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1. The following are body mass index (BMI) scores measured in 12.docx

1. The following are body mass index (BMI) scores measured in 12.docx

Page 1 of 1 PSY2061 Research Methods Lab © 2013 South Un.docx

Page 1 of 1 PSY2061 Research Methods Lab © 2013 South Un.docx

Page 1 of 1 PSY2061 Research Methods Lab © 2013 South Un.docx

Page 1 of 1 PSY2061 Research Methods Lab © 2013 South Un.docx

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1. The following are body mass index (BMI) scores measured in 12.docx

1. The following are body mass index (BMI) scores measured in 12 patients who are free of diabetes and participating in a study of risk factors for obesity. Body mass index is measured as the ratio of weight in kilograms to height in meters squared. Generate a 95% confidence interval estimate of the true BMI.
25
27
31
33
26
28
38
41
24
32
35
40
2. Consider the data in Problem 1. How many subjects would be needed to ensure that a 95% confidence interval estimate of BMI had a margin of error not exceeding 2 units?
3. The mean BMI in patients free of diabetes was reported as 28.2. The investigator conducting the study described in Problem 1 hypothesizes that the BMI in patients free of diabetes is higher. Based on the data in Problem 1 is there evidence that the BMI is significantly higher that 28.2? Use a 5% level of significance.
4. Peak expiratory flow (PEF) is a measure of a patient’s ability to expel air from the lungs. Patients with asthma or other respiratory conditions often have restricted PEF. The mean PEF for children free of asthma is 306. An investigator wants to test whether children with chronic bronchitis have restricted PEF. A sample of 40 children with chronic bronchitis are studied and their mean PEF is 279 with a standard deviation of 71. Is there statistical evidence of a lower mean PEF in children with chronic bronchitis? Run the appropriate test at =0.05.
5. Consider again the study in Problem 4, a different investigator conducts a second study to investigate whether there is a difference in mean PEF in children with chronic bronchitis as compared to those without. Data on PEF are collected and summarized below. Based on the data, is there statistical evidence of a lower mean PEF in children with chronic bronchitis as compared to those without? Run the appropriate test at =0.05.
Group
Number of Children
Mean PEF
Std Dev PEF
Chronic Bronchitis
25
281
68
No Chronic Bronchitis
25
319
74
6. Using the data presented in Problem 5,
a) Construct a 95% confidence interval for the mean PEF in children without chronic bronchitis.
b) How many children would be required to ensure that the margin of error in (a) does not exceed 10 units?
7. A clinical trial is run to investigate the effectiveness of an experimental drug in reducing preterm delivery to a drug considered standard care and to placebo. Pregnant women are enrolled and randomly assigned to receive either the experimental drug, the standard drug or placebo. Women are followed through delivery and classified as delivering preterm (< 37 weeks) or not. The data are shown below.
Preterm Delivery
Experimental Drug
Standard Drug
Placebo
Yes
17
23
35
No
83
77
65
Is there a statistically significant difference in the proportions of women delivering preterm among the three treatment groups? Run the test at a 5% level of significance.
8. Using the data in Problem 7, generate a 95% confidence interval for the difference in proportions of women ...

Page 1 of 1 PSY2061 Research Methods Lab © 2013 South Un.docx

Page 1 of 1
PSY2061 Research Methods Lab
© 2013 South University
Steroid Usage, Grip Strength, Aggression, and Happiness Study
Steroid Usage Grip Strength Aggression Happiness Investigator
3 12 7 5 1
11 49 19 7 1
23 54 30 3 1
15 19 21 6 1
19 64 24 7 2
4 20 5 10 2
19 57 19 5 2
13 21 18 11 2
24 78 36 14 3
6 15 32 11 3
14 17 4 9 3
29 87 26 5 4
2 12 8 5 4
17 74 22 6 4
4 35 6 7 4
Steroid Usage: The number entered reflects the number of weeks on steroids.
Grip Strength: The number registered on a hand meter. The larger the number, the stronger the
person; the scale cannot go higher than 100.
Aggression: The number obtained on the McGuthry Aggression Inventory. The higher the
number, the more aggression; the scale goes from 5 to 30.
Happiness: The number obtained on the Happiness Scale. The higher the number, the happier
the person is; the scale goes from 0 to 15.
Investigator: The number assigned to the person who assessed the participant.
Chapter 3, Section 2, Exercise 068ad
Bisphenol A in Your Soup Cans
Bisphenol A (BPA) is in the lining of most canned goods, and recent studies have shown a positive association between BPA exposure and behavior and health problems. How much does canned soup consumption increase urinary BPA concentration? That was the question addressed in a recent study1 in which consumption of canned soup over five days was associated with a more than 1000% increase in urinary BPA. In the study, 75 participants ate either canned soup or fresh soup for lunch for five days. On the fifth day, urinary BPA levels were measured. After a two-day break, the participants switched groups and repeated the process. The difference in BPA levels between the two treatments was measured for each participant. The study reports that a 95% confidence interval for the difference in means (canned minus fresh) is 19.6 to 25.5 μg/L.
1Carwile J., Ye X., Zhou X., Calafat A., and Michels K., "Canned Soup Consumption and Urinary Bisphenol A: A Randomized Crossover Trial," Journal of the American Medical Association, 2011; 306(20): 2218–2220.
(a) Is this a randomized comparative experiment or a matched pairs experiment?
Randomized comparative experiment
Matched pairs experiment
SHOW HINT
LINK TO TEXT
(d) If the study had included 500 participants instead of 75, would you expect the confidence interval to be wider or narrower?
Wider
Narrower
Click if you would like to Show Work for this question:
Open Show Work
SHOW HINT
LINK TO TEXT
ter 3, Section 2, Exercise 061acd
Have You Ever Been Arrested?
According to a recent study of 7335 young people in the US, 30% had been arrested1 for a crime other than a traffic violation by the age of 23. Crimes included such things as vandalism, underage drinking, drunken driving, shoplifting, and drug possession.
1From a study in USA Today, quoted in The Week, 2012; 11: 547–548..

