This document provides an overview of statistics, including:
- Statistics is concerned with analyzing data to uncover patterns and make inferences. It is used across many fields like business, economics, and medicine.
- There are two main types of data: qualitative and quantitative. Quantitative data can be discrete or continuous.
- Descriptive statistics describe and summarize data, while inferential statistics are used to estimate parameters and generalize from a sample to a population.
- Common measures of central tendency include the mean, median, and mode, while measures of dispersion include the range, average deviation, and standard deviation.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
The process of data cleaning involves the process of transformation of data from a raw format to a format that is compatible with your and use case.
Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Fundamentals Of Statistics-Definition of statistics,Descriptive and Inferential Statistics,Major Types of Descriptive Statistics,Statistical data analysis
In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
Basics of Educational Statistics (Inferential statistics)HennaAnsari
Inferential Statistics
6.1 Introduction to Inferential Statistics
6.1.1 Areas of Inferential Statistics
6.2.2 Logic of Inferential Statistics
6.2 Importance of Inferential Statistics in Research
The process of data cleaning involves the process of transformation of data from a raw format to a format that is compatible with your and use case.
Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
Understanding data type is an important concept in statistics, when you are designing an experiment, you want to know what type of data you are dealing with, that will decide what type of statistical analysis, visualizations and prediction algorithms could be used.
#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence
This presentation discusses the following topics:
Hypothesis Test
Potential Outcomes in Hypothesis Testing
Significance level
P-value
Sampling Errors
Type I Error
What causes Type I errors?
What causes Type II errors?
4 possible outcomes
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
1. Introduction to statistics in curriculum and Instruction
1 The definition of statistics and other related terms
1.2 Descriptive statistics
3 Inferential statistics
1.4 Function and significance of statistics in education
5 Types and levels of measurement scale
2. Introduction to SPSS Software
3. Frequency Distribution
4. Normal Curve and Standard Score
5. Confidence Interval for the Mean, Proportions, and Variances
6. Hypothesis Testing with One and two Sample
7. Two-way Analysis of Variance
8. Correlation and Simple Linear Regression
9. CHI-SQUARE
Understanding data type is an important concept in statistics, when you are designing an experiment, you want to know what type of data you are dealing with, that will decide what type of statistical analysis, visualizations and prediction algorithms could be used.
#data #data types #ai #machine learning #statistics #data science #data analytics #artificial intelligence
This presentation discusses the following topics:
Hypothesis Test
Potential Outcomes in Hypothesis Testing
Significance level
P-value
Sampling Errors
Type I Error
What causes Type I errors?
What causes Type II errors?
4 possible outcomes
Hypothesis Testing is important part of research, based on hypothesis testing we can check the truth of presumes hypothesis (Research Statement or Research Methodology )
1. Introduction to statistics in curriculum and Instruction
1 The definition of statistics and other related terms
1.2 Descriptive statistics
3 Inferential statistics
1.4 Function and significance of statistics in education
5 Types and levels of measurement scale
2. Introduction to SPSS Software
3. Frequency Distribution
4. Normal Curve and Standard Score
5. Confidence Interval for the Mean, Proportions, and Variances
6. Hypothesis Testing with One and two Sample
7. Two-way Analysis of Variance
8. Correlation and Simple Linear Regression
9. CHI-SQUARE
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Chapter 1: Introduction to Statistics
Section 1.2: Types of Data, Key Concept
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
Assignment 2: RA: Annotated Bibliography
In your final paper for this course, you will need to write a Methods section that is about 3–4 pages long where you will assess and evaluate the methods and analysis of your proposed research.
In preparation for this particular section, answer the following questions thoroughly and provide justification/support. The more complete and detailed your answers for these questions, the better prepared you are to successfully write your final paper:
· What is the problem being addressed by your research study?
· State the refined research question and hypothesis (null and alternative).
· What are your independent and dependent variables? What are their operational definitions?
· Who will be included in your sample (i.e., inclusion and exclusion characteristics)?
· How many participants will you have in your sample?
· How will you recruit your sample?
· Identify the type of measurement instrument to be used to collect the raw numeric data to be statistically analyzed and the type of measurement data the instrument produces.
· What issues will you cover in the informed consent?
· If there is potential risk or harm, how will you ensure the safety of all participants?
· Name any possible threats to validity and steps that can be taken to minimize these threats.
· What type of parametric or nonparametric inferential statistical process (correlation, difference, or effect) will you use in your proposed research? Why is this statistical test the best fit?
· State an acceptable behavioral research alpha level you would use to fail to accept or fail to reject the stated null hypothesis and explain your choice.
This paper may be written in question-and-answer format rather than a flowing paper. Write your response in a 3- to 4-page Microsoft Word document.
