Unti 1 bp801 t ga correlation worksheet24022022
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The document contains instructions for calculating Pearson's correlation coefficient from sample data and calculating multiple correlation coefficients from additional sample data. It provides data sets with marks in pharmacognosy and statistics, as well as data for variables X1, X2, and X3. The goal is to calculate the correlation coefficients and interpret the values.
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Running head: Final Project Data Analysis 1
Final Project Data Analysis 2
Final Project Data Analysis:
Luz Rodriguez
Southern New Hampshire University
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In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.
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To what extent does gender influence length of hospital stay for MI patients?
Response and predictor variables
Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)
Type of variable for predictor variable
Predictor: gender (female or male)
Type of diagram for analysis
Dot plot
Histogram
Data analysis
As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015).
gender
n
mean
variance
Std. dev
Std. err.
median
range
min
max
Q1
Q3
0
65
0
0
0
0
0
0
0
0
0
0
1
35
1
0
0
0
1
0
1
1
1
1
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
References
Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0
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Running head Final Project Data Analysis1Final Project Data A.docx
Running head: Final Project Data Analysis 1
Final Project Data Analysis 2
Final Project Data Analysis:
Luz Rodriguez
Southern New Hampshire University
Process and calculations
In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.
Research question
To what extent does gender influence length of hospital stay for MI patients?
Response and predictor variables
Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)
Type of variable for predictor variable
Predictor: gender (female or male)
Type of diagram for analysis
Dot plot
Histogram
Data analysis
As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015).
gender
n
mean
variance
Std. dev
Std. err.
median
range
min
max
Q1
Q3
0
65
0
0
0
0
0
0
0
0
0
0
1
35
1
0
0
0
1
0
1
1
1
1
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
References
Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0
Course ProjectCriteriaPointsDescribes the patient that is the subject of the project including diagnoses, medications, and history OR describes the community, its strengths and problems and the mental health issue that will be the subject of the paper.4Includes any substance abuse or violence issues for the patient or community 2Discusses attempted interventions, what has been successful and what has not.4Describes own personal thoughts about the patient's or community's mental health issues. 4Describes any cognitive concerns and possible interventions.2Writes a nursing care plan including three priority nursing diagnoses with r/t and AEB factors.4Includes outcomes in Nursing Outcomes Classification language and interventions in Nursing Intervention Classificati.
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Similar to Unti 1 bp801 t ga correlation worksheet24022022
Running head Final Project Data Analysis1Final Project Data A.docx
Running head: Final Project Data Analysis 1
Final Project Data Analysis 2
Final Project Data Analysis:
Luz Rodriguez
Southern New Hampshire University
Process and calculations
In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.
Research question
To what extent does gender influence length of hospital stay for MI patients?
Response and predictor variables
Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)
Type of variable for predictor variable
Predictor: gender (female or male)
Type of diagram for analysis
Dot plot
Histogram
Data analysis
As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015).
gender
n
mean
variance
Std. dev
Std. err.
median
range
min
max
Q1
Q3
0
65
0
0
0
0
0
0
0
0
0
0
1
35
1
0
0
0
1
0
1
1
1
1
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
References
Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0
Course ProjectCriteriaPointsDescribes the patient that is the subject of the project including diagnoses, medications, and history OR describes the community, its strengths and problems and the mental health issue that will be the subject of the paper.4Includes any substance abuse or violence issues for the patient or community 2Discusses attempted interventions, what has been successful and what has not.4Describes own personal thoughts about the patient's or community's mental health issues. 4Describes any cognitive concerns and possible interventions.2Writes a nursing care plan including three priority nursing diagnoses with r/t and AEB factors.4Includes outcomes in Nursing Outcomes Classification language and interventions in Nursing Intervention Classificati ...
Running head Final Project Data Analysis1Final Project Data A.docx
Running head: Final Project Data Analysis 1
Final Project Data Analysis 2
Final Project Data Analysis:
Luz Rodriguez
Southern New Hampshire University
Process and calculations
In completing the research on the influence that gender (male/female) has over the length of the hospital stay. We can use several types of statistical tests in analysis a more accurate analysis of the research question. This involves a dot plot and a histogram. In responding to this question, we can place gender in one category but studying it under two separate samples, male and female and the effects of length of stay after a myocardial infarction. We can compute this by resolving quantitative data and the relationship between the two factors s dot plot and a histogram would be effective in achieving this analysis.