Page 1 of 1 PSY2061 Research Methods Lab © 2013 South Un.docx

This document provides data from a study on steroid usage, grip strength, aggression, and happiness. It includes the following information:
1) A table with data from 14 participants, including the number of weeks on steroids, grip strength, aggression score, happiness score, and investigator number.
2) Descriptions of what each column in the data table represents, such as the scales for grip strength, aggression, and happiness.
3) The sample size is 14 participants.

PU 515 Applied Biostatistics Final Exam 1. The.docx

PU 515
Applied Biostatistics
Final Exam
1. The following are body mass index (BMI) scores measured in 12 patients who are free of diabetes
and participating in a study of risk factors for obesity. Body mass index is measured as the ratio of
weight in kilograms to height in meters squared. Generate a 95% confidence interval estimate of
the true BMI.
25 27 31 33 26 28 38 41 24 32 35 40
2. Consider the data in Problem #1. How many subjects would be needed to ensure that a 95%
confidence interval estimate of BMI had a margin of error not exceeding 2 units?
3. A clinical trial is run to investigate the effectiveness of an experimental drug in reducing preterm
delivery to a drug considered standard care and to placebo. Pregnant women are enrolled and
randomly assigned to receive either the experimental drug, the standard drug or placebo. Women
are followed through delivery and classified as delivering preterm (< 37 weeks) or not. The data
are shown below.
Preterm Delivery Experimental Drug Standard Drug Placebo
Yes 17 23 35
No 83 77 65
Is there a statistically significant difference in the proportions of women delivering preterm among
the three treatment groups? Run the test at a 5% level of significance.
4. Consider the data presented in problem #4. Previous studies have shown that approximately 32%
of women deliver prematurely without treatment. Is the proportion of women delivering
prematurely significantly higher in the placebo group? Run the test at a 5% level of significance.
PU 515
Applied Biostatistics
Final Exam
5. A study is run comparing HDL cholesterol levels between men who exercise regularly and those
who do not. The data are shown below.
Regular Exercise N Mean Std Dev
Yes 35 48.5 12.5
No 120 56.9 11.9
Generate a 95% confidence interval for the difference in mean HDL levels between men who
exercise regularly and those who do not.
6. A clinical trial is run to assess the effects of different forms of regular exercise on HDL levels in
persons between the ages of 18 and 29. Participants in the study are randomly assigned to one of
three exercise groups - Weight training, Aerobic exercise or Stretching/Yoga – and instructed to
follow the program for 8 weeks. Their HDL levels are measured after 8 weeks and are summarized
below.
Exercise Group N Mean Std Dev
Weight Training 20 49.7 10.2
Aerobic Exercise 20 43.1 11.1
Stretching/Yoga 20 57.0 12.5
Is there a significant difference in mean HDL levels among the exercise groups? Run the test at a
5% level of significance. HINT: SSwithin = 21,860.
7. Consider again the data in problem #6. Suppose that in the aerobic exercise group we also
measured the number of hours of aerobic exercise per week and the mean is 5.2 hours with a
standard deviation of 2.1 hours. The sample correlation is -0.42.
a) Is there evidence of a s ...

14 + 8 Answers and calculations as basic statistics student would ex.docx

14 + 8 Answers and calculations as basic statistics student would explain put into both an MS Excel spreadsheet and copied into MS Word doc format. Due by 7pm 2/2/14.
Week 2 Assignment
A. What is the probability of rolling a four in the gambling dice game of craps (given two six sided dice)?
B. What is the probability that a player can roll a four 3 times in a row (assume that rolling the dice each time does not affect the outcome of the next roll)?
Population A and Population B both have a mean height of 70.0 inches with an SD of 6.0. A random sample of 30 people is picked from population A, and random sample of 50 people is selected from Population B. Which sample mean will probably yield a more accurate estimate of its population mean? Why?
3. Suppose we obtained data on vein size after application of a nitroglycerin ointment in a sample of 50 patients. The mean vein size is found to be 7.8mm with an SD of 2.1. Using a
t
distribution table, what are the confidence limits for a 95% confidence interval? For a 99% confidence interval?
4. In a pilot study evaluating the use of a new drug to lower resting heart rates (HR) of patients, the following data was recorded:
Subject #
Resting HR
001
72
002
88
003
71
004
87
005
64
006
77
007
79
008
59
009
66
010
68
011
78
012
89
013
91
014
81
015
77
016
75
017
69
Given that the average resting HR of the general population for this study is 72, use StatCrunch to perform the appropriate
t
test. What is the value of
t
? Using an alpha of 0.05, is the
t
statistic significant? Why? What are the confidence limits for a 95% confidence interval here and what do they mean for this patient group? Copy and Paste your work from StatCrunch into your Word document submission.
5. Write one or two sentences that could be used to report the results obtained for the t-test in Exercise 4.
6. For which of the following situations is the
independent
groups t-test appropriate (if inappropriate, why?):
a. The independent variable is infant birth weight at one week (normal vs high); the dependent variable is resting heart rate.
b. The independent variable is radiation treatment on throat cancer patients (after a low dose and then a high dose treatment); the dependent variable is white blood cell count.
c. The IV is infant birth weight (low vs normal birth weight); the DV is number of days absent from school in first grade.
d. The IV is gender (male vs female); the DV is compliance vs noncompliance with a medication regimen.
e. The independent variable is married status (single vs divorced vs married); the dependent variable is happiness measured on a scale from 1 to 50
7. For which of the following situations is the
dependent
groups t-test appropriate (if not appropriate, why?)
a. The IV is presence or absence of conversation directed to comato.

