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For an incircle of a triangle the lengths of line segments from the vertex to the tangent point with the circle are congruent.
If AB=5.5 the AF=5.5
If CD=7 then CB=7
If FE=19.5 then DE=19.5
So AE=AF+FE=5.5+19.5=25
CE=CD+DE=7+19.5=26.5
The answer is D.
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Avenue Five Institute Introduction to Clinical Reminders Baccalaureate Paper
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Avenue Five Institute Introduction to Clinical Reminders Baccalaureate Paper
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#Lab 9#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#PART 1#Suppose you want to test whet ...
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The T-test: The influence of differences in the Variance #--------------------------------------------------------------------------------- #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 3-1: # A) What are the means and SDs for the variables x, y2a, and y2b?# B) Look histograms of the three variables. # C) Based on what you saw in #A), what are the differences between these 3 variables?# D) Run the appropriate t-test to test the following 2 null hypotheses:#H0: mean x = mean y2a#AND#H0: mean x = mean y2b # E) Do you reject or fail to reject the null hypotheses tested above in #D)? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) mean(lab9$x); sd(lab9$x)mean(lab9$y2a); sd(lab9$y2a) mean(lab9$y2b); sd(lab9$y2b)#B) hist(lab9$x)hist(lab9$y2a) hist(lab9$y2b)#C) #??#D) #??#E) #We then compare the p-value to our alpha level#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#???#What conclusion can you make based on the results from the#two t-tests? In other words, how does the difference #in variance between two samples affect our t-test result #assuming other things (e.g., sample size, #difference in means, skewness) are held constant.#Problems with controlling the probability of a Type I error occur:#It can yield inaccurate confidence intervals when sampling #from normal distributions with unequal sample sizes #and unequal variances.#---------------------------------------------------------------------------------# 4. The T-test: The influence of differences in Skewness#--------------------------------------------------------------------------------- #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 4-1: # A) What are the means and SDs for the variables x, y3a, and y3b?# B) Look histograms of the three variables. # C) Based on what you saw in #A), what are the differences between these 3 variables?# D) Run the appropriate t-test to test the following 2 null hypotheses:#H0: mean x = mean y3a#AND#H0: mean x = mean y3b # E) Do you reject or fail to reject the null hypotheses tested above in #D)? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) mean(lab9$x); sd(lab9$x)mean(lab9$y3a); sd(lab9$y3a) mean(lab9$y3b); sd(lab9$y3b)#B) hist(lab9$x)hist(lab9$y3a) #Left Skewhist(lab9$y3b) #Right Skew#C) #Same Means (kinda), same variance, just different skew #D) t.test(lab9$x, lab9$y3a, var.equal=TRUE)t.test(lab9$x, lab9$y3b, var.equal=TRUE)#E) #We then compare the p-value to our alpha level#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Fail to reject H0#Reject H0#Figure 9.2 in your book#When dealing with groups that differ in skewness, #again problems with controlling the probability of a Type I error occur,#and the combination of unequal variances and different amounts of skewness #makes matters worse.#---------------------------------------------------------------------------------# 5. The T-test with Trimmed Means: Yuen's T#--------------------------------------------------------------------------------- #As we saw above, skewness can have an influence on #our ability to detect statistical significance. #Using our univariate outlier detection method (MAD-Median),#we can see that both y3a and y3b have numerous outliers#NOTE: You must load the Rallfun-v33 source code to use the out() functionout(lab9$y3a) #5 outliersout(lab9$y3b) #5 outliers #Given that we have these outliers, we should consider using #a technique that compares the Trimmed Means#The Trimmed Means version of a t-test is called Yuen's Method#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Yuen's Trimmed t-test: yuen(x, y, tr=0.2) #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 5-1: # A) Re-test the hypotheses from Exercise 4-1 using Yuen's# method (with 20% trimming)# B) How do the results comapre to what you concluded previously? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) #HINT: find the p-value to make conclusionyuen(lab9$x, lab9$y3a, tr=0.2)yuen(lab9$x, lab9$y3b, tr=0.2)#B) #???#Given what we've just seen, how might skewness #affect the results we get?#---------------------------------------------------------------------------------# 6. 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The data #is recorded in a table in frosh.txt.frosh=read.table('frosh.txt', header=T)frosh#It is not valid to use a two-sample t-test to compare #the weight gain becuase the data are dependent (on the #same person)#Instead we will use the paired t-test.#The formula for calculating a paired t-test in R is again t.test(). #However, we change one parameter:#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Paired t-test: t.test(x, y, paired=TRUE) #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##Let's run a PAIRED t-test for our data from the freshmen:t.test(frosh$Weight_before,frosh$Weight_after, paired=TRUE) #What would have happend if we ignored the fact that #this was a PAIRED or REPEATED Measure Design?#Let's find out by running the normal independent samples T-Testt.test(frosh$Weight_before,frosh$Weight_after) #Remember: This is WRONG
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Avenue Five Institute Introduction to Clinical Reminders Baccalaureate Paper
Learning objectives1. Utilize technology for data collection, storage, analysis, and reporting of information (3)2. Analyz ...
