Statistical Analysis is complex part but reporting of data in proper manner with proper selective graphs & interpretations is also necessary part of data analysis !!!
PUH 5302, Applied Biostatistics 1
Course Learning Outcomes for Unit III
Upon completion of this unit, students should be able to:
4. Recommend solutions to public health problems using biostatistical methods.
4.1 Compute and interpret probability for biostatistical analysis.
4.2 Draw conclusions about public health problems based on biostatistical methods.
5. Analyze public health information to interpret results of biostatistical analysis.
5.1 Analyze literature related to biostatistical analysis in the public health field.
5.2 Prepare an annotated bibliography that explores a topic related to public health issues.
Course/Unit
Learning Outcomes
Learning Activity
4.1
Unit Lesson
Chapter 5
Unit III Problem Solving
4.2
Unit Lesson
Chapter 5
Unit III Problem Solving
5.1
Chapter 5
Unit III Annotated Bibliography
5.2
Chapter 5
Unit III Annotated Bibliography
Reading Assignment
Chapter 5: The Role of Probability
Unit Lesson
Welcome to Unit III. In previous units, we discussed some fundamentals of biostatistics and their application
to solving public health problems. In Unit III, we will compute, interpret, and apply probability, especially in
relation to different populations.
Computing and Interpreting Probabilities
Probability means using a number (or numbers) to demonstrate how likely something is to occur. For
example, if a coin is tossed, the probability of getting a heads or tail is one out of two chances; that is ½.
Researchers have used probability studies to predict weather and other events and have been successful to
some extent. Public health professionals have used statistical methods to predict the chances of health-
related events, thereby providing arguments in favor of taking precautionary measures and warning the
general public on important health issues.
In biostatistics, we use both descriptive statistics and inferential statistics to address public health issues
within a population. In most cases, researchers are not able to study the entire population; they try to get a
sample from the population from which they can generalize their findings.
Descriptive Statistics
Aside from the use of probability sampling methods, there are other methods used for the computation and
interpretation of data; these are generally known as descriptive statistics. With descriptive statistics, we
UNIT III STUDY GUIDE
Probability
PUH 5302, Applied Biostatistics 2
UNIT x STUDY GUIDE
Title
normally compute the mean, mode, median, variance, and standard deviation. Information obtained using
such computation methods is used for descriptive purposes, as opposed to information obtained from
inferential statistics.
Let’s examine this example using the numbers 5, 10, 2, 4, 6, 10, 2, 3, and 2.
The mean is the sum of all the numbers ÷ the number of cases
= 37 ÷ 9
= 4.11
The median is the middle number after the numbers have been arranged in an ascending or descend ...
I am Samson H. I am a Multiple Linear Regression Homework Expert at statisticshomeworkhelper.com. I hold a Master's in Statistics, from Michigan, USA. I have been helping students with their homework for the past 12 years. I solved homework related to Multiple Linear Regression.
Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com.You can also call on +1 678 648 4277 for any assistance with Multiple Linear Regression Homework Help.
PUH 5302, Applied Biostatistics 1
Course Learning Outcomes for Unit III
Upon completion of this unit, students should be able to:
4. Recommend solutions to public health problems using biostatistical methods.
4.1 Compute and interpret probability for biostatistical analysis.
4.2 Draw conclusions about public health problems based on biostatistical methods.
5. Analyze public health information to interpret results of biostatistical analysis.
5.1 Analyze literature related to biostatistical analysis in the public health field.
5.2 Prepare an annotated bibliography that explores a topic related to public health issues.
Course/Unit
Learning Outcomes
Learning Activity
4.1
Unit Lesson
Chapter 5
Unit III Problem Solving
4.2
Unit Lesson
Chapter 5
Unit III Problem Solving
5.1
Chapter 5
Unit III Annotated Bibliography
5.2
Chapter 5
Unit III Annotated Bibliography
Reading Assignment
Chapter 5: The Role of Probability
Unit Lesson
Welcome to Unit III. In previous units, we discussed some fundamentals of biostatistics and their application
to solving public health problems. In Unit III, we will compute, interpret, and apply probability, especially in
relation to different populations.
Computing and Interpreting Probabilities
Probability means using a number (or numbers) to demonstrate how likely something is to occur. For
example, if a coin is tossed, the probability of getting a heads or tail is one out of two chances; that is ½.
Researchers have used probability studies to predict weather and other events and have been successful to
some extent. Public health professionals have used statistical methods to predict the chances of health-
related events, thereby providing arguments in favor of taking precautionary measures and warning the
general public on important health issues.
In biostatistics, we use both descriptive statistics and inferential statistics to address public health issues
within a population. In most cases, researchers are not able to study the entire population; they try to get a
sample from the population from which they can generalize their findings.
Descriptive Statistics
Aside from the use of probability sampling methods, there are other methods used for the computation and
interpretation of data; these are generally known as descriptive statistics. With descriptive statistics, we
UNIT III STUDY GUIDE
Probability
PUH 5302, Applied Biostatistics 2
UNIT x STUDY GUIDE
Title
normally compute the mean, mode, median, variance, and standard deviation. Information obtained using
such computation methods is used for descriptive purposes, as opposed to information obtained from
inferential statistics.
Let’s examine this example using the numbers 5, 10, 2, 4, 6, 10, 2, 3, and 2.
The mean is the sum of all the numbers ÷ the number of cases
= 37 ÷ 9
= 4.11
The median is the middle number after the numbers have been arranged in an ascending or descend ...
I am Samson H. I am a Multiple Linear Regression Homework Expert at statisticshomeworkhelper.com. I hold a Master's in Statistics, from Michigan, USA. I have been helping students with their homework for the past 12 years. I solved homework related to Multiple Linear Regression.
