Statistical parameter estimation (CADDS)
•
1 like•67 views
Inferential statistics are used to determine the likelihood that a conclusion, based on the analysis of the data from a sample, is true and represents the population studied • The two common forms of statistical inference are: 1. Estimation 2. Null hypothesis tests of significance (NHTS) •Estimation theory is a branch of statistics based on measured/empirical data that has a random component •In statistics, estimation refers to the process by which one makes inferences about a population, based on information obtained from a sample •Estimation of a population parameter is to obtain a guess or an estimate of unknown value of the parameter •The estimation of the parameters of a statistical model is one of the fundamental issue in statistics •Choosing an appropriate estimator ,that is ‘best’ in one or another respect, is an important task OBJECTIVES- Describe the characteristics of the normal distribution in statistical terms • Explain the concept of a confidence interval and how it relates to an estimated parameter A point estimate of a population parameteris a single value of a statistic An interval estimate is defined by two numbers, between which a population parameterissaid to lie. For example, the sample mean x is a point estimateof the populationmean μ For example, a < x < b is an interval estimateof the populationmean p. Similarly, the sample proportion p is a point estimate of the population proportion P. It indicates that the population mean is greaterthan a but less than b
Report
Share
Report
Share
Download to read offline
Recommended
PPT on Sample Size, Importance of Sample Size,
This document discusses factors related to determining sample size for research studies. It defines key terms like sample size, population and importance of sample size. The selection of sample size involves planning the study, specifying parameters, choosing an effect size, and computing the sample size based on those factors. Sample size is influenced by expected effect size, study power, heterogeneity, error risk, and other variables. Dropouts from the sample during a study also impact sample size calculations. Proper determination of sample size is important for obtaining meaningful results and conducting ethical research.
Introduction to Statistics
This document provides an overview of statistics presented by five students. It defines statistics as the practice of collecting and analyzing numerical data. Descriptive statistics summarize data through parameters like the mean, while inferential statistics interpret descriptive statistics to draw conclusions. The document discusses examples of statistics, different types of charts and graphs, descriptive versus inferential statistics, and the importance and applications of statistics in fields like business, economics, and social sciences. It also covers topics like sampling methods, characteristics of sampling, probability versus non-probability sampling, and differences between the two.
POPULATION.pptx
This document provides an introduction to research methodology concepts including population, sample, sampling methods, hypothesis testing, and types of errors. It defines key terms like population, sample, probability and non-probability sampling, null and alternative hypotheses. It explains probability sampling methods like simple random sampling, stratified sampling and cluster sampling. It also summarizes non-probability methods like convenience and purposive sampling. The document concludes by describing type I and type II errors and their relationship to hypothesis testing.
Meta analysis
1) Meta-analysis is a statistical technique that combines the results of multiple studies on a topic and produces a single estimate of the overall effect. It aims to increase power by pooling data.
2) The first meta-analysis was conducted in 1904, and the term was coined in 1976. Meta-analysis is now often called a "systematic review."
3) Meta-analysis can help clinicians and policymakers integrate research findings and determine if relationships are consistent across studies. It increases precision and statistical power compared to individual studies.
Qualitative and Quantitative Research Approaches.pptx
Qualitative and quantitative research approaches are the two main paradigms used in nursing research. Qualitative research explores phenomena through interviews and observations to understand experiences, while quantitative research tests hypotheses through statistical analysis of numerical data to quantify relationships. Mixed methods research incorporates both approaches. The choice of approach depends on the research question and objectives. Both help nurses determine best practices for patient care.
statistical analysis
This document provides an overview of statistical analysis for nursing research. It defines key terms like statistics, data analysis, and population. It outlines the specific objectives of understanding statistical analysis and applying it to nursing research skillfully. It also describes the various types of statistical analysis including descriptive statistics, inferential statistics, parametric and nonparametric tests. Finally, it discusses the steps in statistical analysis, available computer programs, uses of statistical analysis in different fields including nursing, and advantages and disadvantages of statistical analysis.
Statistical
1. The document discusses the assumptions of parametric and non-parametric statistics when analyzing experimental data.
2. For parametric tests, the key assumptions are random selection of subjects, normally distributed data, and equal variances between groups.