Avenue Five Institute Introduction to Clinical Reminders Baccalaureate Paper
Learning objectives1. Utilize technology for data collection, storage, analysis, and reporting of information (3)2. Analyze statistical data for decision making (4)3. Manage information as a key strategic resource and mission tool (5)Student instructions1. If you have questions about this activity, please contact your instructor for assistance.2. You will review a de-identified chart to complete this activity. Your instructor hasprovided you with a link to the Introduction to Clinical Reminders (BS) activity. Click on2: Launch EHR to review the patient chart and begin this activity.3. Refer to the patient chart and any suggested resources to complete this activity.4. Document your answers directly on this activity document as you complete the activity.When you are finished, you will save this activity document to your device and uploadthis activity document with your answers to your Learning Management System (LMS).GlossaryClinical Reminders: The Clinical Reminder system is a built-in Clinical Decision Support (CDS)system that allows the care team to track and improve preventive healthcare and diseasetreatment for patients and ensure that timely clinical interventions are initiated. ClinicalReminders perform automatic chart audits and schedule events based on pre-programmedcriteria. In other words, Clinical Reminders do a chart review and tell clinicians what the patientneeds, or which patients need something.Reminder Inquiry: A reminder-specific summary of the data and criteria associated with anindividual reminder.Reminder Description/Rationale: The human-readable description of the clinical purpose,intent, and inclusion/exclusion criteria for the reminder.EHR Go Knowledge Activity: Introduction to Clinical Reminders (Baccalaureate) HBK1013.3 2Archetype Innovations LLC ©2019Reminder Cohort: Patients included in a group who need an intervention, e.g. “This patientneeds a flu shot” or patients to include on a report, “Show all patients who need flu shots.”Inclusion Criteria: The presence or absence of data that will prompt the reminder to become“due” for a patient.Exclusion Criteria: What will turn this reminder OFF? The findings necessary to resolve theReminder.Not Applicable Reminder: A reminder is considered to be “Not Applicable” for a patient if nofindings are present in the patient’s chart.Reminder Schedule: How often should this reminder or selected intervention be due for thecohort?Reminder Finding: A FINDING is a piece of information that the reminder uses for inclusion forin the EHR. A FINDING is binary: It’s there or it’s not. A finding must be coded data in order forthe EHR to recognize it. Examples of findings are lab results, vitals, completed orders, etc.Due Reminder: A reminder is DUE for a patient if they are in the cohort (applicable) and havenot had the need (frequency) for the intervention (finding) that the reminder is seeking, andthe intervention is currently due.Overdue Reminder: A reminder is OVERDUE for a patient if they are in the cohort (applicable)and have not had the need (frequency) for the intervention (finding) that the reminder isseeking, and the intervention is past due.Not Due Reminder: A reminder is NOT DUE for a patient if they are in the cohort (applicable)and have had the need (frequency) for the intervention (finding) that the reminder is seeking,and the intervention is not currently due.Clinical Decision Support (CDS): Clinical Decision Support (CDS) provides clinicians, staff,patients or other individuals with knowledge and person-specific information, intelligentlyfiltered or presented at appropriate times, to enhance health and health care. CDSencompasses a variety of tools to enhance decision-making in the clinical workflow. These toolsinclude computerized alerts and reminders to care providers and patients; clinical guidelines;condition-specific order sets; focused patient data reports and summaries; documentationtemplates; diagnostic support, and