Visit statisticshomeworkhelper.com or email info@statisticshomeworkhelper.com.You can also call on +1 678 648 4277 for any assistance with Multiple Linear Regression Homework Help.
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Follo.docxaman341480
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Following ANOVA
Analysis of variance (ANOVA)
is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable (see
Exercise 18
). In this exercise, we will focus on the
one-way ANOVA
, which involves testing one independent variable and one dependent variable (as opposed to other types of ANOVAs, such as factorial ANOVAs that incorporate multiple independent variables).
Why ANOVA and not a
t
-test? Remember that a
t
-test is formulated to compare two sets of data or two groups at one time (see
Exercise 23
for guidance on selecting appropriate statistics). Thus, data generated from a clinical trial that involves four experimental groups, Treatment 1, Treatment 2, Treatments 1 and 2 combined, and a Control, would require 6
t
-tests. Consequently, the chance of making a Type I error (alpha error) increases substantially (or is inflated) because so many computations are being performed. Specifically, the chance of making a Type I error is the number of comparisons multiplied by the alpha level. Thus, ANOVA is the recommended statistical technique for examining differences between more than two groups (
Zar, 2010
).
ANOVA is a procedure that culminates in a statistic called the
F
statistic. It is this value that is compared against an
F
distribution (see
Appendix C
) in order to determine whether the groups significantly differ from one another on the dependent variable. The formulas for ANOVA actually compute two estimates of variance: One estimate represents differences between the groups/conditions, and the other estimate represents differences among (within) the data.
Research Designs Appropriate for the One-Way ANOVA
Research designs that may utilize the one-way ANOVA include the randomized experimental, quasi-experimental, and comparative designs (
Gliner, Morgan, & Leech, 2009
). The independent variable (the “grouping” variable for the ANOVA) may be active or attributional. An active independent variable refers to an intervention, treatment, or program. An attributional independent variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. The ANOVA can compare two groups or more. In the case of a two-group design, the researcher can either select an independent samples
t
-test or a one-way ANOVA to answer the research question. The results will always yield the same conclusion, regardless of which test is computed; however, when examining differences between more than two groups, the one-way ANOVA is the preferred statistical test.
Example 1: A researcher conducts a randomized experimental study wherein she randomizes participants to receive a high-dosage weight loss pill, a low-dosage weight loss pill, or a placebo. She assesses the number of pounds lost from baseline to post-treatment
378
for the thre ...
Trochim, W. M. K. (2006). Internal validity.httpwww.socialrescurranalmeta
Trochim, W. M. K. (2006). Internal validity.
http://www.socialresearchmethods.net/kb/intval.php
Please follow link:^^^^^
Social Work Research: Chi Square
Molly, an administrator with a regional organization that advocates for alternatives to long-term prison sentences for nonviolent offenders, asked a team of researchers to conduct an outcome evaluation of a new vocational rehabilitation program for recently paroled prison inmates. The primary goal of the program is to promote full-time employment among its participants.
To evaluate the program, the evaluators decided to use a quasi-experimental research design. The program enrolled 30 individuals to participate in the new program. Additionally, there was a waiting list of 30 other participants who planned to enroll after the first group completed the program. After the first group of 30 participants completed the vocational program (the “intervention” group), the researchers compared those participants’ levels of employment with the 30 on the waiting list (the “comparison” group).
In order to collect data on employment levels, the probation officers for each of the 60 people in the sample (those in both the intervention and comparison groups) completed a short survey on the status of each client in the sample. The survey contained demographic questions that included an item that inquired about the employment level of the client. This was measured through variables identified as none, part-time, or full-time. A hard copy of the survey was mailed to each probation officer and a stamped, self-addressed envelope was provided for return of the survey to the researchers.
After the surveys were returned, the researchers entered the data into an SPSS program for statistical analysis. Because both the independent variable (participation in the vocational rehabilitation program) and dependent variable (employment outcome) used nominal/categorical measurement, the bivariate statistic selected to compare the outcome of the two groups was the Pearson chi-square.
After all of the information was entered into the SPSS program, the following output charts were generated:
TABLE 1. CASE PROCESSING SUMMARY
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Program
Participation
*Employment
59
98.3%
1
1.7%
60
100.0%
TABLE 2. PROGRAM PARTICIPATION *EMPLOYMENT CROSS TABULATION
Employment
Total
None
Part-Time
Full-Time
Program
Participation
Intervention
Group
Count % within Program Participation
5
16.7%
7
23.3%
18
60.0%
30
100.0%
Comparison
Group
Count % within Program Participation
16
55.2%
7
24.1%
6
20.7%
29
100.0%
Total
Count % within Program Participation
21
35.6%
14
23.7%
24
40.7%
59
100.0%
TABLE 3. CHI-SQUARE TESTS
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
11.748a
2
.003
Likelihood Ratio
12.321
2
.002
Linear-by-Linear Association
11.548
1
.001
N of Valid Cases
59
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.88.
The first table, titled Case ...
This presentation takes you through the basics of statistical analysis with SPSS.
Covering:
Basics of statistics
Descriptive statistics: frequency, mean, median, standard deviation
Comparative Statistics: t test, ANOVA, Chi square, Pearson correlation, Regression analysis
This ppt is all about Biostatistics for Medical, Nursing and Pharmacy Students...
The Essentials of Biostatistics for Physicians, Nurses, and Clinicians
Biostatistics – Lecture Notes/Book (PDF) Nursing
Biostatistics are the development and application of statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.
I am Samson H. I am a Multiple Linear Regression Homework Expert at excelhomeworkhelp.com. I hold a Master's in Statistics, from Michigan, USA. I have been helping students with their homework for the past 7 years. I solved homework related to Multiple Linear Regression.