3. Non-parametric tests only require random selection and equal variances if measuring on an ordinal scale or higher.
PPT Sampling editggggggggggggghhhhhh 1.pptx
This document discusses sampling methods for quantitative and qualitative research studies. It defines key sampling terms and outlines different sampling designs for quantitative and qualitative research. For quantitative studies, it describes probability and nonprobability sampling methods as well as considerations for sample size. For qualitative research, it discusses types of sampling approaches and notes that sample size is typically smaller and determined by data saturation. The document provides guidance for critiquing sampling plans in terms of representative sampling and potential biases.
Recommended
PPT on Sample Size, Importance of Sample Size,
This document discusses factors related to determining sample size for research studies. It defines key terms like sample size, population and importance of sample size. The selection of sample size involves planning the study, specifying parameters, choosing an effect size, and computing the sample size based on those factors. Sample size is influenced by expected effect size, study power, heterogeneity, error risk, and other variables. Dropouts from the sample during a study also impact sample size calculations. Proper determination of sample size is important for obtaining meaningful results and conducting ethical research.
Introduction to Statistics
This document provides an overview of statistics presented by five students. It defines statistics as the practice of collecting and analyzing numerical data. Descriptive statistics summarize data through parameters like the mean, while inferential statistics interpret descriptive statistics to draw conclusions. The document discusses examples of statistics, different types of charts and graphs, descriptive versus inferential statistics, and the importance and applications of statistics in fields like business, economics, and social sciences. It also covers topics like sampling methods, characteristics of sampling, probability versus non-probability sampling, and differences between the two.
POPULATION.pptx
This document provides an introduction to research methodology concepts including population, sample, sampling methods, hypothesis testing, and types of errors. It defines key terms like population, sample, probability and non-probability sampling, null and alternative hypotheses. It explains probability sampling methods like simple random sampling, stratified sampling and cluster sampling. It also summarizes non-probability methods like convenience and purposive sampling. The document concludes by describing type I and type II errors and their relationship to hypothesis testing.
Meta analysis
1) Meta-analysis is a statistical technique that combines the results of multiple studies on a topic and produces a single estimate of the overall effect. It aims to increase power by pooling data.
2) The first meta-analysis was conducted in 1904, and the term was coined in 1976. Meta-analysis is now often called a "systematic review."
3) Meta-analysis can help clinicians and policymakers integrate research findings and determine if relationships are consistent across studies. It increases precision and statistical power compared to individual studies.
Qualitative and Quantitative Research Approaches.pptx
Qualitative and quantitative research approaches are the two main paradigms used in nursing research. Qualitative research explores phenomena through interviews and observations to understand experiences, while quantitative research tests hypotheses through statistical analysis of numerical data to quantify relationships. Mixed methods research incorporates both approaches. The choice of approach depends on the research question and objectives. Both help nurses determine best practices for patient care.
statistical analysis
This document provides an overview of statistical analysis for nursing research. It defines key terms like statistics, data analysis, and population. It outlines the specific objectives of understanding statistical analysis and applying it to nursing research skillfully. It also describes the various types of statistical analysis including descriptive statistics, inferential statistics, parametric and nonparametric tests. Finally, it discusses the steps in statistical analysis, available computer programs, uses of statistical analysis in different fields including nursing, and advantages and disadvantages of statistical analysis.
Statistical
1. The document discusses the assumptions of parametric and non-parametric statistics when analyzing experimental data.
2. For parametric tests, the key assumptions are random selection of subjects, normally distributed data, and equal variances between groups.
3. Non-parametric tests only require random selection and equal variances if measuring on an ordinal scale or higher.
PPT Sampling editggggggggggggghhhhhh 1.pptx
This document discusses sampling methods for quantitative and qualitative research studies. It defines key sampling terms and outlines different sampling designs for quantitative and qualitative research. For quantitative studies, it describes probability and nonprobability sampling methods as well as considerations for sample size. For qualitative research, it discusses types of sampling approaches and notes that sample size is typically smaller and determined by data saturation. The document provides guidance for critiquing sampling plans in terms of representative sampling and potential biases.
Meta analysis
This meta-analysis examined the relationship between body mass index (BMI) and incident asthma. It identified 2006 relevant studies and included 12 prospective cohort studies. Inclusion criteria required adult subjects, asthma as the primary outcome, BMI measurement, minimum 1-year follow up of 70%, and BMI data categorized by standard ranges. Random effects models were used to generate summary odds ratios. Results showed overweight individuals had a 38% higher odds of developing asthma compared to normal weight, and obese individuals had 92% higher odds. When stratified by sex, the association was stronger for women. The analysis provided evidence that higher BMI is a risk factor for incident asthma.