Kines 260 Take Home FinalNameDue Friday December 12th at 11 A.docx

Kines 260 Take Home Final
Name:
Due Friday December 12th at 11 A.M. in my mailbox
152 pts total
For Questions 1-2 use the 7 step process to answer. Refer to slides if you are unsure of the 7 step process. PLEASE DON’T OMIT ANY PART OF THE PROCESS!!! (40 pts) THIS IS DONE IN SPSS USING BREAST CANCER AND OBESITY DATASET
Dataset Background – PLEASE READ:
Obesity is very common in American society and is a risk factor for breast cancer for postmenopausal women. One mechanism explaining why obesity is a risk factor is that it may raise estrogen levels in women. In particular, one type of estrogen, serum estradiol, is a strong risk factor for breast cancer. To better assess this relationship, researchers studied a group of 200 postmenopausal women. The SPSS file is entitled, Breast Cancer and Obesity.
Adiposity was measured in two different ways: (a) by body mass index (BMI) = weight (kg) / height (m2) and also (b) by waist-hip ratio (WHR) = waist circumference/hip circumference. BMI is a measure of overall adiposity, whereas WHR is a measure of abdominal adiposity. In addition, a complete hormonal profile was obtained, including serum estradiol. Finally, other breast-cancer risk factors were also assessed among these women, including ethnicity, parity, age at first birth, and age at menarche.
Codebook
Variable
Column
Code
Label
Values (if categorical)
Id
1
Identification number
ES_1
2
Serum Estradiol
ETHNIC
3
Ethnicity
1 = African-American, 0 = Caucasian
NUMCHILD
4
Parity, number of children
AGEFBO
5
Age at 1st birth
(missing a response if never had a child)
ANYKIDS
6
Gave birth to any children?
1 = Yes, 0 = No
AGEMENAR
7
Age at menarche
BMI
8
Body Mass Index
WHR
9
Waist-hip ratio
**Missing responses are left blank
ALSO THE FOLLOWING CONTINUOUS VARIABLES HAVE BEEN CATEGORIZED!!
· BMI has been categorized, bmi_cat, : normal BMI (<25) and abnormal BMI (25 or greater).
· Menarche has been categorized, menarche_cat, two categories - 9-12 and 13-16
· WHR has also been categorized, whr_category, 3 categories - 0-.69, .7-.79, and .8 and greater
1. Is there a statistically significant difference in mean estradiol between African Americans and Caucasions?
a. Provide a visual aid depicting the mean differences between the two groups.
2. Is there a statistically significant difference in mean estradiol between ethnicity status depending on BMI_CAT (using the categorized variable, so normal or abnormal groups)?
3. Please use the rock climbing performance dataset. Here is the description: This is research done by a senior at PSU-Berks. He was interested in determining the effects of imagery on rock climbing performance. He chose 20 experienced rock climbers. With randomization on the order, he had them climb with no imagery on a rock wall and then had them climb with imagery (on a different but same difficulty ...

152 pts totalFor Questions 1-2 use the 7 step process to answe.docx

152 pts total
For Questions 1-2 use the 7 step process to answer. Refer to slides if you are unsure of the 7 step process.
PLEASE DON’T OMIT ANY PART OF THE PROCESS!!! (40 pts) THIS IS DONE IN SPSS USING BREAST CANCER AND OBESITY DATASET
Dataset Background – PLEASE READ:
Obesity is very common in American society and is a risk factor for breast cancer for postmenopausal women.
One mechanism explaining why obesity is a risk factor is that it may raise estrogen levels in women.
In particular, one type of estrogen, serum estradiol, is a strong risk factor for breast cancer.
To better assess this relationship, researchers studied a group of 200 postmenopausal women.
The SPSS file is entitled,
Breast Cancer and Obesity
.
Adiposity was measured in two different ways:
(a) by body mass index (BMI) = weight (kg) / height(m
2
) and also (b) by waist-hip ratio (WHR) = waist circumference/hip circumference.
BMI is a measure of overall adiposity, whereas WHR is a measure of abdominal adiposity.
In addition, a complete hormonal profile was obtained, including serum estradiol.
Finally, other breast-cancer risk factors were also assessed among these women, including ethnicity, parity, age at first birth, and age at menarche.
Codebook
Variable
Column
Code
Label
Values (if categorical)
Id
1
Identification number
ES_1
2
Serum Estradiol
ETHNIC
3
Ethnicity
1 = African-American, 0 = Caucasian
NUMCHILD
4
Parity, number of children
AGEFBO
5
Age at 1
st
birth
(missing a response if never had a child)
ANYKIDS
6
Gave birth to any children?
1 = Yes, 0 = No
AGEMENAR
7
Age at menarche
BMI
8
Body Mass Index
WHR
9
Waist-hip ratio
**Missing responses are left blank
ALSO THE FOLLOWING CONTINUOUS VARIABLES HAVE BEEN CATEGORIZED!!
BMI has been categorized, bmi_cat, :
normal BMI (<25) and abnormal BMI (25 or greater).
Menarche has been categorized, menarche_cat, two categories - 9-12 and 13-16
WHR has also been categorized, whr_category, 3 categories - 0-.69, .7-.79, and .8 and greater
1.
Is there a statistically significant difference in mean estradiol between African Americans and Caucasions?
a.
Provide a visual aid depicting the mean differences between the two groups.
2.
Is there a statistically significant difference in mean estradiol between ethnicity status depending on BMI_CAT (using the categorized variable, so normal or abnormal groups)?
3.
Please use the rock climbing performance dataset.
Here is the description:
This is research done by a senior at PSU-Berks.
He was interested in determining the effects of imagery on rock climbing performance.
He chose 20 experienced rock climbers.
With randomization on the order, he had them climb with no imagery on a rock wall and then had them climb with imagery (on a different but same difficulty wall).
Ignoring issues of confounding, can we conclude that imagery decreases rock wall climb time (in seconds)?
(15pts)
a.
What statistical test should be use.