Visit excelhomeworkhelp.com or email info@excelhomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Multiple Linear Regression Homework.
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Follo.docxaman341480
Calculating Analysis of Variance (ANOVA) and Post Hoc Analyses Following ANOVA
Analysis of variance (ANOVA)
is a statistical procedure that compares data between two or more groups or conditions to investigate the presence of differences between those groups on some continuous dependent variable (see
Exercise 18
). In this exercise, we will focus on the
one-way ANOVA
, which involves testing one independent variable and one dependent variable (as opposed to other types of ANOVAs, such as factorial ANOVAs that incorporate multiple independent variables).
Why ANOVA and not a
t
-test? Remember that a
t
-test is formulated to compare two sets of data or two groups at one time (see
Exercise 23
for guidance on selecting appropriate statistics). Thus, data generated from a clinical trial that involves four experimental groups, Treatment 1, Treatment 2, Treatments 1 and 2 combined, and a Control, would require 6
t
-tests. Consequently, the chance of making a Type I error (alpha error) increases substantially (or is inflated) because so many computations are being performed. Specifically, the chance of making a Type I error is the number of comparisons multiplied by the alpha level. Thus, ANOVA is the recommended statistical technique for examining differences between more than two groups (
Zar, 2010
).
ANOVA is a procedure that culminates in a statistic called the
F
statistic. It is this value that is compared against an
F
distribution (see
Appendix C
) in order to determine whether the groups significantly differ from one another on the dependent variable. The formulas for ANOVA actually compute two estimates of variance: One estimate represents differences between the groups/conditions, and the other estimate represents differences among (within) the data.
Research Designs Appropriate for the One-Way ANOVA
Research designs that may utilize the one-way ANOVA include the randomized experimental, quasi-experimental, and comparative designs (
Gliner, Morgan, & Leech, 2009
). The independent variable (the “grouping” variable for the ANOVA) may be active or attributional. An active independent variable refers to an intervention, treatment, or program. An attributional independent variable refers to a characteristic of the participant, such as gender, diagnosis, or ethnicity. The ANOVA can compare two groups or more. In the case of a two-group design, the researcher can either select an independent samples
t
-test or a one-way ANOVA to answer the research question. The results will always yield the same conclusion, regardless of which test is computed; however, when examining differences between more than two groups, the one-way ANOVA is the preferred statistical test.
Example 1: A researcher conducts a randomized experimental study wherein she randomizes participants to receive a high-dosage weight loss pill, a low-dosage weight loss pill, or a placebo. She assesses the number of pounds lost from baseline to post-treatment
378
for the thre ...
Trochim, W. M. K. (2006). Internal validity.httpwww.socialrescurranalmeta
Trochim, W. M. K. (2006). Internal validity.
http://www.socialresearchmethods.net/kb/intval.php
Please follow link:^^^^^
Social Work Research: Chi Square
Molly, an administrator with a regional organization that advocates for alternatives to long-term prison sentences for nonviolent offenders, asked a team of researchers to conduct an outcome evaluation of a new vocational rehabilitation program for recently paroled prison inmates. The primary goal of the program is to promote full-time employment among its participants.
To evaluate the program, the evaluators decided to use a quasi-experimental research design. The program enrolled 30 individuals to participate in the new program. Additionally, there was a waiting list of 30 other participants who planned to enroll after the first group completed the program. After the first group of 30 participants completed the vocational program (the “intervention” group), the researchers compared those participants’ levels of employment with the 30 on the waiting list (the “comparison” group).
In order to collect data on employment levels, the probation officers for each of the 60 people in the sample (those in both the intervention and comparison groups) completed a short survey on the status of each client in the sample. The survey contained demographic questions that included an item that inquired about the employment level of the client. This was measured through variables identified as none, part-time, or full-time. A hard copy of the survey was mailed to each probation officer and a stamped, self-addressed envelope was provided for return of the survey to the researchers.
After the surveys were returned, the researchers entered the data into an SPSS program for statistical analysis. Because both the independent variable (participation in the vocational rehabilitation program) and dependent variable (employment outcome) used nominal/categorical measurement, the bivariate statistic selected to compare the outcome of the two groups was the Pearson chi-square.
After all of the information was entered into the SPSS program, the following output charts were generated:
TABLE 1. CASE PROCESSING SUMMARY
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Program
Participation
*Employment
59
98.3%
1
1.7%
60
100.0%
TABLE 2. PROGRAM PARTICIPATION *EMPLOYMENT CROSS TABULATION
Employment
Total
None
Part-Time
Full-Time
Program
Participation
Intervention
Group
Count % within Program Participation
5
16.7%
7
23.3%
18
60.0%
30
100.0%
Comparison
Group
Count % within Program Participation
16
55.2%
7
24.1%
6
20.7%
29
100.0%
Total
Count % within Program Participation
21
35.6%
14
23.7%
24
40.7%
59
100.0%
TABLE 3. CHI-SQUARE TESTS
Value
df
Asymp. Sig. (2-sided)
Pearson Chi-Square
11.748a
2
.003
Likelihood Ratio
12.321
2
.002
Linear-by-Linear Association
11.548
1
.001
N of Valid Cases
59
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.88.
The first table, titled Case ...
This presentation takes you through the basics of statistical analysis with SPSS.
Covering:
Basics of statistics
Descriptive statistics: frequency, mean, median, standard deviation
Comparative Statistics: t test, ANOVA, Chi square, Pearson correlation, Regression analysis
This ppt is all about Biostatistics for Medical, Nursing and Pharmacy Students...
The Essentials of Biostatistics for Physicians, Nurses, and Clinicians
Biostatistics – Lecture Notes/Book (PDF) Nursing
Biostatistics are the development and application of statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results.