Bstat01 introduction
Biostatistics is the application of statistical methods to biological and medical fields. It is used in areas like epidemiology, medicine, health sciences, education, and environmental research. Biostatistics analyzes data from samples to make inferences about populations. It is concerned with collecting, organizing, summarizing, and analyzing data to draw conclusions even when only a part of the total data is observed. Data can come from records, surveys, experiments, and reports, and can be measurements or counts of variables like characteristics and properties. Variables can be quantitative and measured or qualitative and sorted into categories. Random variables arise by chance and observations are used to determine their values. Statistical inference allows conclusions about populations based on analyzing samples drawn from those populations.
Systematic Review & Meta Analysis.pptx
Systematic review and meta analysis is considered as the highest body of evidence in research evidence hierarchy. Often misunderstood or skipped over, this powerful tool can broaden our understanding on a specific topic and form basis of practicing evidence based medicine for us.
I presented systematic review and meta analysis as part of my PG seminar and got a good feedback. Now I wanted to share the presentation for a broader audience.
Any kind of constructive feedback is welcome.
Dr. Anik Chakraborty
JR3, Dept. Of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
Basics of Educational Statistics (Inferential statistics)
This document provides information about inferential statistics presented by Dr. Hina Jalal. It defines inferential statistics as using data from a sample to make inferences about the larger population from which the sample was taken. It discusses key areas of inferential statistics like estimating population parameters and testing hypotheses. It also explains the importance of inferential statistics in research for making conclusions from samples, comparing models, and enabling inferences about populations based on sample data. Flow charts are presented for selecting common statistical tests for comparisons, correlations, and regression.
Basic Biostatistics_1.1. Introduction the Course.pdf
This is the first chapter of basic Biostatistics course, which is primarily aimed to fulfill a Masters degree in epidemiology
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
This document provides an overview of research methods and sampling techniques. It discusses the differences between probability and non-probability sampling, and describes common probability sampling methods like simple random sampling. The document also summarizes different approaches to systematic reviews, including traditional systematic reviews primarily used in medicine, mixed methods reviews that incorporate qualitative research, and integrative reviews focused on theory building. Finally, it outlines the typical steps involved in undertaking a systematic review project.
2_Lecture 2_Confidence_Interval_3.pdf
Census
Everyone in population
Eg. All Cambodian residents
Population
is a set of persons (or objects) having a common observable characteristic.
the entire collection of units about which we would like information
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Statistical inference is the process by which we draw conclusions about a population from data collected on a sample.
In medical research
Population
All patients candidate for treatment
Sample
All patients candidate for treatment who volunteer for your study
Infer results from volunteers (sample) to other candidates for the same treatment (population).
We usually obtain information on an appropriate sample of the community and generalize from it to the entire population.
The way the sample is selected, not its size, determines whether we may draw appropriate inferences about a population.
The primary reason for selecting a sample from a population is to draw inferences about that population.
Statistical inference is the process by which we infer
Seminar case control study
This document provides an overview of case control studies in epidemiology. It defines a case control study as a retrospective study that compares cases (people with a disease or condition) to controls (people without the disease or condition) to determine risk factors for the disease. The key steps outlined are: selection of cases and controls from the same study base; matching cases and controls on important characteristics; measuring exposure to suspected risk factors in the same way for both groups; and analyzing the data to compare exposure rates between cases and controls and estimate disease risk associated with exposure. Advantages are that case control studies are efficient for rare diseases, but limitations include potential for selection and recall bias.
Research and Scientific Journal Publication support services | Research pape...
The Publishing process can be a daunting task. At Pubrica, we translate your research writing into a publishable manuscript. We guide you through the entire life cycle of your publication, including identifying the most suitable journal, executing the peer review, manuscript editing, statistical review and provide post-submission support.
Contact us for your Medical Writing & Publication Support Service @ https://pubrica.com/services/publication-support/
Visit us @ https://pubrica.com/
Methods of data collection
There are two types of data: primary and secondary. Primary data is collected directly by the researcher through methods like questionnaires, observations, interviews, and surveys. Secondary data is previously collected data from sources like government publications, journals, and reports.
Data collection methods for primary data include questionnaires, observations made without controlling the situation, interviews between a researcher and participant, and surveys administered through enumerators. Secondary data comes from published sources like government documents and unpublished sources from individuals and organizations.
