Statistics Topics Describing Data Producing Data Probability Statistical Inference Describing Data Ch 1: Describing Data: Graphically and Numerically
More informationOverview of NonParametric Statistics
Overview of NonParametric Statistics LISA Short Course Series Mark Seiss, Dept. of Statistics April 7, 2009 Presentation Outline 1. Homework 2. Review of Parametric Statistics 3. Overview NonParametric
More informationGeneral Example: Gas Mileage (Stat 5044 Schabenberger & J.P.Morgen)
General Example: Gas Mileage (Stat 5044 Schabenberger & J.P.Morgen) From Motor Trend magazine data were obtained for n=32 cars on the following variables: Y= Gas Mileage (miles per gallon, MPG) X1= Engine
More informationThe Pretest! Pretest! Pretest! Assignment (Example 2)
The Pretest! Pretest! Pretest! Assignment (Example 2) May 19, 2003 1 Statement of Purpose and Description of Pretest Procedure When one designs a Math 10 exam one hopes to measure whether a student s ability
More informationStatistical Techniques. Masoud Mansoury and Anas Abulfaraj
Statistical Techniques Masoud Mansoury and Anas Abulfaraj What is Statistics? https://www.youtube.com/watch?v=lmmzj7599pw The definition of Statistics The practice or science of collecting and analyzing
More informationDaniel Boduszek University of Huddersfield
Daniel Boduszek University of Huddersfield d.boduszek@hud.ac.uk Introduction to Multiple Regression (MR) Types of MR Assumptions of MR SPSS procedure of MR Example based on prison data Interpretation of
More informationCorrelational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots
Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship
More informationResearch Questions, Variables, and Hypotheses: Part 2. Review. Hypotheses RCS /7/04. What are research questions? What are variables?
Research Questions, Variables, and Hypotheses: Part 2 RCS 6740 6/7/04 1 Review What are research questions? What are variables? Definition Function Measurement Scale 2 Hypotheses OK, now that we know how
More informationCHAPTER VI RESEARCH METHODOLOGY
CHAPTER VI RESEARCH METHODOLOGY 6.1 Research Design Research is an organized, systematic, data based, critical, objective, scientific inquiry or investigation into a specific problem, undertaken with the
More informationLecture 20: Chi Square
Statistics 20_chi.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 20: Chi Square Introduction Up until now, we done statistical test using means, but the assumptions for means have eliminated
More informationEvidenceBased Medicine Journal Club. A Primer in Statistics, Study Design, and Epidemiology. August, 2013
EvidenceBased Medicine Journal Club A Primer in Statistics, Study Design, and Epidemiology August, 2013 Rationale for EBM Conscientious, explicit, and judicious use Beyond clinical experience and physiologic
More informationSTATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS
STATISTICS 8 CHAPTERS 1 TO 6, SAMPLE MULTIPLE CHOICE QUESTIONS Circle the best answer. This scenario applies to Questions 1 and 2: A study was done to compare the lung capacity of coal miners to the lung
More informationMeasuring the User Experience
Measuring the User Experience Collecting, Analyzing, and Presenting Usability Metrics Chapter 2 Background Tom Tullis and Bill Albert Morgan Kaufmann, 2008 ISBN 9780123735584 Introduction Purpose Provide
More informationGetting a DIF Breakdown with Lertap
Getting a DIF Breakdown with Lertap Larry Nelson Curtin University of Technology Document date: 8 October 2009 website: www.lertap.curtin.edu.au This document shows how Lertap 5 may be used to look for
More informationExample of Interpreting and Applying a Multiple Regression Model
Example of Interpreting and Applying a Multiple Regression We'll use the same data set as for the bivariate correlation example  the criterion is 1 st year graduate grade point average and the predictors
More informationIntroduction to SPSS. Katie Handwerger Why n How February 19, 2009
Introduction to SPSS Katie Handwerger Why n How February 19, 2009 Overview Setting up a data file Frequencies/Descriptives Onesample Ttest Pairedsamples Ttest Independentsamples Ttest Oneway ANOVA
More informationRisk Aversion in Games of Chance
Risk Aversion in Games of Chance Imagine the following scenario: Someone asks you to play a game and you are given $5,000 to begin. A ball is drawn from a bin containing 39 balls each numbered 139 and
More informationCHAPTER 3 RESEARCH METHODOLOGY
CHAPTER 3 RESEARCH METHODOLOGY 3.1 Introduction 3.1 Methodology 3.1.1 Research Design 3.1. Research Framework Design 3.1.3 Research Instrument 3.1.4 Validity of Questionnaire 3.1.5 Statistical Measurement
More informationIntroduction to Multilevel Models for Longitudinal and Repeated Measures Data