contextually relevant reference information, among otherEHR Go Knowledge Activity: Introduction to Clinical Reminders (Baccalaureate) HBK1013.3 3Archetype Innovations LLC ©2019tools. Clinical Reminders are a powerful type of CDS. (Centers for Medicare and MedicaidServices, 2014)The activityYou have been asked to review the Central Clinic’s use of Clinical Reminders as an initiative forimproved Clinical Decision Support (CDS) in conjunction with their EHR system. You have beenprovided with a recent report of the Influenza Vaccine Clinical Reminder cohort findings and ade-identified chart with some examples of Clinical Reminders, including a BMI Clinical ReminderAlert with details for your review. Please use this information to complete this activity.Launch the de-identified EHR and answer the following questions.Questions1. Which clinical reminder(s) are due?2. Which clinical reminder(s) are not due?3. What is the exclusion criteria for the Influenza Vaccine reminder?4. What is the inclusion criteria for the Body Mass Index >25 reminder?5. Whenwas the Body Mass Index >25 reminder last completed?6. How long before the Body Mass Index >25 reminder is due again?7. Where in the EHR can the inclusion criteria for the Body Mass Index >25 reminder befound and what is the exact value?8. What is the inclusion criteria for the Lipid Measurement reminder?9. How frequently is the Lipid Measurement due for this patient and why?10. How long ago was the Lipid Measurement reminder last completed?EHR Go Knowledge Activity: Introduction to Clinical Reminders (Baccalaureate) HBK1013.3 4Archetype Innovations LLC ©201911. How long before the Lipid Measurement reminder is due again?12. Where in the EHR can the inclusion criteria for the Lipid Measurement reminder befound?Refer to the BMI Reminder Inquiry to answer the following questions.13. What is the rationale listed for the BMI > 25 Clinical Reminder?14. What happens to this reminder if a patient’s BMI drops to 23?15. What happens to this reminder if a patient’s BMI of 29 was last documented two yearsago?16. In youropinion, what is the purpose of the BMI >25 clinical reminder?Refer to the EHR Go Influenza Clinical Reminder spreadsheet report to answer the followingquestions.17. Take a moment to examine the report data and the pivot table summary and describethe population and subject of this report.18. Review this report of influenza clinical reminders. What information about the data canyou gather from the pivot table?19. In your opinion, what is the most probable explanation of the results of the InfluenzaClinical Reminder report?20. Do you have any specific recommendations based on this report?EHR Go Knowledge Activity: Introduction to Clinical Reminders (Baccalaureate) HBK1013.3 5Archetype Innovations LLC ©2019Submit your workDocument your answers directly on this activity document as you complete the activity. Whenyou are finished, save this activity document to your device and upload this activity documentwith your answers to your Learning Management System (LMS).If you have any questions aboutsubmitting your work to your LMS, please contact your instructor.Referencesal., W. A. (2010). Best Practices in CDS: The Case of Preventative Care Reminders. Retrievedfrom Applied Clinical Informatics: http://dx.doi.org/10.4338/ACI-2010-05-RA-0031Centers for Medicare and Medicaid Services. (2014, July). Clinical Descision Support: More thanjust alerts. Retrieved from CDS Tipsheet:https://www.healthit.gov/sites/default/files/clini...Health IT.Gov. (2016, March 28). Clinical Decision Support (CDS).Kernan, R. (2012, August 22). Clinical Document Architecture (CDA) - HealthIT.gov. Retrievedfrom HealthIT.gov: https://www.healthit.gov/sites/default/files/cda_c-cda_theirrole_in_mu.pdfPractice Fusion. (2017). What is structured data or a structured result? United States.
PSY 7864 CU Week 4 Correlation in Data Analysis & Application Essay
CORRELATION APPLICATION AND INTERPRETATIONundefinedWeek 4 assignment InstructionsundefinedComplete the following for this ...