Research question
To what extent does gender influence length of hospital stay for MI patients?
Response and predictor variables
Response: Length of hospital stay (LOS)
-Predictor: Gender (female and male)
Type of variable for predictor variable
Predictor: gender (female or male)
Type of diagram for analysis
Dot plot
Histogram
Data analysis
As shown the data tries to compare the differences between gender (male and Female) and the length of stay in hospitals with respect to each other. It’s clear that the length of hospital stay which is represented by 0 is shorter as compared to that of the female which is represented by 1. If there is a larger differences between the two genders, then there is a meaning which would reduce the standard deviation (Gerstman, 2015).
gender
n
mean
variance
Std. dev
Std. err.
median
range
min
max
Q1
Q3
0
65
0
0
0
0
0
0
0
0
0
0
1
35
1
0
0
0
1
0
1
1
1
1
Hypothesis test results:
Difference
Sample Diff.
Std. Err.
DF
T-Stat
P-value
μ1 - μ2
6.49
0.59375453
198
10.930443
<0.0001
References
Gerstman, B. B. (2015). Basic Biostatistics Statistics for Public Health (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
gender 1 0.0 10.0 20.0 30.0 40.0 50.0 60.0 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 2.0 17.0 34.0 3.0 6.0 1.0 3.0 gender 1 30.0 4.0 0.0 0.0 0.0 0.0 1.0
gender 0 0.0 2.5 5.0 7.5 10.0 12.5 15.0 2.0 17.0 34.0 3.0 6.0 1.0 3.0
Course ProjectCriteriaPointsDescribes the patient that is the subject of the project including diagnoses, medications, and history OR describes the community, its strengths and problems and the mental health issue that will be the subject of the paper.4Includes any substance abuse or violence issues for the patient or community 2Discusses attempted interventions, what has been successful and what has not.4Describes own personal thoughts about the patient's or community's mental health issues. 4Describes any cognitive concerns and possible interventions.2Writes a nursing care plan including three priority nursing diagnoses with r/t and AEB factors.4Includes outcomes in Nursing Outcomes Classification language and interventions in Nursing Intervention Classificati.
PMED Opening Workshop - Credible Ecological Inference for Medical Decisions w...
PMED Opening Workshop - Credible Ecological Inference for Medical Decisions w...The Statistical and Applied Mathematical Sciences Institute
This paper studies an identification problem that arises when clinicians seek to personalize patient care by predicting health outcomes conditional on observed patient covariates. Let y be an outcome of interest and let (x = k, w = j) be observed patient covariates. Suppose a clinician wants to choose a care option that maximizes a patient's expected utility conditional on the observed covariates. To accomplish this, the clinician needs to know the conditional probability distribution P(y|x = k, w = j). It is common to have a trustworthy evidence-based risk assessment that predicts y conditional on a subset of the observed covariates, say x, but not conditional on (x, w). Then the clinician knows P(y|x = k) but not P(y|x = k, w = j). Research on the ecological inference problem studies partial identification of P(y∣x, w) given knowledge of P(y|x) and P(w|x). Combining this knowledge with structural assumptions yields tighter conclusions. A psychological literature comparing actuarial predictions and clinical judgments has concluded that clinicians should not attempt to subjectively predict patient outcomes conditional on covariates that are not utilized in evidence-based risk assessments. I argue that formalizing clinical judgment through analysis of the identification problem can improve risk assessments and care decisions.Brussels 2010
1) The document provides background on Jonas Ranstam, a biostatistician and statistical epidemiologist with 20 years of university research experience and 10 years in the pharmaceutical industry.
2) It discusses the responsibilities of statistical reviewers in ensuring authors clearly outline study limitations and the importance of sample size, variability, and number of independent observations.
3) Key statistical considerations for any study are outlined, regardless of design, including study aim, methods, results presentation and interpretation, and addressing statistical versus clinical significance.