Introduction to Multilevel Models for Longitudinal and Repeated Measures Data Today s Class: Features of longitudinal data Features of longitudinal models What can MLM do for you? What to expect in this
More information3.2 Least Squares Regression
3.2 Least Squares Regression Linear (straight line) relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these
More informationStatistics 2. RCBD Review. Agriculture Innovation Program
Statistics 2. RCBD Review 2014. Prepared by Lauren Pincus With input from Mark Bell and Richard Plant Agriculture Innovation Program 1 Table of Contents Questions for review... 3 Answers... 3 Materials
More information1. Below is the output of a 2 (gender) x 3(music type) completely between subjects factorial ANOVA on stress ratings
SPSS 3 Practice Interpretation questions A researcher is interested in the effects of music on stress levels, and how stress levels might be related to anxiety and life satisfaction. 1. Below is the output
More informationResearch paper. Oneway Analysis of Variance (ANOVA) Research paper. SPSS output. Learning objectives. Alcohol and driving ability
Research paper Alcohol and driving ability Oneway Analysis of Variance (ANOVA) Thirtysix people took part in an experiment to discover the effects of alcohol on drinking ability. They were randomly assigned
More informationChapter 12: Analysis of covariance, ANCOVA
Chapter 12: Analysis of covariance, ANCOVA Smart Alex s Solutions Task 1 A few years back I was stalked. You d think they could have found someone a bit more interesting to stalk, but apparently times
More informationTutorial 3: MANOVA. Pekka Malo 30E00500 Quantitative Empirical Research Spring 2016
Tutorial 3: Pekka Malo 30E00500 Quantitative Empirical Research Spring 2016 Step 1: Research design Adequacy of sample size Choice of dependent variables Choice of independent variables (treatment effects)
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) 1) A) B) C) D)
Exam Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) 1) A) B) C) D) Decide whether or not the conditions and assumptions for inference with
More informationHypothesis Testing. Richard S. Balkin, Ph.D., LPCS, NCC
Hypothesis Testing Richard S. Balkin, Ph.D., LPCS, NCC Overview When we have questions about the effect of a treatment or intervention or wish to compare groups, we use hypothesis testing Parametric statistics
More informationRoller coasters are an old thrill that continues to grow in popularity. Engineers and
chapter 29 Multiple Regression Wisdom 29.1 Indicators 29.2 Diagnosing Regression Models: Looking at the Cases 29.3 Building Multiple Regression Models Where have we been? We ve looked ahead in each of
More informationStatistics Guide. Prepared by: Amanda J. Rockinson Szapkiw, Ed.D.
This guide contains a summary of the statistical terms and procedures. This guide can be used as a reference for course work and the dissertation process. However, it is recommended that you refer to statistical
More informationWorkshop: Basic Analysis of Survey Data Martin Mölder November 23, 2017
Contents Workshop: Basic Analysis of Survey Data Martin Mölder November 23, 2017 1 Introduction and general remarks 1 1.1 Further reference........................................... 2 1.2 Statistical
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationOn the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA
STRUCTURAL EQUATION MODELING, 13(2), 186 203 Copyright 2006, Lawrence Erlbaum Associates, Inc. On the Performance of Maximum Likelihood Versus Means and Variance Adjusted Weighted Least Squares Estimation
More informationUsing SPSS for Correlation
Using SPSS for Correlation This tutorial will show you how to use SPSS version 12.0 to perform bivariate correlations. You will use SPSS to calculate Pearson's r. This tutorial assumes that you have: Downloaded
More informationIntroduction to Quantitative Methods (SR8511) Project Report
Introduction to Quantitative Methods (SR8511) Project Report Exploring the variables related to and possibly affecting the consumption of alcohol by adults Student Registration number: 554561 Word counts
More informationTo open a CMA file > Download and Save file Start CMA Open file from within CMA
Example name Effect size Analysis type Level Tamiflu Symptom relief Mean difference (Hours to relief) Basic Basic Reference Cochrane Figure 4 Synopsis We have a series of studies that evaluated the effect
More informationUNIVERSITY OF THE FREE STATE DEPARTMENT OF COMPUTER SCIENCE AND INFORMATICS CSIS6813 MODULE TEST 2
UNIVERSITY OF THE FREE STATE DEPARTMENT OF COMPUTER SCIENCE AND INFORMATICS CSIS6813 MODULE TEST 2 DATE: 3 May 2017 MARKS: 75 ASSESSOR: Prof PJ Blignaut MODERATOR: Prof C de Villiers (UP) TIME: 2 hours
More informationStatistical Significance, Effect Size, and Practical Significance Eva Lawrence Guilford College October, 2017