PSY 7864 CU Week 4 Correlation in Data Analysis & Application Essay
CORRELATION APPLICATION AND INTERPRETATIONundefinedWeek 4 assignment InstructionsundefinedComplete the following for this assignment:undefinedFor this assignment, you will use the Data Analysis and Application template (DAA Template [DOC]).For help with SPSS, refer to IBM SPSS Step-by-Step Guide: Correlations [DOC].For help copying SPSS output into your DAA review the Copy/Export Output Instructions [HTML].For information on the data set, you may find the Data Set Instructions [DOC] helpful.Refer to the 7864 Course Study Guide for information on analyses and interpretation.undefinedThe grades.sav file is a sample SPSS data set. The data represent a teacher's recording of student demographics and performance on quizzes and a final exam across three sections of the course. Each section consists of 35 students (N = 105). There are 21 variables in grades.sav. undefinedThis week’s assignment is on correlations. You will analyze the following variables in the grades.sav data set:undefined SPSS Variable Definition Quiz1 Quiz 1: number of correct answers GPA Previous grade point average Total Total number of points earned in class Final Final exam: number of correct answers undefinedStep 1: Write Section 1 of the DAA: The Data Analysis PlanundefinedName the four variables used in this analysis and whether they are categorical or continuous.State a research question, null hypothesis, and alternate hypothesis for one X-Y pair. For example, you could articulate a research question, null hypothesis, and alternate hypothesis for quiz1 (X) and final (Y).undefinedStep 2: Write Section 2 of the DAA: Testing AssumptionsundefinedTest for one of the assumptions of correlation – normality.undefinedCreate a descriptive statistics table in SPSS to assess normality. This table should include the four variables named above.Paste the table in the DAA.Interpret the skewness and kurtosis values and how you determined whether the assumption of normality was met or violated.undefinedStep 3: Write Section 3 of the DAA: Results and InterpretationundefinedPaste the intercorrelation matrix (SPSS Correlation table) for the four variables into the document.First, report the lowest magnitude correlation in the intercorrelation matrix, including degrees of freedom, correlation coefficient, and p value.Specify whether or not to reject the null hypothesis for this correlation.Second, report the highest magnitude correlation in the intercorrelation matrix, including degrees of freedom, correlation coefficient, and p value.Specify whether or not to reject the null hypothesis for this correlation.undefinedStep 4: Write Section 4 of the DAA: Statistical ConclusionsundefinedProvide a brief summary of your analysis and the conclusions drawn about correlations.Analyze the limitations of the statistical test and/or possible alternative explanations for your results.undefinedStep 5: Write Section 5 of the DAA: ApplicationundefinedAnalyze how you might use correlations in your field of study.undefinedName an independent variable and dependent variable that would work for such an analysis and why studying it may be important to the field or practice.undefinedSubmit your DAA template as an attached Word document in the assignment area.undefinedResources:undefined7864 Course Study Guide:undefinedhttps://courseroomc.capella.edu/bbcswebdav/institution/MULTI/MULTI7864/210100_10x/Course_Files/cf_7864_course_study_guide.pdfundefinedhttps://ebookcentral-proquest-com.library.capella.edu/lib/capella/detail.action?docID=5569480undefinedField, A. (2018). Discovering statistics using IBM SPSS: North American edition (5th ed.). Sage.undefinedReview Chapter 8's information on assumptions and effect sizes to help you with your assignment.
Lab Homework Using R
#Lab 9#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#PART 1#Suppose you want to test whet ...