I am Samson H. I am a Multiple Linear Regression Homework Expert at excelhomeworkhelp.com. I hold a Master's in Statistics, from Michigan, USA. I have been helping students with their homework for the past 7 years. I solved homework related to Multiple Linear Regression.
Visit excelhomeworkhelp.com or email info@excelhomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Multiple Linear Regression Homework.
Similar to Statistical Analysis.02.06.2022 (1).pdf (20)
Sample Size determination feels magical to students but it is really easy task !
This is not mine ! This is avileble in G power module too
www.gpowertools.com
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Best Ayurvedic medicine for Gas and IndigestionSwastikAyurveda
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
1. Chapter -
Data Presentation, Analysis and Interpretations
Introduction
Data Analysis is a key phase of research work. The present chapter entitled ‘Data Presentation,
Analysis and Interpretation comprised of four sections Part ‘A’, Part ‘B’ and the details of each
section are given below,
A. Presentation, analysis and interpretation of data is done with help of sorting the raw data,
its coding, classification and tabulation, percentage calculation and drawing the
inferences.
B. Statistical Analysis is done by using measures of central tendency, measures of variation,
Testing of Hypothesis is done by using statistical tools like One Sample t test, ANOVA
etc. In the present chapter the information comprising to several variables is presented in
order to pertain a fair comprehensive profile of respondents
This chapter begins with the information on the Clinical results and the description of the
respondent’s demographic information. The descriptive analysis of the variables used in this
study is also presented. This is followed closely by the testing of the hypotheses formulated for
this study and presented in the order of the hypotheses. Each hypothesis focused on the variables
of the research with dependent and independent variable.
The analysis of the hypotheses is carried out based on the statistical tools adopted. The
researcher’s position in this study is clearly stated under result presentation and discussion.
These views are within the theoretical framework of this study.
Reliability test is the most common measure of internal consistency ("Reliability"). It is most
commonly used. After testing the reliability we can conclude for normality of the data so here we
checked the normality. As researcher found that the data is normal, we used the parametric test
so researcher used the one sample t test, ANOVA for analysis.
Note:For all the statistical tests, p<0.05 was considered to be statistically significant, keeping α
error at 5% and β error at 20%, thus giving a power to the study as 80%.
* = statistically significant difference (p<0.05)
** = statistically highly significant difference (p<0.01)
# = non significant difference (p>0.05) … for all tables
Survey Results
Survey Results of this study are analyzed using SPSS 25 (SPSS, Inc., 2020) statistical program.
2. Table No 1: Age Distribution
Age Group Distribution
Parameter Frequency Percent Valid Percent
Cumulative
Percent
Valid
30 To 40 9 25.0 25.0 25.0
40 To 50 14 38.9 38.9 63.9
50 to 60 8 22.2 22.2 86.1
60 to 70 5 13.9 13.9 100.0
Total 36 100.0 100.0
(Source: Primary Data)
Descriptive Statistics
N Minimum Maximum Mean
Std.
Deviation
Variance
Statistic Statistic Statistic Statistic
Std.
Error Statistic Statistic
Age 36 31 66 46.56 1.769 10.611 112.597
Graph No 1: Age Group Distribution
(Source: Primary Data)
Interpretation:
Age distribution has scattered in the age group of 40 to 30 with sample size of 36 was 38.9
percent. In the age group 30 to 40, it was 25 percent while in the age group of 50 to 60 it was
22.2 percent. Age distribution has mean 46.56 with S.D. 10.611.
0
10
20
30
40
30 To 40 40 To 50 50 to 60 60 to 70
Age Group Distribution
Series1 Series2
3. Table No 1: Gender Distribution
Gender
Parameter Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Female 20 55.6 55.6 55.6
Male 16 44.4 44.4 100.0
Total 36 100.0 100.0
(Source: Primary Data)
Graph No 2: Gender Distribution
(Source: Primary Data)
Interpretation:
Gender distribution has scattered in the female group 55.6 percent and in a male group it is 44.4
6 percent .
Table No 3: Diet Distribution
Diet
Parameter
Frequency Percent
Valid
Percent
Cumulative
Percent
Valid Mixed 36 100.0 100.0 100.0
Veg 0 0 0 0
Total 37 100.0 100.0
(Source: Primary Data)
Interpretation:
From table no 3 , it was seen that in a sample size of 36 , 100 percent respondents having mixed
diet .
44.4%
55.6 %
Gender Distribution
1 2
4. Table No 4: Occupation Distribution
Occupation
Parameter Frequency Percent Valid Percent
Cumulative
Percent
Valid
Business 1 2.8 2.8 2.8
Employe 1 2.8 2.8 5.6
Engineer 6 16.7 16.7 22.2
farming 1 2.8 2.8 25.0
Housewife 12 33.3 33.3 58.3
Job 6 16.7 16.7 75.0
Lawyer 1 2.8 2.8 77.8
Peon 1 2.8 2.8 80.6
Retired 1 2.8 2.8 83.3
Salesman 1 2.8 2.8 86.1
shopkeeper 2 5.6 5.6 91.7
Teacher 1 2.8 2.8 94.4
veg.vendor 1 2.8 2.8 97.2
webdesigner 1 2.8 2.8 100.0
Total 36 100.0 100.0
(Source: Primary Data)
Graph No 3: Occupation Distribution
(Source: Primary Data)
Interpretation:
From the above table, 33.3 % respondents were housewife, 16.7 % was engineer and 16.7 %
were having job, rest 2.8 were from all sectors like business, employment, farming, lawer, poen,
retired person, salespersons, shopkeepers etc.