All written assignments and responses should follow APA rules for attributing sources.
Submission Details:
· By the due date assigned, save your document as M4_A2_Lastname_Firstname.doc and submit it to the Submissions Area .
Assignment 2 Grading Criteria
Maximum Points
Stated the problem being addressed.
8
Stated the refined research question and hypothesis (null and alternative).
6
Stated the independent and dependent variables and provided the operational definitions.
12
Discussed sample characteristics and size.
8
Discussed a sample recruitment strategy.
6
Identified the type of measurement instrument to be used and the type of measurement data the instrument produces.
8
Discussed the informed consent and potential risk and protection factors.
12
Named the possible threats to validity and steps that can be taken to minimize these threats.
12
Discussed the type of parametric or nonparametric inferential statistical process that will be used and why it is a best fit.
8
Stated an acceptable behavioral research alpha level for analyzing the data.
4
Wrote in a clear, concise, and organized manner; demonstrated ethical scholarship in accurate representation and attrib.
CHAPTER 1 SEMESTER V PREVENTIVE-PEDIATRICS.pdfSachin Sharma
This content provides an overview of preventive pediatrics. It defines preventive pediatrics as preventing disease and promoting children's physical, mental, and social well-being to achieve positive health. It discusses antenatal, postnatal, and social preventive pediatrics. It also covers various child health programs like immunization, breastfeeding, ICDS, and the roles of organizations like WHO, UNICEF, and nurses in preventive pediatrics.
Navigating Challenges: Mental Health, Legislation, and the Prison System in B...Guillermo Rivera
This conference will delve into the intricate intersections between mental health, legal frameworks, and the prison system in Bolivia. It aims to provide a comprehensive overview of the current challenges faced by mental health professionals working within the legislative and correctional landscapes. Topics of discussion will include the prevalence and impact of mental health issues among the incarcerated population, the effectiveness of existing mental health policies and legislation, and potential reforms to enhance the mental health support system within prisons.
India Clinical Trials Market: Industry Size and Growth Trends [2030] Analyzed...Kumar Satyam
According to TechSci Research report, "India Clinical Trials Market- By Region, Competition, Forecast & Opportunities, 2030F," the India Clinical Trials Market was valued at USD 2.05 billion in 2024 and is projected to grow at a compound annual growth rate (CAGR) of 8.64% through 2030. The market is driven by a variety of factors, making India an attractive destination for pharmaceutical companies and researchers. India's vast and diverse patient population, cost-effective operational environment, and a large pool of skilled medical professionals contribute significantly to the market's growth. Additionally, increasing government support in streamlining regulations and the growing prevalence of lifestyle diseases further propel the clinical trials market.
Growing Prevalence of Lifestyle Diseases
The rising incidence of lifestyle diseases such as diabetes, cardiovascular diseases, and cancer is a major trend driving the clinical trials market in India. These conditions necessitate the development and testing of new treatment methods, creating a robust demand for clinical trials. The increasing burden of these diseases highlights the need for innovative therapies and underscores the importance of India as a key player in global clinical research.
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Antibiotic Stewardship by Anushri Srivastava.pptxAnushriSrivastav
Stewardship is the act of taking good care of something.
Antimicrobial stewardship is a coordinated program that promotes the appropriate use of antimicrobials (including antibiotics), improves patient outcomes, reduces microbial resistance, and decreases the spread of infections caused by multidrug-resistant organisms.
WHO launched the Global Antimicrobial Resistance and Use Surveillance System (GLASS) in 2015 to fill knowledge gaps and inform strategies at all levels.
ACCORDING TO apic.org,
Antimicrobial stewardship is a coordinated program that promotes the appropriate use of antimicrobials (including antibiotics), improves patient outcomes, reduces microbial resistance, and decreases the spread of infections caused by multidrug-resistant organisms.
ACCORDING TO pewtrusts.org,
Antibiotic stewardship refers to efforts in doctors’ offices, hospitals, long term care facilities, and other health care settings to ensure that antibiotics are used only when necessary and appropriate
According to WHO,
Antimicrobial stewardship is a systematic approach to educate and support health care professionals to follow evidence-based guidelines for prescribing and administering antimicrobials
In 1996, John McGowan and Dale Gerding first applied the term antimicrobial stewardship, where they suggested a causal association between antimicrobial agent use and resistance. They also focused on the urgency of large-scale controlled trials of antimicrobial-use regulation employing sophisticated epidemiologic methods, molecular typing, and precise resistance mechanism analysis.
Antimicrobial Stewardship(AMS) refers to the optimal selection, dosing, and duration of antimicrobial treatment resulting in the best clinical outcome with minimal side effects to the patients and minimal impact on subsequent resistance.