Lab Homework Using R
#Lab 9#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#PART 1#Suppose you want to test whether or not boys like the T.V. show Rick and Morty better than girls. You ask 10 boys and 10 girls to rate the T.V. show on a scale from 1 to 7, where 7 is highly enjoyable and 1 is not enjoyable.#For the boys, you observe the values:# 5 7 6 3 1 1 2 4 5 6#For the girls, you observe the values:# 1 3 7 7 6 4 3 5 7 4#1.1) Are the groups independent?#1.2) Examine the means and SDs for each of the groups#1.3) Look at a histogram for both groups#1.4) Use the appropriate t-test to test the null hypothesis that mean score for boys=girls #1.5) Do you reject or fail to reject the null?#PART 2#Suppose you want to test the effectiveness of a new drug, Cholmed, that helps treat high cholesterol in adults. In your study, you have 16 people who have been diagnosed with high cholesterol. To 8 of them, you give regular doses of Cholmed over one month. To the other 8 participants, you give regular doses of a placebo over one month. Then, you calculate the difference in the cholesterol levels for each participant.#For the Cholmed group, you observe the values (in mg/dl):# -33 -15 1 -75 -23 -3 -60 -57#For the placebo group, you observe the values:# -17 4 15 -8 -30 -7 -2 4#2.1) Are the groups independent? #2.2) Examine the means and SDs for each of the groups #2.3) Look at a histogram for both groups #2.4) Use the appropriate t-test to test the null hypothesis that mean score for cholmed=placebo #2.5) Do you reject or fail to reject the null?#PART 3 #You are curious about whether or not eating too much of a good thing will decrease a person's preference for that type of food. You ask 12 people how much they like pizza on a scale from 1 to 7, where 7 is highly enjoyable and 1 is not enjoyable. Then, you make them eat a pizza for dinner every day for two weeks. After that, you ask them how much they like pizza.#Before the study, you observe the values:# 7 7 3 1 6 5 4 3 3 4 6 7#After the study, you observe the values:# 2 1 4 4 1 1 2 1 6 4 5 4 #3.1) Are the groups independent? Examine the means and SDs for each of the groups#3.2) Use the appropriate t-test to test the null hypothesis that mean score for before=after. Do you reject or fail to reject the null?Cannot use external packagesPlease see lecture notes below provided by professor to do lab homework above, and to follow their packages and formulas please#Lab 9-Contents (Lecture Notes)#1. Comparing Independent Groups#2. The T-test: The influence of differences in the Means#3. The T-test: The influence of differences in the Variance #4. The T-test: The influence of differences in Skewness#5. The T-test with Trimmed Means: Yuen's T#6. Comparing Dependent Groups: The Paired T-test#Goal: In this lab we will look at comparing #independent and dependent groups in R#---------------------------------------------------------------------------------# 1. Comparing Independent Groups#--------------------------------------------------------------------------------- #Previously we learned the function t.test(), #which we used to conduct one-sample t-tests.#We can extend our knowledge of this function #for using it to compare 2 independent groups.#All we need to do is to add another variable to the function#Two ways to perform an Indepedent Sample t-test in R:# t.test(y~g)#where y is a vector of scores of the data and#g is a grouping variable# t.test(x, y)#where x and y represent scores from group 1 and #group 2, respectively#Which to use depends on how your DATA are STRUCTURED.#In Chapter 9, you learned two types of two-sample t tests: #1) Student's two-sample t-test (assumes variance is equal) #2) Welch's test (assumes variance is NOT equal)#The function t.test() can run either; #you change the parameter "var.equal"#to change which test you use#(remember: Student's t assumes equal variances).#To run two-sample t-test:#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Student's t-test: t.test(x, y, var.equal=TRUE) OR # t.test(y~g, var.equal=TRUE)# Welch's test: t.test(x,y, var.equal=FALSE) OR # t.test(y~g, var.equal=FALSE)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##We're going to create some data we can practice on using#the rnorm() function we learned before.#Let's assume that I measured 20 young people and #25 old people on some measure #(could be anything...perhaps IQ)#I'll simulate what their data might look like. young=rnorm(n=20, mean=0, sd=1)old=rnorm(n=25, mean=0.5, sd=1)#Our interest here is find out if the mean for this #variable is different between young and old people. mean(young); mean(old)#Because our data is structured such that each group #has it's own variable, we'll use the t.test function #that allows for that.#Similarly, we need to decide if we're going to use the#student's t-test or the welch's t-test.#NOTE: we simulated the data so that the variance would be #equal, so we'll presume that the student t-test is fine.