After collection, data must be processed which includes editing, coding, classification, and tabulation to organize it for analysis. Different types of analysis are then used like descriptive, correlation, multivariate, and inferential analysis. Hypotheses are
RCT CH0.ppt
This document provides an introduction to the course "Design and Analysis of Clinical Trials". It discusses how clinical research uses statistics to investigate medical treatments and assess benefits of therapies. Statistics allow for reasonable inferences from collected data despite variability in patient responses. The course covers fundamental concepts of clinical trial design and analysis including phases of trials, randomization, sample size, treatment allocation, and ethical considerations. It aims to teach students how to generalize trial results to populations and combine empirical evidence with medical theory using statistical methods.
RCT CH0.ppt
This document provides an introduction to the course "Design and Analysis of Clinical Trials". It discusses how clinical research uses statistics to investigate medical treatments and assess benefits of therapies. Statistics allow for reasonable inferences from collected data despite variability in patient responses. The course covers fundamental concepts of clinical trial design and analysis including phases of trials, randomization, sample size, treatment allocation, and ethical considerations. It aims to teach students how to generalize trial results to populations and combine empirical evidence with medical theory using statistical methods.
Sampling Methods.pptx
Prof. Shriram Kargaonkar (Asst. Prof . & HOD Statistics Department) explained the concept of Population and Sample in the very simple way. (YouTube Video Link - https://youtu.be/r0ovqPw7624?t=18
Email- snkargaonkar@mitacsc.ac.in
LIKE SHARE & SUBSCRIBE YT Channel - https://www.youtube.com/channel/UC9QIMhC3OioapZDXgsPvnAw
Meta analysis
Meta-analysis is a statistical technique used to synthesize the results of multiple scientific studies. It provides a high-level overview of the key steps in conducting a meta-analysis, which include: formulating the research question, performing a literature search and selecting studies based on eligibility criteria, extracting relevant data from the studies, using statistical methods like fixed or random effects models to calculate an overall effect, and conducting sensitivity analyses to evaluate the robustness of the results. Meta-analysis allows researchers to obtain a better understanding of how an intervention works by combining results from several studies while accounting for variability between the studies.
methods of randomization.pptx
This document discusses various methods of randomization used in clinical trials. It begins by describing the purpose of randomization as removing bias and achieving comparability between treatment groups. It then explains different randomization methods like simple randomization, random allocation, block randomization, and stratification. Block randomization randomly assigns participants in blocks to ensure equal allocation. Stratification divides participants according to prognostic factors. The document also discusses sample size determination, documentation, and monitoring in clinical trials.
Biostatisitics.pptx
The document provides an overview of biostatistics and its key concepts. It discusses the objectives of biostatistical study as presenting data in a way that allows for valid interpretations. It outlines topics covered in the course including different data types, scales of measurement, data collection techniques, and the use of computers for statistical analysis. References for further learning are also provided.
randomized clinical trials I
This document provides an overview of randomized controlled trials (RCTs), including their definition, characteristics, and critical appraisal. RCTs are prospective studies that randomly assign participants to experimental and control groups. The key characteristics discussed include randomization and allocation concealment to distribute confounding variables equally between groups, blinding to reduce bias, pre-specified outcomes, sample size calculation, and intention-to-treat analysis. Critical appraisal involves assessing the validity, relevance, and risk of bias in RCTs to determine the reliability and generalizability of results.
WHAT-IS-RESEARCH.pptx
Research is the systematic inquiry into a topic using scientific methods to describe, explain, predict, or control observed phenomena. It involves both inductive and deductive reasoning. Inductive methods analyze observations to develop generalizations while deductive methods test hypotheses. There are two main types of research: qualitative, which uses open-ended questions to understand perspectives, and quantitative, which uses measurable variables to establish relationships. Common qualitative methods include interviews and case studies, while quantitative methods include surveys, descriptive studies, and correlational analysis. Accurate research requires identifying trends, tracking frequencies, evaluating findings, addressing gaps, and reviewing results.
BASIC CONCEPTS OF ESTIMATION STATISTICS AND PROBABILITY.pptx
This document discusses basic concepts in estimation, including defining estimation as using a sample to calculate unknown population parameters. It distinguishes between point estimates, which are single values approximating a parameter, and interval estimates, which provide a range of values within which the parameter falls. The document also outlines properties of good estimators such as being unbiased, consistent, and efficient in order to accurately predict population values from samples.