0
5
10
15
20
25
30
35
Business
Employe
Engineer
farming
Housewife
Job
Lawyer
Peon
Retired
Salesman
shopkeeper
Teacher
veg.vendor
webdesigner
Occupation Distribution
Series1 Series2
5. Table No 5: HODM Distribution
HODM Distribution
Parameters Frequency Percent Valid Percent
Cumulative
Percent
Valid
Absent 24 66.7 66.7 66.7
Present 12 33.3 33.3 100.0
Total 36 100.0 100.0
(Source: Primary Data)
Graph No 4: HODM Distribution
(Source: Primary Data)
Interpretation:
In HODM was absent 66.7 % as well as it was present 33.3 % respondents.
26%
74%
HODM
1
2
6. Table No 5: Shoulder Joint Involvement Distribution
Shoulder joint Involvement
Parameters Frequency Percent Valid Percent Cumulative Percent
Valid Bilateral 5 13.9 13.9 13.9
Left 17 47.2 47.2 61.1
Right 14 38.9 38.9 100.0
Total 36 100.0 100.0
(Source: Primary Data)
Graph No 5: Shoulder Joint Involvement Distribution
(Source: Primary Data)
Interpretation:
From the above table , respondents had shoulder joint involvement 47.2 % at right side , 38.9 %
at right side and 13.9 % had bilateral side.
14%
47%
39%
Shoulder Joint Involvement
Bilateral
Left
Right
7. Table No 6: Chronicity Distribution
Chronicity_1
Chronic in Months Frequency Percent
Valid
Percent
Cumulative Percent
Valid
.5 2 5.6 5.6 5.6
.6 2 5.6 5.6 11.1
1.0 18 50.0 50.0 61.1
2.0 2 5.6 5.6 66.7
3.0 5 13.9 13.9 80.6
5.0 3 8.3 8.3 88.9
6.0 4 11.1 11.1 100.0
Total 36 100.0 100.0
(Source: Primary Data)
Graph No 6: Chronicity Distribution
(Source: Primary Data)
Interpretation:
From the above table , respondents had maximum joint pain of 1 month as 50 % ,3 months
chronic pain as 13.9 % , and 6 months chronic pain was for 11.1 % , 5 months chronic pain was
for 8.3 % .Total near about 80 % respondents had chronic pain.
0
10
20
30
40
50
.5 .6 1.0 2.0 3.0 5.0 6.0
Chronicity Distribution
Series1 Series2
8. Table No 7: Bahushool Distribution
Bahushool Distribution
Bahushool Scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
No Pain 0 0 1 2.8
Mild pain,can do strenuous
work with difficulty
3 8.3 25 69.4
Moderate pain can do normal
work with support
23 63.9 10 27.8
Severe pain,unable to do any
work at all
10 27.8 0 0.0
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 7: Bahushool Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment ,there was 63.9 % respondents has a moderate pain that is they
can work with support ,27.8 % has severe pain that they was unable to work but only 8.3 % has
mild pain but after the treatment 69.4% respondents reduced to mild pain and 27.8 %
respondents reduced to moderate pain .
0
10
20
30
40
50
60
70
Frequency Percent Frequency Percent
Before Treatment After Treatment
Bahushool Distribution
No Pain
Mild pain,can do strenuous work with
difficulty
Moderate pain can do normal work with
support
Severe pain,unable to do any work at all
9. Table No 8: Bahuprapanditahar Distribution
Bahupraspanditahar Distribution
Bahupraspanditahar Scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
No stiffness 0 0 9 25.0
Mild,has difficulty in moving the
joint without support
12 33.3 24 66.7
Moderate,has difficulty in
moving,can lift only with
support
22 61.1 3 8.3
Severe,unable to lift 2 5.6 0 0.0
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 8: Bahupraapanditahar Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment ,there was 61.1 % respondents has a moderate difficulty in
moving bahu can lift only with support, 33.3 % has mild difficulty in moving the joint without
support ,only 5.6 % has severe difficulty but after the treatment 66.7% respondents reduced to
mild pain and 25 % respondents reduced to no stiffness in bahuprapanditahar .
0
20
40
60
80
Frequency Percent Frequency Percent
Before Treatment After Treatment
Bahupraspanditahar Scale
No stiffness
Mild,has difficulty in moving the joint without support
Moderate,has difficulty in moving,can lift only with support
Severe,unable to lift
10. Table No 9: Abduction Distribution
Abduction Distribution
Abduction scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
0-45 Degree 12 33.3 0 0.0
45-90 Degree 24 66.7 9 25.0
90-135 Degree
23 63.9
24 66.7
135-180 Degree 10 27.8 3 8.3
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 9: Abduction Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment, there was 66.6 % respondents who can move their abduction in
45 to 95 degree , 63.9 % respondents who can move their abduction in 90 to 135 degree, 33.3 %
has respondents who can move in 0 to 45 degree and 27.8 % respondents who can move in 135
to 180 degree but after treatment 66.7 % respondents who can move their abduction in 90 to 135
degree, there was 25 % respondents who can move their abduction in 45 to 95 degree.
0
10
20
30
40
50
60
70
Frequency Percent Frequency Percent
Before Treatment After Treatment
Abduction Distribution
0-45 Degree 45-90 Degree 90-135 Degree 135-180 Degree
11. Table No 10: Extension Distribution
Extension Distribution
Extension Scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
0-45 Degree 5 13.9 0 0.0
45-90 Degree 25 69.4 11 30.6
90-135 Degree 6 16.7 18 50.0
135-180 Degree 0 0.0 7 19.4
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 10: Extension Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment, there was 69.4 % respondents who have extension in 45 to 95
degree , 16.7 % respondents who can move their extension in 90 to 135 degree, 13.9 % has
respondents who has extension in 0 to 45 degree but after treatment 50 % respondents who can
move their extension in 90 to 135 degree, there was 30.6 % respondents who has extension in 45
to 95 degree and 19.4% respondents has extension 135 to 180 degree.