Study on body fat density prediction

For a human body to function properly it is essential to have a certain amount of body fat. Fat serves to
manage body temperature, pads and protects the organs. Fat is the fundamental type of the body's vitality
stockpiling. It is important to have a healthy amount of body fat. Overabundance of fat quotient can build
danger of genuine wellbeing issues. Anthropometry is a broadly accessible and basic strategy for the
appraisal of body composition. Anthropometry measures are weight, height, Body Mass Index (BMI),
waist, boundary, biceps, skinfold etc. The human fat percentage is figured by taking anthropometric
variables. We proposed a methodology to determine the body fat percentage using R programming and
regression formula. We analyzed 10 anthropometric variables and 3 demographic variables. Our study
shows that the impact of certain variables has an edge over other in predicting body fat percentage.

Running head Research report11Research report13Business Resea.docx

Running head: Research report 11
Research report 13Business Research Project Part 5:Research Report
Alexis Madera, Christopher Lauko, Cristina Linares,
Marina Garcia, & Sarah Maokosy
QNT/561
August 4, 2014
Jonathan Edelman
Research Report
Due to the new fitness and health awareness fad, gym memberships are growing rapidly. In order to better understand the demographics and cater to the needs of gym members, research is required. Fitness Gymnasium is a large fitness gym in Lincoln, Nebraska. Members of Learning Team A are a part of the management team. The management team has been tasked to provide health and wellness checkups for 40 random female gym members.
The purpose of this research is to obtain an overall understanding of the female demographics of the gym. The results of this research will provide Fitness Gymnasium with enough information to create fitness programs based on the needs of its female members. Blood pressure is an important variable to consider before allowing customers to obtain gym memberships. Varying results have been observed amongst the Blood Pressure and Body Mass Index (BMI) of female gym members.
Hypothesis
H0: There is no correlation between an applicant's BP and their BMI.
Ha: There is a correlation between an applicant's BP and their BMI.
Study
The purpose of this study is to find a correlation between Body Mass Index (BMI) and Blood Pressure (BP). Studies have shown those who have an above average BMI are more likely to have long term health problems such as high BP or hypertension. BMI is the calculation of a person's weight and height, and provides a reliable indicator of body fat in a person (Centers for Disease Control, 2013).BP is the measurement of the force of blood running through veins and arteries, which is measured in two numbers as a ratio: Systolic and Diastolic (S/D) (American Heart Association, 2014). According to a past research study, those who increased their BMI also increased their BP, which put them at risk of hypertension (Droyvold, Midthjell, Nilsen, & Holmen, 2005). From this, Fitness Gymnasium will create a program with the needs and the well-being of its female members in mind.
Population and Size
The current population and size is the amount of gym members. Fitness Gymnasium’s population is the amount of all male and female gym members, which is 100 members. With that being said, the population for Fitness Gymnasium is all male and female gym members, and the size is 100 members.
Target Population and Sample Size
The current target population for the gym is female gym members. At Fitness Gymnasium, the current percentage of female members is 50% of 100 total gym members, which makes the target population 50 females. For Fitness Gymnasium, the sample size is 40 using a 95% confidence level, 5% margin of error with a target population of 40 females (Appendix A).
Assessment/Survey
Fitness Gymnasium created the following survey to observe the correlation between a member’s BMI a.