Epidemiological statistics II
This document discusses various types of data used in epidemiological statistics and sampling techniques in epidemiological studies. It describes demographic data, vital statistics data, surveillance data, health status and behavioral data, and other types of data. It also explains different sampling methods like random sampling, multi-stage sampling, systematic sampling, stratification, purposive selection, and paired sampling. Finally, it briefly discusses methods for obtaining data like interviews and measurements and measures for statistical analysis of epidemiological data.
How to Manage Reception Report in Odoo 17
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف أحرف الخلاف للقراء العشرةأعد أحرف الخلاف بالتلوين وصلا سمير بسيوني غفر الله له
More Related Content
Similar to Statistical parameter estimation (CADDS)
Meta analysis
This meta-analysis examined the relationship between body mass index (BMI) and incident asthma. It identified 2006 relevant studies and included 12 prospective cohort studies. Inclusion criteria required adult subjects, asthma as the primary outcome, BMI measurement, minimum 1-year follow up of 70%, and BMI data categorized by standard ranges. Random effects models were used to generate summary odds ratios. Results showed overweight individuals had a 38% higher odds of developing asthma compared to normal weight, and obese individuals had 92% higher odds. When stratified by sex, the association was stronger for women. The analysis provided evidence that higher BMI is a risk factor for incident asthma.
Bstat01 introduction
Biostatistics is the application of statistical methods to biological and medical fields. It is used in areas like epidemiology, medicine, health sciences, education, and environmental research. Biostatistics analyzes data from samples to make inferences about populations. It is concerned with collecting, organizing, summarizing, and analyzing data to draw conclusions even when only a part of the total data is observed. Data can come from records, surveys, experiments, and reports, and can be measurements or counts of variables like characteristics and properties. Variables can be quantitative and measured or qualitative and sorted into categories. Random variables arise by chance and observations are used to determine their values. Statistical inference allows conclusions about populations based on analyzing samples drawn from those populations.
Systematic Review & Meta Analysis.pptx
Systematic review and meta analysis is considered as the highest body of evidence in research evidence hierarchy. Often misunderstood or skipped over, this powerful tool can broaden our understanding on a specific topic and form basis of practicing evidence based medicine for us.
I presented systematic review and meta analysis as part of my PG seminar and got a good feedback. Now I wanted to share the presentation for a broader audience.
Any kind of constructive feedback is welcome.
Dr. Anik Chakraborty
JR3, Dept. Of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
Basics of Educational Statistics (Inferential statistics)
This document provides information about inferential statistics presented by Dr. Hina Jalal. It defines inferential statistics as using data from a sample to make inferences about the larger population from which the sample was taken. It discusses key areas of inferential statistics like estimating population parameters and testing hypotheses. It also explains the importance of inferential statistics in research for making conclusions from samples, comparing models, and enabling inferences about populations based on sample data. Flow charts are presented for selecting common statistical tests for comparisons, correlations, and regression.
Basic Biostatistics_1.1. Introduction the Course.pdf
This is the first chapter of basic Biostatistics course, which is primarily aimed to fulfill a Masters degree in epidemiology
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
This document provides an overview of research methods and sampling techniques. It discusses the differences between probability and non-probability sampling, and describes common probability sampling methods like simple random sampling. The document also summarizes different approaches to systematic reviews, including traditional systematic reviews primarily used in medicine, mixed methods reviews that incorporate qualitative research, and integrative reviews focused on theory building. Finally, it outlines the typical steps involved in undertaking a systematic review project.
2_Lecture 2_Confidence_Interval_3.pdf
Census
Everyone in population
Eg. All Cambodian residents
Population
is a set of persons (or objects) having a common observable characteristic.
the entire collection of units about which we would like information
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Sample
is a representative subject (subgroup) of a population.
the collection of units we actually measure
Example:
If we want to know many persons in a community
have quit smoking or
have health insurance or
plan to vote for a certain candidate,
Statistical inference is the process by which we draw conclusions about a population from data collected on a sample.
In medical research
Population
All patients candidate for treatment
Sample
All patients candidate for treatment who volunteer for your study
Infer results from volunteers (sample) to other candidates for the same treatment (population).
We usually obtain information on an appropriate sample of the community and generalize from it to the entire population.
The way the sample is selected, not its size, determines whether we may draw appropriate inferences about a population.