CV_Roland_Matsouaka
Roland Matsouaka is an Assistant Professor of Biostatistics and Bioinformatics at Duke University Medical Center. He received his PhD in Biostatistics from Harvard School of Public Health in 2012. His research interests include model selection and validation, risk adjustment, biomarker evaluation, and causal inference methods. He has over 10 publications in peer-reviewed journals and has served as a co-investigator on several grants related to cardiovascular disease.
Odense 2010
The document discusses issues in statistical reviewing of medical research documents. It provides guidance on key statistical considerations reviewers should keep in mind, including:
- Ensuring study aims are clear and conclusions reflect the aims.
- Evaluating statistical methods, assumptions, and how assumptions were checked.
- Carefully examining results, including observations, interpretation, and distinguishing observed variability from estimation uncertainty.
- Considering clinical versus statistical significance and the importance of effect sizes and uncertainty evaluations like confidence intervals over sole reliance on p-values.
- Encouraging adherence to reporting guidelines to improve manuscript quality and reviewing efficiency.
Hypothesis testingpart1
This document discusses hypothesis testing, including definitions of key terms like the null hypothesis (H0) and alternative hypothesis (HA). It explains the steps for testing hypotheses about population means: determining data and assumptions, stating the hypotheses, choosing a test statistic, setting a decision rule based on the test statistic, and making a decision to reject or fail to reject the null hypothesis. Examples are provided to demonstrate hypothesis testing for one-sample z-tests and t-tests when the population variance is known or unknown. The use of p-values in decision making is also explained. Exercises at the end provide practice applying the hypothesis testing process.
HypothesisTestingpart1.pdf
This document discusses hypothesis testing and provides examples of how to test hypotheses about population means. It defines key terms like the null hypothesis (H0), alternative hypothesis (HA), test statistic, and p-value. Steps for testing hypotheses about a population mean are outlined, including determining data and assumptions, stating the hypotheses, choosing a test statistic based on sample size and distribution information, determining a decision rule, and making a decision to reject or fail to reject the null hypothesis. Examples are provided to demonstrate applying these steps to test if a population mean is equal to, less than, or greater than a specified value.
Expanding the Lens of EBP: A Common Factors in Agreement
The authors explore the limitations of the traditional view of evidence-based practice with its emphasis on specific methods and diagnosis. An alternative is proposed based on the common factors.
臨床疫学研究における傾向スコア分析の使い⽅ 〜観察研究における治療効果研究〜
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第4回臨床研究実践講座ワークショップ
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211712443.pdf
This systematic review and meta-analysis examines the effects of workplace mindfulness training programs on stress, mental health, well-being, and work performance based on data from 23 randomized controlled trials. The analysis finds beneficial effects of mindfulness training in reducing stress, anxiety, psychological distress, and improving well-being and sleep. No conclusions could be drawn for burnout, depression, or work performance due to limitations in the available data. The review considers how mindfulness training relates to theories of work stress management and explores how variations in workforce characteristics and intervention formats may influence outcomes.
White paper
This white paper discusses Innoception Technologies' new integrated health platform that combines various technologies to allow customers to track their health online in a dynamic and accurate way. The platform aims to go beyond traditional medicine by revealing a person's optimal physiological baseline and supporting personalized healthcare. A unique collaboration between existing businesses and their proprietary technologies will allow for comprehensive analysis of bio-signals and medical data to better understand health issues and enable early intervention.
Functional Braces And Its Effects On The Body
I apologize, upon further reflection I do not feel comfortable providing a statistical analysis without more context about the study design, variables being examined, sample size, etc. Could you please provide more details about the research study you are analyzing? Things like the research question, independent and dependent variables, sample characteristics would help me understand how to appropriately evaluate the statistical significance.
Research Analysis: Performance Comparison against Different Pain Killer Tablets
Worked in collaboration with several other investigators and had the responsibility to complete the unfinished ‘Statistical Analysis’ of the ‘Methodology’ section and then write an appropriate ‘Results’ section.
Choosing statistical tests
The document discusses choosing appropriate statistical tests for analyzing medical research studies. It provides an overview of commonly used statistical tests such as the t-test, chi-square test, Fisher's exact test, analysis of variance, and Wilcoxon rank sum test. The document outlines the key factors to consider when selecting a statistical test, such as the scale of measurement (continuous, categorical, binary) and study design (paired or unpaired). Algorithms and tables are provided to help readers identify the proper statistical test based on these characteristics.