Stat170 - Introductory Statistics Semester 2, 2015 Assignmen.docx

Stat170 - Introductory Statistics
Semester 2, 2015 Assignment 2
Instructions:
1. Type your answers directly into this document.
2. The answers to all questions are to be word processed. You can either type formulae into your solution or you can use the equation editor in Word or you may include hand-written equations and diagrams by photographing them so that you have the image saved as a picture file and then pasting (inserting) the image/s into your solution.
3. Your assignment should be uploaded as a Word (.doc or .docx) or PDF file (created from a Word processed document) ONLY. Other formats, including a PDF created from an image, will not be accepted by the system.
Information:
Q1
Q2
Q3
Q4
Total
10
13
10
17
50
1) Question 1 (10 marks)
Include an appropriate diagram for each part of this question. You can sketch these diagrams and paste in a photo of your sketch, along with your solutions.
a. BranCrunch is a new breakfast cereal. Boxes of BranCrunch are labelled ‘ 675 grams’ but there is some variation. The actual mean weight is 675 grams with a standard deviation of 21 grams.
i. Dan’s Discount Store sells BranCrunch in mega-packs of 8 boxes. Assuming the weights of boxes of BranCrunch are normally distributed, find the probability that the average weight of a mega-pack of BranCrunch is higher than 665 grams.
ii. Louie’s Convenience Store receives a shipment of 30 boxes of BranCrunch. Louie’s will complain to the manufacturers if the total weight of this shipment is lower than 20 kg. Find the probability that Louie’s Convenience Store will complain about the shipment and explain why the information about the weights of BranCrunch following a normal distribution is not necessary to answer this part of the question.
b. In 2014 the Department of Social Services reported that 32% of current marriages in Australia were expected to end in divorce.
Find the probability that more than 8 marriages out of a random sample of 20 marriages which were current in 2014 would end in divorce.
Question 2 (13 marks)
Lean body mass is the amount of weight carried on the body that is not fat. Metabolic rate is the rate at which the body consumes energy. The following Minitab output was constructed using data recorded in a fitness study which was designed to compare the lean body masses and also the metabolic rates of adolescent males and adolescent females. Use this output to answer the questions which follow.
Two-sample T for LeanBodyMass
Sex N Mean StDev SE Mean
Male 75 52.63 6.66 0.77
Female 75 43.42 6.05 0.70
Difference = μ (Male) - μ (Female)
Estimate for difference: 9.21
95% CI for difference: (****, ****)
T-Test of difference = 0 (vs ≠): T-Value = **** P-Value = **** DF = ****
Both use Pooled StDev = 6.3633
Two-sample T for MetRate
SE
Sex N Mean StDev Mean
Male 75 1626 227 26
Female 75 1258 172.

Data.savQuestion.docxOver the same period, determine wheth.docx

This document provides instructions for conducting three chi-square tests of independence using SPSS on data about students' conflict resolution styles and suspensions from school. It describes entering the data, selecting the appropriate tests and variables, and interpreting the output. Students are asked to conduct the chi-square tests following the five steps of hypothesis testing, calculate effect sizes, and explain the results to someone unfamiliar with statistics.

Week 2 Assignment1. A. What is the probability of rolling a four.docx

This document contains instructions and questions for a statistics assignment involving probability, confidence intervals, hypothesis testing using t-tests and ANOVA, chi-square tests, and matching statistical tests to research situations. It includes 14 multiple choice and short answer questions requiring calculations and interpretations of statistical analyses on sample data using the StatCrunch software.

13. Construyendo capacidades locales para la Seguridad Alimentaria y Nutricional

13. Construyendo capacidades locales para la Seguridad Alimentaria y NutricionalPrograma Mundial de Alimentos

This paper analyzes the impact of food vouchers, vouchers plus nutrition training, and vouchers plus training and water purification on malnutrition in Ecuador using an experimental design. The interventions had no significant impact on consumption, chronic malnutrition, or anemia. However, food vouchers had a significant positive impact on dietary diversity. Adding training or water purification to vouchers did not result in differentiated effects, suggesting food vouchers alone are the most cost-effective for improving dietary diversity.Research Critique GuidelinesQuantitative StudyBackground of .docx

Research Critique Guidelines
Quantitative Study
Background of Study:
· Identify the clinical problem and research problem that led to the study. What was not known about the clinical problem that, if understood, could be used to improve health care delivery or patient outcomes? This gap in knowledge is the research problem.
· How did the author establish the significance of the study? In other words, why should the reader care about this study? Look for statements about human suffering, costs of treatment, or the number of people affected by the clinical problem.
· Identify the purpose of the study. An author may clearly state the purpose of the study or may describe the purpose as the study goals, objectives, or aims.
· List research questions that the study was designed to answer. If the author does not explicitly provide the questions, attempt to infer the questions from the answers.
· Were the purpose and research questions related to the problem?
Methods of Study
· Identify the benefits and risks of participation addressed by the authors. Were there benefits or risks the authors do not identify?
· Was informed consent obtained from the subjects or participants?
· Did it seem that the subjects participated voluntarily in the study?
· Was institutional review board approval obtained from the agency in which the study was conducted?
· Are the major variables (independent and dependent variables) identified and defined? What were these variables?
· How were data collected in this study?
· What rationale did the author provide for using this data collection method?
· Identify the time period for data collection of the study.
· Describe the sequence of data collection events for a participant.
· Describe the data management and analysis methods used in the study.
· Did the author discuss how the rigor of the process was assured? For example, does the author describe maintaining a paper trail of critical decisions that were made during the analysis of the data? Was statistical software used to ensure accuracy of the analysis?
· What measures were used to minimize the effects of researcher bias (their experiences and perspectives)? For example, did two researchers independently analyze the data and compare their analyses?
Results of Study
· What is the researcher's interpretation of findings?
· Are the findings valid or an accurate reflection of reality? Do you have confidence in the findings?
· What limitations of the study were identified by researchers?
· Was there a coherent logic to the presentation of findings?
· What implications do the findings have for nursing practice? For example, can the findings of the study be applied to general nursing practice, to a specific population, or to a specific area of nursing?
· What suggestions are made for further studies?
Ethical Considerations
· Was the study approved by an Institutional Review Board?
· Was patient privacy protected?
· Were there ethical considerations regarding the tr.

Chi-square tests are great to show if distributions differ or i.docx

Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...

Bases talk for slideshare (atkinson)

The document discusses detecting and quantifying individual responses to exercise interventions. It notes that there are true, clinically meaningful differences between individuals in how they respond to the same exercise program. However, some studies that claim to show individual response differences are flawed. Simply looking at responses in a treatment group without a control is not enough. Using a measurement error statistic like the technical error of measurement to define "responders" is also illogical, as such a statistic would label about 24% of individuals as responders or adverse responders in a reliability study alone. The document advocates for using replicated crossover studies and controlling for factors like regression to the mean to better understand individual variability in exercise responses.