Statistical Significance, Effect Size, and Practical Significance Eva Lawrence Guilford College October, 2017 Definitions Descriptive statistics: Statistical analyses used to describe characteristics of
More informationEvaluating you relationships
Evaluating you relationships What relationships are important to you? What are you doing today to care for them? Have you told those concerned how you feel? Most of us regularly inspect the health of our
More informationDesigning Psychology Experiments: Data Analysis and Presentation
Data Analysis and Presentation Review of Chapter 4: Designing Experiments Develop Hypothesis (or Hypotheses) from Theory Independent Variable(s) and Dependent Variable(s) Operational Definitions of each
More informationChapter 7: Correlation
Chapter 7: Correlation Smart Alex s Solutions Task 1 A student was interested in whether there was a positive relationship between the time spent doing an essay and the mark received. He got 45 of his
More informationCHAPTER III METHODOLOGY
24 CHAPTER III METHODOLOGY This chapter presents the methodology of the study. There are three main subtitles explained; research design, data collection, and data analysis. 3.1. Research Design The study
More informationDesigning Psychology Experiments: Data Analysis and Presentation
Data Analysis and Presentation Review of Chapter 4: Designing Experiments Develop Hypothesis (or Hypotheses) from Theory Independent Variable(s) and Dependent Variable(s) Operational Definitions of each
More informationSample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome
Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome Stephen Burgess July 10, 2013 Abstract Background: Sample size calculations are an
More informationBinary Diagnostic Tests Two Independent Samples
Chapter 537 Binary Diagnostic Tests Two Independent Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary
More informationMEASURES OF ASSOCIATION AND REGRESSION
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 816 MEASURES OF ASSOCIATION AND REGRESSION I. AGENDA: A. Measures of association B. Two variable regression C. Reading: 1. Start Agresti
More information3. For a $5 lunch with a 55 cent ($0.55) tip, what is the value of the residual?
STATISTICS 216, SPRING 2006 Name: EXAM 1; February 21, 2006; 100 points. Instructions: Closed book. Closed notes. Calculator allowed. Doublesided exam. NO CELL PHONES. Multiple Choice (3pts each). Circle
More informationPÄIVI KARHU THE THEORY OF MEASUREMENT
PÄIVI KARHU THE THEORY OF MEASUREMENT AGENDA 1. Quality of Measurement a) Validity Definition and Types of validity Assessment of validity Threats of Validity b) Reliability True Score Theory Definition
More informationBinary Diagnostic Tests Paired Samples
Chapter 536 Binary Diagnostic Tests Paired Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary measures
More informationANALYSIS OF VARIANCE (ANOVA): TESTING DIFFERENCES INVOLVING THREE OR MORE MEANS
ANALYSIS OF VARIANCE (ANOVA): TESTING DIFFERENCES INVOLVING THREE OR MORE MEANS REVIEW Testing hypothesis using the difference between two means: Onesample ttest Independentsamples ttest Dependent/Pairedsamples
More informationStatistics and Probability
Statistics and a single count or measurement variable. S.ID.1: Represent data with plots on the real number line (dot plots, histograms, and box plots). S.ID.2: Use statistics appropriate to the shape
More informationANSWERS TO EXERCISES AND REVIEW QUESTIONS
ANSWERS TO EXERCISES AND REVIEW QUESTIONS PART THREE: PRELIMINARY ANALYSES Before attempting these questions read through Chapters 6, 7, 8, 9 and 10 of the SPSS Survival Manual. Descriptive statistics
More information12/31/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Introduce moderated multiple regression Continuous predictor continuous predictor Continuous predictor categorical predictor Understand
More information2 Assumptions of simple linear regression
Simple Linear Regression: Reliability of predictions Richard Buxton. 2008. 1 Introduction We often use regression models to make predictions. In Figure?? (a), we ve fitted a model relating a household
More informationChapter Eight: Multivariate Analysis
Chapter Eight: Multivariate Analysis Up until now, we have covered univariate ( one variable ) analysis and bivariate ( two variables ) analysis. We can also measure the simultaneous effects of two or
More informationTwoWay Independent Samples ANOVA with SPSS
TwoWay Independent Samples ANOVA with SPSS Obtain the file ANOVA.SAV from my SPSS Data page. The data are those that appear in Table 173 of Howell s Fundamental statistics for the behavioral sciences
More informationTEACHING REGRESSION WITH SIMULATION. John H. Walker. Statistics Department California Polytechnic State University San Luis Obispo, CA 93407, U.S.A.