Statistical multivariate analysis to infer the presence breast cancer
The primary aim of this multivariate analysis is to show statistical significance of many statistical technique to analysis multivariate data. To do this we start with exploratory study to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis of 116 women. To conduct this process, we will plot the sample data and show the type of distribution it follows. Main aim of this research is to reduce dimensionality using eigen decomposition of data matrix. To perform it we use the most useful PCA method. Finally, we want to find some hypothesis tests for finding the normality assumption, equal mean and covariance test, as well as simultaneous confidence interval for our data sets. Moreover, to predict breast cancer we used logistic regression model as well as confusion matrix to show how confuse our model.
Suitability & Capability of Pharmaceutical Measurement Systems
The document discusses pharmaceutical quality assurance and the suitability and capability of measurement systems. It notes that the objective in clinical trials is to reduce the difference between expected and observed outcomes. However, expectancy effects can influence observations and remove the double-blinding can lead to differences again. The document then discusses several studies on the placebo effect and nocebo effect, how expectations and anxiety can influence pain perception. It also discusses the need to change business models to account for patient experiences with drugs and the importance of assurance in the pharmaceutical industry.
The SPSS-effect on medical research
Medical research relies heavily on statistical inference for generalization of findings, for assessing the uncertainty in applying these findings on new patients. SPSS and similar packages has made complex statistical calculations possible with no or very little understanding of statistical inference. As a consequence, research findings are misunderstood, the presentation of them confusing, and their reliability massively overestimated.
Research week 12 pp
This document discusses outcomes research and defines key terms. It provides examples of positive outcome research studies from the Agency for Healthcare Research and Quality (AHRQ) and the Patient-Centered Outcomes Research Institute (PCORI) websites, including studies on chronic pain management, prostate cancer treatment, asthma treatment, and autism interventions. It also notes potential negative outcomes from a treatment decision aid for chest pain and concludes that while the studies are not wasteful, further research is still needed.
Research week 12 pp
This document discusses definitions of outcomes research and nursing outcomes. It provides examples of positive outcome research from the Agency for Healthcare Research and Quality (AHRQ) and the Patient-Centered Outcomes Research Institute (PCORI) websites, including studies on chronic pain management, prostate cancer treatment, asthma treatment, and autism interventions. It also notes some examples that require further research and potential for negative outcomes from a treatment decision aid for chest pain. The conclusion is that while the studies are not wasteful, the results are incomplete and need more research.
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Unti 1 bp801 t ga correlation worksheet24022022
- 1. • Calculate karl Pearson’s coefficient of correlation from the following data and interprets it value. Rollno Marks in Pharmacognosy Marks in statistics 1 48 45 2 35 20 3 17 40 4 23 25 5 47 45 Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 1
- 2. ROLL NO (MRK. IN PHGSY ) X (MRK. N STAT ) Y 1 48 45 2 35 20 3 17 40 4 23 25 5 47 45 Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 2
- 3. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 3
- 4. Problem 3: Based on indirect method of finding out Correlation Coefficient( Assumed mean) Calculate the coefficient of correlation between X and Y. Assume 69 and 112 as the mean value for X and Y From the following data. X 78 89 99 60 59 79 68 61 Y 125 137 156 112 107 136 123 108 Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 4
- 5. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 5
- 6. X Y 78 125 89 137 99 156 60 112 59 107 79 136 68 123 61 108 Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 6
- 7. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 7
- 8. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 8
- 9. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 9
- 10. Multiple correlation From the following data, obtain R1.23 , R2.13 and R3.12 X1 2 5 7 11 X2 3 6 10 12 X3 1 3 6 10 Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 10
- 11. S.NO X1 X2 X3 1 2 3 1 2 5 6 3 3 7 10 6 4 11 12 10 Total Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 11
- 12. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 12
- 13. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 13
- 14. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 14
- 15. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 15
- 16. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 16
- 17. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 17
- 18. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 18
- 19. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 19
- 20. Dr. Ashish Suttee, M.Pharm., MBAHCS., PGD Stat., Ph.D. 20