Seminar iv

This document outlines aspects of interpreting quantitative research results. It discusses interpreting results with graphs and diagrams, credibility and different types of biases, magnitude and precision of results, and clinical versus statistical significance. It provides examples of interpreting hypothesized, non-significant, and unhypothesized results. The document emphasizes considering validity, bias, corroboration, and effect sizes when interpreting results as well as implications, generalizability, and significance of findings.

teaching-2394666758-7290-1618944682-1.pptx

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1. What is a codebook Who might create one, what would she or h.docx

1. What is a codebook? Who might create one, what would she or he include in it, and what purpose or purposes would it serve?
2. Explain the distinction between variable labels and value labels in an electronic dataset.
3. True or False: The linear regression model cannot handle curvilinear relationships between independent and dependent variables.
4. True or False: By convention, if we conduct a statistical hypothesis test and obtain a p-value of .3, we would reject the null hypothesis.
5. Suppose you have two SPSS datasets. The first contains the variables ID, X1, X2, and X3 for participants 1 through 100; the second contains the variables ID, X4, X5, and X6 for the same 100 participants. Suppose that the datasets are named EvalPre.sav and EvalPost.sav, and are saved on your computer in following file location:
C:\Documents and Settings\Evaluation\EvalData\
And suppose, finally, that you want to combine these datasets to create a new dataset, to be named EvalPrePost.sav, containing ID and X1 through X6 for all 100 participants. What SPSS syntax would you use to accomplish this?
6. A research team is studying cognitive decline in old age. They collect data on 300 people between the ages of 75 and 95 years. One of the key variables is a measure of one particular aspect of cognitive functioning: Executive function (named EXFUNC in the dataset). For this study it is measured using a test that produces values ranging from 0 to 100, with higher values representing better executive function. The investigators fit a linear regression model to their data and obtain the following estimated model:
EXFUNCi = 161.73 – 1.05AGEi + ei
According to this model, by how many points does the typical score on the executive function scale decline between age 80 and 90?
7. Suppose your boss gives you a dataset and asks you to run frequencies on the variables X1 and X4, and descriptive statistics on the variables X2, X3, X5, and X6. What SPSS syntax would you use to accomplish this task? (Please present only the command(s) that generate the frequencies and descriptive statistics.)
8. Suppose your dataset has a variable, X1, that was derived from a questionnaire item with a response options ranging from Strongly Disagree (coded 1) to Strongly Agree (coded 5). Because the wording of this item runs in the opposite direction of the wording of several related items, you want to create a reverse-coded version of this variable on which Strongly Disagree will be coded 5 while Strongly Agree will be coded 1. What SPSS syntax would you use to accomplish this task?
9. An investigator interested in regional differences in breastfeeding attitudes and practices conducts a national survey. The survey includes a multi-item instrument measuring breastfeeding attitudes. The resulting breastfeeding attitudes scale takes values ranging from 1 to 5, with higher numbers representing more favorable attitudes toward breastfeeding. This scale score is name ...

1. complete stats notes

1) Statistics is the science of collecting, analyzing, and drawing conclusions from data. It is used to understand populations based on samples since directly measuring entire populations is often impossible.
2) There are two main types of data: qualitative data which relates to descriptive characteristics, and quantitative data which can be expressed numerically. Common statistical analyses include calculating the mean, standard deviation, and using t-tests, ANOVA, correlation, and chi-squared tests.
3) Statistical analyses allow researchers to determine uncertainties in measurements, compare groups, identify relationships between variables, and assess whether observed differences are likely due to chance or a factor being studied. Key concepts include null and alternative hypotheses, p-values, and effect size.

1. The following are body mass index (BMI) scores measured in 12.docx

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Bases talk for slideshare (atkinson)

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Seminar iv

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teaching-2394666758-7290-1618944682-1.pptx

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1. complete stats notes

1. complete stats notes

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Use the attendee list QR codes to register attendees quickly. Each attendee will have a QR code, which we can easily scan to register for an event. You will get the attendee list from the “Attendees” menu under “Reporting” menu.

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ByWater Solutions, a leader in open-source library software, will discuss the future of open-source AI Models and Retrieval-Augmented Generation (RAGs). Discover how these cutting-edge technologies can transform information access and management in special libraries. Dive into the open-source world, where transparency and collaboration drive innovation, and learn how these can enhance the precision and efficiency of information retrieval.
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In this talk we will review recent research work carried out at the University of Saint Joseph and its partners in Macao. The focus of this research is in application of Artificial Intelligence and neuro sensing technology in the development of new ways to engage with brands and consumers from a business and design perspective. In addition we will review how these technologies impact resilience and how the University benchmarks these results against global standards in Sustainable Development.

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This is an introduction to Google Productivity Tools for office and personal use in a Your Skill Boost Masterclass by the Excellence Foundation for South Sudan on Saturday 13 and Sunday 14 July 2024. The PDF talks about various Google services like Google search, Google maps, Android OS, YouTube, and desktop applications.Brigada Eskwela 2024 PowerPoint Update for SY 2024-2025

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A beginner’s guide to project reviews - everything you wanted to know but wer...