Proceedings of the 004 Winter Simulation Conference R G Ingalls, M D Rossetti, J S Smith, and B A Peters, eds TEACHING REGRESSION WITH SIMULATION John H Walker Statistics Department California Polytechnic
More informationCP Statistics Sem 1 Final Exam Review
Name: _ Period: ID: A CP Statistics Sem 1 Final Exam Review Multiple Choice Identify the choice that best completes the statement or answers the question. 1. A particularly common question in the study
More informationPoisson regression. DaeJin Lee Basque Center for Applied Mathematics.
DaeJin Lee dlee@bcamath.org Basque Center for Applied Mathematics http://idaejin.github.io/bcamcourses/ D.J. Lee (BCAM) Intro to GLM s with R GitHub: idaejin 1/40 Modeling count data Introduction Response
More informationFixedEffect Versus RandomEffects Models
PART 3 FixedEffect Versus RandomEffects Models Introduction to MetaAnalysis. Michael Borenstein, L. V. Hedges, J. P. T. Higgins and H. R. Rothstein 2009 John Wiley & Sons, Ltd. ISBN: 9780470057247
More informationChemistry, Biology and Environmental Science Examples STRONG Lesson Series
Chemistry, Biology and Environmental Science Examples STRONG Lesson Series 1 Examples from Biology, Chemistry and Environmental Science Applications 1. In a forest near Dahlonega, 1500 randomly selected
More informationChapter 9: Comparing two means
Chapter 9: Comparing two means Smart Alex s Solutions Task 1 Is arachnophobia (fear of spiders) specific to real spiders or will pictures of spiders evoke similar levels of anxiety? Twelve arachnophobes
More informationCHAMP: CHecklist for the Appraisal of Moderators and Predictors
CHAMP  Page 1 of 13 CHAMP: CHecklist for the Appraisal of Moderators and Predictors About the checklist In this document, a CHecklist for the Appraisal of Moderators and Predictors (CHAMP) is presented.
More informationUNEQUAL CELL SIZES DO MATTER
1 of 7 1/12/2010 11:26 AM UNEQUAL CELL SIZES DO MATTER David C. Howell Most textbooks dealing with factorial analysis of variance will tell you that unequal cell sizes alter the analysis in some way. I
More informationPROC CORRESP: Different Perspectives for Nominal Analysis. Richard W. Cole. Systems Analyst Computation Center. The University of Texas at Austin
PROC CORRESP: Different Perspectives for Nominal Analysis Richard W. Cole Systems Analyst Computation Center The University of Texas at Austin 35 ABSTRACT Correspondence Analysis as a fundamental approach
More informationTitle:Mixedstrain Housing for Female C57BL/6, DBA/2, and BALB/c Mice: Validating a Splitplot Design that promotes Refinement and Reduction
Author's response to reviews Title:Mixedstrain Housing for Female C57BL/6, DBA/2, and BALB/c Mice: Validating a Splitplot Design that promotes Refinement and Reduction Authors: Michael Walker Mr (mwalk04@uoguelph.ca)
More informationM15_BERE8380_12_SE_C15.6.qxd 2/21/11 8:21 PM Page Influence Analysis 1
M15_BERE8380_12_SE_C15.6.qxd 2/21/11 8:21 PM Page 1 15.6 Influence Analysis FIGURE 15.16 Minitab worksheet containing computed values for the Studentized deleted residuals, the hat matrix elements, and
More informationMultiple Choice Questions
ACTM State Statistics Work the multiple choice questions first, selecting the single best response from those provided and entering it on your scantron form. You may write on this test and keep the portion
More informationPsychological. Influences on Personal Probability. Chapter 17. Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc.
Psychological Chapter 17 Influences on Personal Probability Copyright 2005 Brooks/Cole, a division of Thomson Learning, Inc. 17.2 Equivalent Probabilities, Different Decisions Certainty Effect: people
More information