#Remember we are testing:#H0: mu1 = mu2#HA: mu1 != mu2#We also have degrees_of_freedom=N1+N2-2t.test(old, young, var.equal=TRUE)#We then compare the p-value to our alpha level#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Fail to reject!#OK...now, we're going to rearrange our data so that #it looks more like it will in real life when you collect it:yourdata=as.data.frame(matrix(, ncol=2, nrow=45))colnames(yourdata) = c("outcome", "group")yourdata[1:20, "outcome"]=youngyourdata[1:20, "group"]=1yourdata[21:45, "outcome"]=oldyourdata[21:45, "group"]=2yourdata#We can then run the more usual specification which is:t.test(yourdata$outcome ~ yourdata$group, var.equal=TRUE)#For the rest of the lab, we will use the t.test() #specification where each group has it's own variable.#??????????????????????????????????????????????????????????????##Thought Question 1: Given all that we've just discussed, what does#it mean to reject the null hypothesis using our t.test? #??????????????????????????????????????????????????????????????##---------------------------------------------------------------------------------# 2. The T-test: The influence of differences in the Means#--------------------------------------------------------------------------------- #Let's first load the table object lab9.txt lab9=read.table ('lab9.txt', header=T) #Choose lab9.txthead(lab9)#Check if the table is properly stored#Your table should look like the one below:# x y1a y1b y2a y2b y3a y3b#1 -3.16919102 -1.16919102 -0.669191 -6.575259 0.6328316 3.7191954 -0.2191954#2 -6.35526894 -4.35526894 -3.855269 -16.133493 -0.4291944 3.1886523 0.3113477#3 -1.91975197 0.08024803 0.580248 -2.826942 1.0493113 3.7193226 -0.2193226#4 2.58377901 4.58377901 5.083779 10.683651 2.5504883 0.4866459 3.0133541#5 -0.88984272 1.11015728 1.610157 0.262786 1.3926144 -9.3365268 12.8365268#6 0.02129019 2.02129019 2.521290 2.996185 1.6963254 2.3916263 1.1083737#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 2-1: # A) What are the means and SDs for the variables x, y1a, and y1b?# B) Look histograms of the three variables. # C) Based on what you saw in #A), # what are the differences between these 3 variables?# D) Run the appropriate t-test to test the following 2 null hypotheses:#H0: mean x = mean y1a#AND#H0: mean x = mean y1b # E) Do you reject or fail to reject the null hypotheses tested above in #D)? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) mean(lab9$x); sd(lab9$x)mean(lab9$y1a); sd(lab9$y1a)mean(lab9$y1b); sd(lab9$y1b)#B) hist(lab9$x)hist(lab9$y1a)hist(lab9$y1b)#C) #D) t.test(lab9$x, lab9$y1a, var.equal=TRUE)t.test(lab9$x, lab9$y1b, var.equal=TRUE)#E) #We then compare the p-value to our alpha level#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#H0: mean x = mean y1a#p-value=... therefore ...#H0: mean x = mean y1b #p-value=... therefore ...#What conclusion can you make based on the results from #the two t-tests? In other words, how does the difference #in Means between two samples affect our t-test result #assuming other things (e.g., sample size, difference in variance, skewness) are held constant.#See Figure 9.3 in your book#Power of any method based on means is highly sensitive to#small changes in the tails of the distributions and that#situations where outliers tend to occur have the potential #of masking an important difference among the bulk of the #participants.#---------------------------------------------------------------------------------# 3. The T-test: The influence of differences in the Variance #--------------------------------------------------------------------------------- #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 3-1: # A) What are the means and SDs for the variables x, y2a, and y2b?# B) Look histograms of the three variables. # C) Based on what you saw in #A), what are the differences between these 3 variables?# D) Run the appropriate t-test to test the following 2 null hypotheses:#H0: mean x = mean y2a#AND#H0: mean x = mean y2b # E) Do you reject or fail to reject the null hypotheses tested above in #D)? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) mean(lab9$x); sd(lab9$x)mean(lab9$y2a); sd(lab9$y2a) mean(lab9$y2b); sd(lab9$y2b)#B) hist(lab9$x)hist(lab9$y2a) hist(lab9$y2b)#C) #??#D) #??#E) #We then compare the p-value to our alpha level#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#???#What conclusion can you make based on the results from the#two t-tests? In other words, how does the difference #in variance between two samples affect our t-test result #assuming other things (e.g., sample size, #difference in means, skewness) are held constant.#Problems with controlling the probability of a Type I error occur:#It can yield inaccurate confidence intervals when sampling #from normal distributions with unequal sample sizes #and unequal variances.#---------------------------------------------------------------------------------# 4. The T-test: The influence of differences in Skewness#--------------------------------------------------------------------------------- #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 4-1: # A) What are the means and SDs for the variables x, y3a, and y3b?