A beginner’s guide to project reviews - everything you wanted to know but wer...Association for Project Management

APM event held on 9 July in Bristol.
Speaker: Roy Millard
The SWWE Regional Network were very pleased to welcome back to Bristol Roy Millard, of APM’s Assurance Interest Group on 9 July 2024, to talk about project reviews and hopefully answer all your questions.
Roy outlined his extensive career and his experience in setting up the APM’s Assurance Specific Interest Group, as they were known then.
Using Mentimeter, he asked a number of questions of the audience about their experience of project reviews and what they wanted to know.
Roy discussed what a project review was and examined a number of definitions, including APM’s Bok: “Project reviews take place throughout the project life cycle to check the likely or actual achievement of the objectives specified in the project management plan”
Why do we do project reviews? Different stakeholders will have different views about this, but usually it is about providing confidence that the project will deliver the expected outputs and benefits, that it is under control.
There are many types of project reviews, including peer reviews, internal audit, National Audit Office, IPA, etc.
Roy discussed the principles behind the Three Lines of Defence Model:, First line looks at management controls, policies, procedures, Second line at compliance, such as Gate reviews, QA, to check that controls are being followed, and third Line is independent external reviews for the organisations Board, such as Internal Audit or NAO audit.
Factors which affect project reviews include the scope, level of independence, customer of the review, team composition and time.
Project Audits are a special type of project review. They are generally more independent, formal with clear processes and audit trails, with a greater emphasis on compliance. Project reviews are generally more flexible and informal, but should be evidence based and have some level of independence.
Roy looked at 2 examples of where reviews went wrong, London Underground Sub-Surface Upgrade signalling contract, and London’s Garden Bridge. The former had poor 3 lines of defence, no internal audit and weak procurement skills, the latter was a Boris Johnson vanity project with no proper governance due to Johnson’s pressure and interference.
Roy discussed the principles of assurance reviews from APM’s Guide to Integrated Assurance (Free to Members), which include: independence, accountability, risk based, and impact, etc
Human factors are important in project reviews. The skills and knowledge of the review team, building trust with the project team to avoid defensiveness, body language, and team dynamics, which can only be assessed face to face, active listening, flexibility and objectively.
Click here for further content: https://www.apm.org.uk/news/a-beginner-s-guide-to-project-reviews-everything-you-wanted-to-know-but-were-too-afraid-to-ask/Java MCQ Questions and Answers PDF By ScholarHat