According to the 2019 report, in the US, more than 2.8 million antibiotic-resistant infections occur each year, and more than 35000 people die. In addition to this, it also mentioned that 223,900 cases of Clostridoides difficile occurred in 2017, of which 12800 people died. The report did not include viruses or parasites
VISION
Being proactive
Supporting optimal animal and human health
Exploring ways to reduce overall use of antimicrobials
Using the drugs that prevent and treat disease by killing microscopic organisms in a responsible way
GOAL
to prevent the generation and spread of antimicrobial resistance (AMR). Doing so will preserve the effectiveness of these drugs in animals and humans for years to come.
being to preserve human and animal health and the effectiveness of antimicrobial medications.
to implement a multidisciplinary approach in assembling a stewardship team to include an infectious disease physician, a clinical pharmacist with infectious diseases training, infection preventionist, and a close collaboration with the staff in the clinical microbiology laboratory
to prevent antimicrobial overuse, misuse and abuse.
to minimize the developme
CHAPTER 1 SEMESTER V - ROLE OF PEADIATRIC NURSE.pdfSachin Sharma
Pediatric nurses play a vital role in the health and well-being of children. Their responsibilities are wide-ranging, and their objectives can be categorized into several key areas:
1. Direct Patient Care:
Objective: Provide comprehensive and compassionate care to infants, children, and adolescents in various healthcare settings (hospitals, clinics, etc.).
This includes tasks like:
Monitoring vital signs and physical condition.
Administering medications and treatments.
Performing procedures as directed by doctors.
Assisting with daily living activities (bathing, feeding).
Providing emotional support and pain management.
2. Health Promotion and Education:
Objective: Promote healthy behaviors and educate children, families, and communities about preventive healthcare.
This includes tasks like:
Administering vaccinations.
Providing education on nutrition, hygiene, and development.
Offering breastfeeding and childbirth support.
Counseling families on safety and injury prevention.
3. Collaboration and Advocacy:
Objective: Collaborate effectively with doctors, social workers, therapists, and other healthcare professionals to ensure coordinated care for children.
Objective: Advocate for the rights and best interests of their patients, especially when children cannot speak for themselves.
This includes tasks like:
Communicating effectively with healthcare teams.
Identifying and addressing potential risks to child welfare.
Educating families about their child's condition and treatment options.
4. Professional Development and Research:
Objective: Stay up-to-date on the latest advancements in pediatric healthcare through continuing education and research.
Objective: Contribute to improving the quality of care for children by participating in research initiatives.
This includes tasks like:
Attending workshops and conferences on pediatric nursing.
Participating in clinical trials related to child health.
Implementing evidence-based practices into their daily routines.
By fulfilling these objectives, pediatric nurses play a crucial role in ensuring the optimal health and well-being of children throughout all stages of their development.
Leading the Way in Nephrology: Dr. David Greene's Work with Stem Cells for Ki...Dr. David Greene Arizona
As we watch Dr. Greene's continued efforts and research in Arizona, it's clear that stem cell therapy holds a promising key to unlocking new doors in the treatment of kidney disease. With each study and trial, we step closer to a world where kidney disease is no longer a life sentence but a treatable condition, thanks to pioneers like Dr. David Greene.
Telehealth Psychology Building Trust with Clients.pptxThe Harvest Clinic
Telehealth psychology is a digital approach that offers psychological services and mental health care to clients remotely, using technologies like video conferencing, phone calls, text messaging, and mobile apps for communication.
2. What is Statistics?
“Statistics is concerned with the inferential process, in particular with planning and
analysis of experiments or surveys, with the nature of observational errors and sources of
variability that obscure underlying patters, and with efficient summarizing of set of data”
= Kruskal
Why should we use statistics?
Statistical methods are required to ensure that data are interpreted correctly and the
apparent relationship are meaningful and not simply chance occurrence.
Statistics in Different Field
1. Business
2. Economics
3. Banking
4. Accounts and Auditing etc.,
Every day example…
1. Weather forecast
2. Emergency preparedness
3. Predicting diseases
4. Medical study
5. Political campaigns etc.,
4. • Qualitative Data
Qualitative data can be arranged into categories that are non numerical. These
categories can be physical traits, gender, colors or anything that does not have a number
associated to it. Qualitative data is sometimes referred to as categorical data
•Examples:
•Hair color (black, brown, blonde, white, grey, mahogany)
•Make of car (Dodge, Honda, Ford, Toyota)
•Gender (male, female)
•Place of birth (Riyadh, Jeddah, Yanbu)
5. • Quantitative Data
Quantitative data are measures of values or counts and are
expressed in numeric variables.