0
10
20
30
40
50
60
70
Frequency Percent Frequency Percent
Before Treatment After Treatment
Extension Distribution
0-45 Degree 45-90 Degree 90-135 Degree 135-180 Degree
12. Table No 11: Flexion Distribution
Flexion Distribution
Flexion Scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
0-45 Degree 9 25.0 0 0.0
45-90 Degree 25 69.4 7 19.4
90-135 Degree 2 5.6 25 69.4
135-180 Degree 0 0.0 4 11.1
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 11: Flexion Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment, there was 69.4 % respondents who have extension in 45 to 95
degree , 5.6 % respondents who can move their extension in 90 to 135 degree, 25 % has
respondents who has extension in 0 to 45 degree but after treatment 69.4% respondents who can
move their extension in 90 to 135 degree, there was 19.4 % respondents who has extension in 45
to 95 degree and 11.1% respondents has extension 135 to 180 degree.
0
10
20
30
40
50
60
70
Frequency Percent Frequency Percent
Before Treatment After Treatment
Flexion Distribution
0-45 Degree 45-90 Degree 90-135 Degree 135-180 Degree
13. Table No 12: Internal Rotation Distribution
Internal Rotation Distribution
Internal Rotation Flexion Scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
Upto 90 Degree 0 0.0 7 19.4
Upto 60 Degree 6 16.7 26 72.2
Upto 30 Degree 29 80.6 3 8.3
Upto 0 Degree 1 2.8 0 0.0
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 12: Internal Rotation Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment, there was 80.6 % respondents who has internal rotation flexion
scale upto 30 degree , 16.7 % has internal rotation upto 60 degree and 2.8 % respondents has
internal rotation upto 0 degree but after treatment 72.2% respondents has internal rotation upto
60 degree and 19.4 % respondents has internal rotation upto 90 degree.
0
10
20
30
40
50
60
70
80
90
Frequency Percent Frequency Percent
Before Treatment After Treatment
Internal Rotation Distribution
Upto 90 Degree Upto 60 Degree Upto 30 Degree Upto 0 Degree
14. Table No 13: External Rotation Distribution
External Rotation Distribution
External Rotation Flexion
Scale
Before Treatment After Treatment
Frequency Percent Frequency Percent
Valid
Upto 90
Degree
0 0.0 7 19.4
Upto 60
Degree
21 58.3 27 75.0
Upto 30
Degree
15 41.7 2 5.6
Upto 0
Degree
0 0.0 0 0.0
Total 36 100.0 36 100.0
(Source: Primary Data)
Graph No 13: External Rotation Distribution
(Source: Primary Data)
Interpretation:
It was seen that before treatment, there was 58.3 % respondents who has external rotation
flexion scale upto 60 degree , 41.7 % has external rotation upto 30 degree but after treatment
75% respondents has external rotation upto 60 degree and 19.4 % respondents has external
rotation upto 90 degree.
0
10
20
30
40
50
60
70
80
Frequency Percent Frequency Percent
Before Treatment After Treatment
External Rotation Distribution
Upto 90 Degree Upto 60 Degree Upto 30 Degree Upto 0 Degree
15. Table No 14: Descriptive Statistics
Descriptive Statistics
Parameter
N Minimum Maximum Mean
Std.
Deviation
Variance
Confidence
Interval
Statistic Statistic Statistic Statistic
Std.
Error
Statistic Statistic Statistic
Chronicity_1 36 0.5 6.0 2.172 0.3103 1.8620 3.467 2.172± 1.86
Bahushool 0
BT 36 1 3 2.19 0.096 0.577 0.333 2.19 ± 0.577
AT 36 0 2 1.25 0.083 0.500 0.250 1.25 ± 0.50
Bahupraspanditahar 0
BT_A 36 1 3 1.72 0.094 0.566 0.321 1.72 ± 0.566
AT_A 36 0 2 0.83 0.093 0.561 0.314 0.83 ± 0.566
ABDUCTION 0
BT_B 36 2 3 2.33 0.080 0.478 0.229 2.33 ± 0.478
AT_B 36 0 2 1.17 0.093 0.561 0.314 1.172± 0.562
Extension 0
BT_C 36 1 3 1.97 0.093 0.560 0.313 1.97 ± 0.56
AT_C 36 0 2 1.11 0.118 0.708 0.502 1.11 ± 0.708
Flexion 0
BT_D 36 1 3 2.19 0.087 0.525 0.275 2.19 ± 5.25
AT_D 36 0 2 1.08 0.092 0.554 0.307 1.08 ± 0.554
Internal Rotation 0
BT_E 36 1 3 1.86 0.071 0.424 0.180 1.86 ± 0.424
AT_E 36 0 2 0.89 0.087 0.523 0.273 0.89 ± 0.523
External Rotation 0
BT_F 36 1 2 1.42 0.083 0.500 0.250 1.42 ± 0.500
AT_F 36 0 2 0.86 0.081 0.487 0.237 0.86 ± 0.487
Valid N (listwise) 0
(Source: Primary Data)
Interpretation:
From the above table it was seen that chronity has mean 2.172 with Standard deviation of 3.467
and follows in a confidence interval of 2.172± 1.86.
Bhabushool has mean 2.19 which was reduced to 1.25 after treatment with S.D. 0.250.
Bahupraspanditahar has mean 1.72 which was reduced to 0.83 with standard deviation 0.561
.Abduction has mean 2.33 which was reduced to 1.17 with standard deviation 0.561 as
confidence interval 1.172± 0.562.