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- 2. Question Evaluate if there is a relationship (predict) between the personal characteristics and the screening tools with weight loss. Prepare a short description of what was done and what you found. IV=independent variable, DV= dependent variable Conduct a multiple linear regression to predict satisfaction using all of the personal characteristics and perceptions variables (if appropriate). Follow the guide in Module 9 of how to conduct this analysis and include in your description what you did such as the following: a) Define the hypothesis b) Describe each variable using appropriate descriptive statistics; no need to recode anything but make sure dummy coding is correct; create a ‘table1-remember analysis exercise 1’for this step c) Run bivariate associations (why? need IV by each IV to check for _________) d) Run the full model (DV and multiple IVs ) –show evidence that you checked assumptions, etc (for this exercise it is ok to enter the selected IVs all at once in one ‘block’) e) Summarize the above (a-d) and the results in your OWN words f) Include IS raw output view or the Excel output. excelhomeworkhelp.com
- 3. Solution a) Hypothesis Null hypothesis: there is no relationship between the dependent variable (Islost) and the independent variable (sex, age, diet, exercise, confid, sedentary) Alternative hypothesis: there is at least a relationship between the dependent (Islost) and the independent variable (sex, age, diet, exercise, confid, sedentary) a) Descriptive statistics n mean Median Standard deviation Age 51 26.94 23 8.09 Exercise 51 39.59 39 5.49 Confid 51 17.78 17 3.37 Sedentary 51 114.39 114 4.40 Ibslost 51 24.43 24 5.07 Sex 51 Female (57%) Male (43%) diet 51 Yes (63%) No (37%) excelhomeworkhelp.com
- 4. The table above shows the descriptive statistics of the weight dataset. 57% of the total participants are female while 43% of the participant are male. 63% of the participant have diet adherence while 37% do not have diet adherence. The average age of participants was 26.96 years (SD = 8.09). The mean and standard deviation of minutes exercising per day is (39.59, 5.49), confidence in success (M = 17.78, SD = 3.37) respectively, minutes in active per day (M = 114.39, 4.40), pounds lost since start of the program (24.43, 5.07). excelhomeworkhelp.com
- 5. c. The bivariate association graph above shows the bivariate relationship between the dependent variable and the independent variables. The dependent variable is weight loss while the independents variables are age, exercise, sedentary, and confid. d. The following are the assumption of multiple linear regression which is illustrate from the graphs below; •There exists a linear relationship between the dependent and independent variables •The independent variables are not highly correlated with each other •The variance of the residuals is constant •Independence of observation •Multivariate normality i.e. it follows a normal distribution 0 10 20 30 40 0 20 40 60 80 100 120 lbslost Sample Percentile Normal Probability Plot excelhomeworkhelp.com
- 6. 0 10 20 30 40 0 20 40 60 lbslost age age Line Fit Plot lbslost Predicted lbslost 0 10 20 30 40 0 10 20 30 lbslost confid confid Line Fit Plot lbslost Predicted lbslost excelhomeworkhelp.com
- 7. 0 10 20 30 40 0 20 40 60 lbslost exercise exercise Line Fit Plot lbslost Predicted lbslost 0 10 20 30 40 105 110 115 120 125 lbslost sedentary sedentary Line Fit Plot lbslost Predicted lbslost excelhomeworkhelp.com
- 8. -20 0 20 40 60 80 100 120 140 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 Line chart of Weighloss data sex age diet exercise confid sedentary lbslost e. Multiple regression (OLS) was used to estimate the ability sex, age, diet, exercise, confidence in success, minutes inactive per day, in predicting weight loss. Forty- fivepercent of the variance surrounding weight loss was explained by sex, age, diet, exercise, confidence in success, minutes inactive per day weight (R2 = 0.4567). Overall, the model was statistically significant weight loss (F = 6.1667, p = 0.000). Sex, Age, Exercise, Confidence in success, and Minutes inactive per day was not statistically significant in the model (p > 0.05); whereas diet was statistically significant (t = 2.096, p = 0.04). For every one cm increase in head circumference, motor coordination scores increased by 0.65 points (beta = 0.65). Males were also found to score higher than females. Males scores were .35 points higher (beta=.35, p=.04). excelhomeworkhelp.com
- 10. Part B. Multiple logistic regression Question Task: Now we would like to see if we can find a relationship (predict) between weight loss and some of the personal characteristics and the chance of recommending the clinic to others.Prepare a short description including the following information: 1. Is running a multiple logistic regression appropriate for this task? Explain why it is or is not appropriate. 2. Define the hypotheses 3. How many and what percent of patients indicated they would recommend the clinic? 4. You do not need to run logistic regression in EXEL or IS. Use the output below to write a summary of the relationship. DV: recommend clinic to others (1=yes vs 0=no) B S.E Sig OR 95% C I for OR Lower Upper Lbslost Sex (female vs male) Age 248 1.393 .011 .095 .694 .044 .009 .045 .801 1.282 4.028 1.011 1.064 1.034 .028 1.544 15.683 1.102 excelhomeworkhelp.com
- 11. Diet Constant .113 -7.228 .757 2.780 .882 .009 1.119 .001 .254 4.935 Solution 1. Is running a multiple logistic regression appropriate for this task? Explain why it is or is not appropriate. Answer: Yes, this is because the outcome or target variable is binary (yes or no) and since the number of observations is greater than the number of features in the datasets, there is no room for overfitting in the model. 2. Define the hypotheses Ans: : There is a relationship between weight loss and some of the personal characteristics and the chance of recommending the clinic to others. i.e. H1: There is a relationship between weight loss and some of the personal characteristics and the chance of recommending the clinic to others. i.e. excelhomeworkhelp.com
- 12. 3. How many and what percent of patients indicated they would recommend the clinic? Ans: 25 (Twenty-five) patients and 49 % of patients indicated that they would recommend the clinic. 4. Logistic multiple regression was used to estimate the ability of Age, Sex, Lbslost and Diet in predicting if the patients will recommend the clinic (yes) or not (No). Age and Diet were not statistically significant in the model (p > 0.05). A significant association was found between variables: Lbslost, Sex and Patients recommending the Clinic and there’s no significant relationship between Age, Diet and Patients recommending the Clinic. An increase in sex of the patients will increase the odds of recommending the clinics by four fold (Odds ratio= 4.03, 95% confidence interval= 1.034, 15.68, p<.001), an increase in Lbslost of the patients will increase the odds of recommending the Clinic by almost two fold (Odds ratio= 1.28, 95% confidence interval= 1.064, 1.544, p<.001) and an increase in Age (Odds ratio= 1.011, 95% confidence interval= 0.928, 1.102, p<.001) and Diet (Odds ratio= 1.119, 95% confidence interval= 0.254, 4.935, p<.001) of the patients will increase the odds of recommending the Clinic by one fold respectively. Part C. Sensitivity & Specificity Question Recall that our survey used a self-report measure of diet adherence. We want to assess if excelhomeworkhelp.com
- 13. the results are valid and accurate by comparing the self-report with a gold standard (stool sample detecting microbiome and should see only small amounts of fats and sugars, etc). We identify 15 true positives out of the 32 clients who self-identified as being diet adherent and 18 true negatives. 1. Fill in the following table 2. Calculate the sensitivity of the self-report measure. 3. Calculate the specificity of the self- report measure. 4. What does this mean—was our self-report of diet adherence a good measure? What does having a good or poor measure mean when exploring relationships, how do you think about it when applying these kinds of evidence based findings? Gold standard positive Gold standard negative Total Self-report +adherence Self- reportnonadherence Total excelhomeworkhelp.com
- 14. Solution 1. Fill in the following table 2. Calculate the sensitivity of the self-report measure. Sensitivity = 15/32 = 0.46875 3. Calculate the specificity of the self- report measure. Specificity = 18/32 = 0.5625 4. What does this mean—was our self-report of diet adherence a good measure? What does having a good or poor measure mean when exploring relationships, how do you think about it when applying these kinds of evidence based findings? Since both sensitivity and specificity have average values, it does not indicate a good measure. Gold standard positive Gold standard negative Total Self-report +adherence 15 17 32 Self- reportnonadherence 14 18 32 Total 29 35 64 excelhomeworkhelp.com
- 15. Part D. Run Chart 1. Did the proportion of women administered RhoGam vaccination change—what was the mean before and after the program change? From the data provided, I notice that the proportion of women administered RhoGam vaccination change, the mean before program change is 51.89 while the mean after program change is 51.33 2. Did all the changes that were made lead to improvements? The changes that were lead does not lead to much improvement based on the data analyzed using the run chart. 3. What data would you want to start collecting to determine other steps for quality improvement in these patients? In other to determine other steps for quality improvement in the patients, I will suggest that data can be collected on the average glucose intake, percentage of time in hypoglycemic ranges, and percentage of time in hyperglycemic range. excelhomeworkhelp.com