Examples:
For each orange tree, the number of oranges is measured
For a particular day, the number of cars entering a college
campus is measured
Time until a light bulb burns out
Etc.,
7. Nominal Scale:
This scale is the crudest among all measurement scales but is also the simplest scale.
In this scale the different scores on a measurement simply indicate different
categories.
The nominal scale is often referred to as a categorical scale. The assigned numbers
have no arithmetic properties and act only as labels. The only statistical operation that
can be performed on nominal scales is a frequency count. We cannot determine an
average except mode.
Examples:
Gender (1= male, 0=female)
ZIP code (7000=Philippines, …)
Plate numbers of vehicles (JK3429, MC001, …)
Course (Biology, Mathematics, History, …)
Race (Asian, American, …)
Eye color (Brown, Blue, …)
8. Ordinal Scale:
It involves the ranking of items along the continuum of the characteristic being scales.
In this scale, the items are classified according to whether they have more or less of
characteristic.
The main characteristic of the ordinal scale is that the categories have a logical or
ordered relationship. This type of scale permits the measurement of degrees of
difference, (i.e. 'more’ or ‘less’) but not the specific amount of differences (i.e. how
much ‘more’ or ‘less’).
Examples:
Ranks in a race (first, second, third, …)
Sizes of shirts (small, medium, large, …)
Order of birth (first child, second child , third child , …)
Socio-economic status (lower, middle, upper, …)
Difficulty level of a test (easy, average, difficult, …)
Degree of agreement (SD, D, A, SA)
9. Interval scale
Interval scale is a scale in which the numbers are used to rank attributes such that
numerically equal distance on the scale represent equal distance in the characteristic
being measured. An interval scale contains all the information of an ordinal scale, but
it also one allows to compare the difference/distance between attributes. Interval
scales may be either in numeric or semantic formats.
Examples:
Temperature (in oF or oC)
IQ Scores
10. Ratio scale
The highest scale, it allows the researcher to identify or classify objects, and compare
intervals or differences. It is also meaningful to compute ratios of scale values.
Is a possesses all the properties of the nomincal, ordinal and interval scale and in
addition an absolute zero point.
It is also meaningful to compute ratios of scale values. In the marketing , sales, costs,
market share and number of customers are available measure on ratio scale.
Examples:
I. Height (165cm, 154cm, 144cm, …)
II. Reaction time (20sec, 43sec, 37sec, …)
III. Number of siblings (2, 5, 8, …)
IV. Hours spent on studying for an exam (0, 2, 3, …)
12. Discrete and Continuous Data
Numerical data could be either discrete or continuous.
Continuous data can take any numerical value (within a range);
For example, weight, height, etc.,
There can be an infinite number of possible values in continuous
data.
Discrete data can take only certain values by a finite ;jumps;, i., it
‘jumps’ from one value to another but does not take any
intermediate value between them (For example, umber of
students in the class)
13. Example for Discrete and Continuous Data
A good example to distinguish discrete data from continuous data
is digital and analogue meter or clock were digital is discrete and
analog is continuous.
15. Area of Statistics
Descriptive statistical limits generalization to
the particular group of individuals observed.
That is:
1. No conclusions are extended beyond
this group
2. Any similarity to those outside the
group cannot be assumed.
3. The data describe one group and that
group only.
Example: Assessment findings, findings a
much simpler action research.
Inferential analysis selects a small group out
of larger group an the findings are applied to
the larger group. It is used to estimate a
parameter, the corresponding value in the
population from the which the sample is
selected.
It is necessary to carefully select the sample
or the inferences may not apply to the
population.
18. Measures of Central Tendency
Mean Median Mode
Definition The Arithmetic Average
The middle score in a
distribution of scores
organized from highest or
lowest or lowest to
highest
The score occurring with
greatest frequency
Use With
Interval and Ratio Ordinal, interval and Ratio
data
Nominal, Ordinal, Interval
or ratio data
Caution
Not for use with
distributions with a few
extreme scores.
Not a reliable measure of
central tendency
19. Measures of Dispersion
Range Ave.Deviation Std.Deviation
Definition
The difference between the
lowest and highest scores
in the distribution.
The average distance of all of the
scores from the mean of the
distribution
The square root of the
average squared
deviation from the mean
of a distribution
Use With
Primarily interval and ratio
data, but can be used with
any type of data
Only interval and ratio data
Only interval and ratio
data
Caution
A simple measure that does
not use all scores in the
distribution in its
calculation.
A more sophisticated measure in
which all scores are used, but which
may not weight extreme scores
adequately.
The most sophisticated
and most frequently
used measure of
variation.