Extension has mean 1.97 which was reduced to 1.11 with standard deviation 0.708 as confidence
interval 1.11 ± 0.708.
16. Flexion has mean 2.19 which was reduced to 1.08 with standard deviation 0.092 with C.F. 1.08 ±
0.554.
Internal rotation has mean 1.86 which was reduced to 0.89 with standard deviation 0.523 with
C.F. 0.89 ± 0.523.
External rotation has mean 1.42 which was reduced to 0.86 with standard deviation 0.237 with
C.F. 0.86 ± 0.487
Table No 15: Tests of Normality
Tests of Normality
Parameters Time
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Bahushol
BT 0.354 36 0.000 0.742 36 0.000
AT 0.414 36 0.000 0.662 36 0.000
Bahupraspanditahar
BT_A 0.355 36 0.000 0.731 36 0.000
AT_A 0.367 36 0.000 0.730 36 0.000
Abduction
BT_B 0.424 36 0.000 0.596 36 0.000
AT_B 0.367 36 0.000 0.730 36 0.000
Extension
BT_C 0.353 36 0.000 0.732 36 0.000
AT_C 0.257 36 0.000 0.805 36 0.000
Flexion
BT_D 0.395 36 0.000 0.696 36 0.000
AT_D 0.365 36 0.000 0.727 36 0.000
Internal Rotation
BT_E 0.462 36 0.000 0.569 36 0.000
AT_E 0.390 36 0.000 0.696 36 0.000
External Rotation
BT_F 0.381 36 0.000 0.627 36 0.000
AT_F 0.418 36 0.000 0.656 36 0.000
a. Lilliefors Significance Correction
(Source: Primary Data)
Interpretation:
From the above table , researcher observe that by both the test Kolmogorov Smimnov test ,we
can see that all the values are gtreater than 0.05 which was totally significant to prove the
normality of the data .
17. Table No 16: Paired t Test-A
H0:There is not a significant mean difference after the treatment
H1:There is a significant mean difference after the treatment
Here researcher used the paired t test to observe the difference before and after treatment at
sample size of 36 with considering the level; of significance at 5% with 95% confidence interval
of data.
Paired Samples Statistics
Parameter Scale Mean N
Std.
Deviation
Std. Error
Mean
Bahushol Pair 1
BT 2.19 36 0.577 0.096
AT 1.25 36 0.500 0.083
Bahupraspanditahar Pair 2
BT_A 1.72 36 0.566 0.094
AT_A 0.83 36 0.561 0.093
Abduction Pair 3
BT_B 2.33 36 0.478 0.080
AT_B 1.17 36 0.561 0.093
Extension Pair 4
BT_C 1.97 36 0.560 0.093
AT_C 1.11 36 0.708 0.118
Flexion Pair 5
BT_D 2.19 36 0.525 0.087
AT_D 1.08 36 0.554 0.092
Internal Rotation Pair 6
BT_E 1.86 36 0.424 0.071
AT_E 0.89 36 0.523 0.087
External Rotation Pair 7
BT_F 1.42 36 0.500 0.083
AT_F 0.86 36 0.487 0.081
(Source: Primary Data)
Table No 16: Paired t Test-B
Paired Samples Correlations
Parameter Scale N Correlation Sig.
Bahushol Pair 1 BT & AT 36 0.718 0.000
Bahupraspanditahar Pair 2 BT_A & AT_A 36 0.570 0.000
Abduction Pair 3 BT_B & AT_B 36 0.426 0.010
Extension Pair 4 BT_C & AT_C 36 0.224 0.189
Flexion Pair 5 BT_D & AT_D 36 0.532 0.001
Internal Rotation Pair 6 BT_E & AT_E 36 0.444 0.007
External Rotation Pair 7 BT_F & AT_F 36 0.479 0.003
(Source: Primary Data)
18. Table No 16: Paired t Test-C
Paired Samples Test
Parameter Scale
Paired Differences
Mean
Std.
Deviation
Std.
Error
Mean
95%
Confidence
Interval of the
Difference
t df
Sig. (2-
tailed)
Lower Upper
Bahushol
Pair
1
BT -
AT
0.944 0.410 0.068 0.806 1.083 13.815 35 0.000
Bahupraspanditahar
Pair
2
BT_A
-
AT_A
0.889 0.523 0.087 0.712 1.066 10.207 35 0.000
Abduction
Pair
3
BT_B
-
AT_B
1.167 0.561 0.093 0.977 1.356 12.486 35 0.000
Extension
Pair
4
BT_C
-
AT_C
0.861 0.798 0.133 0.591 1.131 6.472 35 0.000
Flexion
Pair
5
BT_D
-
AT_D
1.111 0.523 0.087 0.934 1.288 12.759 35 0.000
Internal Rotation
Pair
6
BT_E
-
AT_E
0.972 0.506 0.084 0.801 1.144 11.521 35 0.000
External Rotation
Pair
7
BT_F
-
AT_F
0.556 0.504 0.084 0.385 0.726 6.614 35 0.000
(Source: Primary Data)
Interpretation:
From the above table , researcher observe that Bahushool has mean difference 94.4 % ,
Bahupraspanditahar has mean difference 88.9 % , abduction has mean difference 1.167 % ,
extension has mean difference 86 % , flexion has mean difference 1.111% , internal rotation has
mean difference 97% while external rotation has 55.6% mean difference .
From above table no 16 ,it was seen that t statistics values are more significant and all
significance p values are less than 0.05 so we reject the null hypothesis and conclude that there is
a significant difference after the treatment at 5 % level of significance and 36 degree of freedom.
19. Table No 17: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in Bahushool.
H1: There is a significant mean difference after the treatment with internal consistency in
Bahushool.
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares
d.f.
Mean
Square
F Sig.