# B) Look histograms of the three variables. # C) Based on what you saw in #A), what are the differences between these 3 variables?# D) Run the appropriate t-test to test the following 2 null hypotheses:#H0: mean x = mean y3a#AND#H0: mean x = mean y3b # E) Do you reject or fail to reject the null hypotheses tested above in #D)? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) mean(lab9$x); sd(lab9$x)mean(lab9$y3a); sd(lab9$y3a) mean(lab9$y3b); sd(lab9$y3b)#B) hist(lab9$x)hist(lab9$y3a) #Left Skewhist(lab9$y3b) #Right Skew#C) #Same Means (kinda), same variance, just different skew #D) t.test(lab9$x, lab9$y3a, var.equal=TRUE)t.test(lab9$x, lab9$y3b, var.equal=TRUE)#E) #We then compare the p-value to our alpha level#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Fail to reject H0#Reject H0#Figure 9.2 in your book#When dealing with groups that differ in skewness, #again problems with controlling the probability of a Type I error occur,#and the combination of unequal variances and different amounts of skewness #makes matters worse.#---------------------------------------------------------------------------------# 5. The T-test with Trimmed Means: Yuen's T#--------------------------------------------------------------------------------- #As we saw above, skewness can have an influence on #our ability to detect statistical significance. #Using our univariate outlier detection method (MAD-Median),#we can see that both y3a and y3b have numerous outliers#NOTE: You must load the Rallfun-v33 source code to use the out() functionout(lab9$y3a) #5 outliersout(lab9$y3b) #5 outliers #Given that we have these outliers, we should consider using #a technique that compares the Trimmed Means#The Trimmed Means version of a t-test is called Yuen's Method#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Yuen's Trimmed t-test: yuen(x, y, tr=0.2) #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##Exercise 5-1: # A) Re-test the hypotheses from Exercise 4-1 using Yuen's# method (with 20% trimming)# B) How do the results comapre to what you concluded previously? #*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*#*##A) #HINT: find the p-value to make conclusionyuen(lab9$x, lab9$y3a, tr=0.2)yuen(lab9$x, lab9$y3b, tr=0.2)#B) #???#Given what we've just seen, how might skewness #affect the results we get?#---------------------------------------------------------------------------------# 6. Comparing Dependent Groups: The Paired T-test#--------------------------------------------------------------------------------- #We just learned the various ways to conduct a t-test#for independent groups (ie. different people in each group). #We used Student's t-test, Welch's, and Yuen's. #Now we are going to learn about what to do when #our groups are dependent (ie. same people in each group). #Often the dependent groups we see are based on time, #such that people are measured at baseline (group1) #and we want to see how much they've changed at followup (group2). #Here is an example:#We are interested in measuring the weight of college freshmen before and after the first year #(to test whether or not there is any truth to the "freshman 15"?). We tested 16 freshmen on the #first day of school, and tested them again after finals week of the spring semester. The data #is recorded in a table in frosh.txt.frosh=read.table('frosh.txt', header=T)frosh#It is not valid to use a two-sample t-test to compare #the weight gain becuase the data are dependent (on the #same person)#Instead we will use the paired t-test.#The formula for calculating a paired t-test in R is again t.test(). #However, we change one parameter:#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Paired t-test: t.test(x, y, paired=TRUE) #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##Let's run a PAIRED t-test for our data from the freshmen:t.test(frosh$Weight_before,frosh$Weight_after, paired=TRUE) #What would have happend if we ignored the fact that #this was a PAIRED or REPEATED Measure Design?#Let's find out by running the normal independent samples T-Testt.test(frosh$Weight_before,frosh$Weight_after) #Remember: This is WRONG
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Discuss two areas or specific items covered in this course that you can foresee going through radical change over the next ...
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MATH 211 WCU Systematic Sampling Method & Population of People Question
This is undergraduate level (basic statistics) of statistics signature assignment.Please see the attached file carefully b ...
MATH 211 WCU Systematic Sampling Method & Population of People Question
This is undergraduate level (basic statistics) of statistics signature assignment.Please see the attached file carefully before accept my request :) I need 100% accurate answer along with detailed problem solving step. Thank you!
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