Bahushool
BT
Between
Groups
0.311 6 0.052
0.133 0.991
Within
Groups
11.328 29 0.391
Total 11.639 35
AT
Between
Groups
0.883 6 0.147
0.543 0.771
Within
Groups
7.867 29 0.271
Total 8.750 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are greater
than 0.05 so we accept the null hypothesis and conclude that there is significant difference in
internal consistency at 5 % level of significance.
20. Table No 18: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in Bahupraspanditahar.
H1: There is a significant mean difference after the treatment with internal consistency in
Bahupraspanditahar.
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares
d.f. Mean Square F Sig.
Bahupraspanditahar
BT_A
Between
Groups
1.922 6 0.320
0.999 0.445
Within
Groups
9.300 29 0.321
Total 11.222 35
AT_A
Between
Groups
3.172 6 0.529
1.959 0.105
Within
Groups
7.828 29 0.270
Total 11.000 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are less than
0.05 so we reject the null hypothesis and conclude that there is not a significant difference in
internal consistency at 5 % level of significance.
21. Table No 19: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in Abduction.
H1: There is a significant mean difference after the treatment with internal consistency in
Abduction.
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares d.f.
Mean
Square F Sig.
Abduction
BT_B
Between
Groups
1.439 6 0.240
1.060 0.409
Within
Groups
6.561 29 0.226
Total 8.000 35
AT_B
Between
Groups
2.222 6 0.370
1.224 0.323
Within
Groups
8.778 29 0.303
Total 11.000 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are less than
0.05 so we reject the null hypothesis and conclude that there is not a significant difference in
internal consistency at 5 % level of significance.
22. Table No 20: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in Extension.
H1: There is a significant mean difference after the treatment with internal consistency in
Extension.
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares
df
Mean
Square
F Sig.
Extension
BT_C
Between
Groups
1.311 6 0.219
0.656 0.685
Within
Groups
9.661 29 0.333
Total 10.972 35
AT_C
Between
Groups
2.744 6 0.457
0.896 0.511
Within
Groups
14.811 29 0.511
Total 17.556 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are greater
than 0.05 so we reject the null hypothesis and conclude that there is a significant difference in
internal consistency at 5 % level of significance.
23. Table No 21: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in flexion.
H1: There is a significant mean difference after the treatment with internal consistency in
flexion.
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares
df
Mean
Square
F Sig.
Flexion
BT_D
Between
Groups
0.589 6 0.098
0.315 0.924
Within
Groups
9.050 29 0.312
Total 9.639 35
AT_D
Between
Groups
1.256 6 0.209
0.639 0.698
Within
Groups
9.494 29 0.327
Total 10.750 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are greater
than 0.05 so we reject the null hypothesis and conclude that there is a significant difference in
internal consistency at 5 % level of significance.
24. Table No 22: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in internal rotation .
H1: There is a significant mean difference after the treatment with internal consistency in
internal rotation .
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares
df
Mean
Square
F Sig.
Internal
Rotation
BT_E
Between
Groups
0.994 6 0.166
0.905 0.505
Within
Groups
5.311 29 0.183
Total 6.306 35
AT_E
Between
Groups
1.644 6 0.274
1.005 0.441
Within
Groups
7.911 29 0.273
Total 9.556 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are greater
than 0.05 so we reject the null hypothesis and conclude that there is a significant difference in
internal consistency at 5 % level of significance.
25. Table No 23: ANALYSIS OF VARIANCE
H0:There is not a significant mean difference after the treatment with internal consistency
in external rotation.
H1: There is a significant mean difference after the treatment with internal consistency in
external rotation .
Here researcher used the ANOVA test to observe the difference & internal consistency of data at
sample size of 36 with considering the level of significance at 5% with 95% confidence interval
of data.
ANOVA
Parameter Scale
Sum of
Squares
df
Mean
Square
F Sig.
External
Rotation
BT_F
Between
Groups
1.439 6 0.240
0.951 0.475
Within
Groups
7.311 29 0.252
Total 8.750 35
AT_F
Between
Groups
2.561 6 0.427
2.155 0.077
Within
Groups
5.744 29 0.198
Total 8.306 35
(Source: Primary Data)
Interpretation:
From the above table, it was seen that F values are significantly higher but p values are less than
0.05 so we accept the null hypothesis and conclude that there is not a significant difference in
internal consistency at 5 % level of significance.
26. Table No 24: Summary Table of Testing of Hypothesis
Testing of Hypothesis
Sr.no. Hypothesis
Test
used
Level of
Significance
Test
Statistics
P value Decision
1
There is not a significant
mean difference after the
treatment
Paired t
test
5%
Table no
16
> 0.05
Reject the
Hypothesis
2
There is not a significant
mean difference after the
treatment with internal
consistency in Bahushool.
ANOVA 5%
Table no
17
< 0.05
Accept the
Hypothesis
3
There is not a significant
mean difference after the
treatment with internal
consistency in
Bahupraspanditahar.
ANOVA 5%
Table no
18
> 0.05
Reject the
Hypothesis
4
There is not a significant
mean difference after the
treatment with internal
consistency in Abduction.
ANOVA 5%
Table no
19
> 0.05
Reject the
Hypothesis
5
There is not a significant
mean difference after the
treatment with internal
consistency in flexion.
ANOVA 5%
Table no
20
> 0.05
Reject the
Hypothesis
6
There is not a significant
mean difference after the
treatment with internal
consistency in internal
rotation .
ANOVA 5%
Table no
21
> 0.05
Reject the
Hypothesis
7
There is not a significant
mean difference after the
treatment with internal
consistency in external
rotation.
ANOVA 5%
Table no
22
> 0.05
Reject the
Hypothesis
(Source: Primary Data)