The primary reason for selecting a sample from a population is to draw inferences about that population.
Statistical inference is the process by which we infer
Seminar case control study
This document provides an overview of case control studies in epidemiology. It defines a case control study as a retrospective study that compares cases (people with a disease or condition) to controls (people without the disease or condition) to determine risk factors for the disease. The key steps outlined are: selection of cases and controls from the same study base; matching cases and controls on important characteristics; measuring exposure to suspected risk factors in the same way for both groups; and analyzing the data to compare exposure rates between cases and controls and estimate disease risk associated with exposure. Advantages are that case control studies are efficient for rare diseases, but limitations include potential for selection and recall bias.
Research and Scientific Journal Publication support services | Research pape...
The Publishing process can be a daunting task. At Pubrica, we translate your research writing into a publishable manuscript. We guide you through the entire life cycle of your publication, including identifying the most suitable journal, executing the peer review, manuscript editing, statistical review and provide post-submission support.
Contact us for your Medical Writing & Publication Support Service @ https://pubrica.com/services/publication-support/
Visit us @ https://pubrica.com/
Methods of data collection
There are two types of data: primary and secondary. Primary data is collected directly by the researcher through methods like questionnaires, observations, interviews, and surveys. Secondary data is previously collected data from sources like government publications, journals, and reports.
Data collection methods for primary data include questionnaires, observations made without controlling the situation, interviews between a researcher and participant, and surveys administered through enumerators. Secondary data comes from published sources like government documents and unpublished sources from individuals and organizations.
After collection, data must be processed which includes editing, coding, classification, and tabulation to organize it for analysis. Different types of analysis are then used like descriptive, correlation, multivariate, and inferential analysis. Hypotheses are
RCT CH0.ppt
This document provides an introduction to the course "Design and Analysis of Clinical Trials". It discusses how clinical research uses statistics to investigate medical treatments and assess benefits of therapies. Statistics allow for reasonable inferences from collected data despite variability in patient responses. The course covers fundamental concepts of clinical trial design and analysis including phases of trials, randomization, sample size, treatment allocation, and ethical considerations. It aims to teach students how to generalize trial results to populations and combine empirical evidence with medical theory using statistical methods.
RCT CH0.ppt
This document provides an introduction to the course "Design and Analysis of Clinical Trials". It discusses how clinical research uses statistics to investigate medical treatments and assess benefits of therapies. Statistics allow for reasonable inferences from collected data despite variability in patient responses. The course covers fundamental concepts of clinical trial design and analysis including phases of trials, randomization, sample size, treatment allocation, and ethical considerations. It aims to teach students how to generalize trial results to populations and combine empirical evidence with medical theory using statistical methods.
Sampling Methods.pptx
Prof. Shriram Kargaonkar (Asst. Prof . & HOD Statistics Department) explained the concept of Population and Sample in the very simple way. (YouTube Video Link - https://youtu.be/r0ovqPw7624?t=18
Email- snkargaonkar@mitacsc.ac.in
LIKE SHARE & SUBSCRIBE YT Channel - https://www.youtube.com/channel/UC9QIMhC3OioapZDXgsPvnAw
Meta analysis
Meta-analysis is a statistical technique used to synthesize the results of multiple scientific studies. It provides a high-level overview of the key steps in conducting a meta-analysis, which include: formulating the research question, performing a literature search and selecting studies based on eligibility criteria, extracting relevant data from the studies, using statistical methods like fixed or random effects models to calculate an overall effect, and conducting sensitivity analyses to evaluate the robustness of the results. Meta-analysis allows researchers to obtain a better understanding of how an intervention works by combining results from several studies while accounting for variability between the studies.
methods of randomization.pptx
This document discusses various methods of randomization used in clinical trials. It begins by describing the purpose of randomization as removing bias and achieving comparability between treatment groups. It then explains different randomization methods like simple randomization, random allocation, block randomization, and stratification. Block randomization randomly assigns participants in blocks to ensure equal allocation. Stratification divides participants according to prognostic factors. The document also discusses sample size determination, documentation, and monitoring in clinical trials.
Biostatisitics.pptx
The document provides an overview of biostatistics and its key concepts. It discusses the objectives of biostatistical study as presenting data in a way that allows for valid interpretations. It outlines topics covered in the course including different data types, scales of measurement, data collection techniques, and the use of computers for statistical analysis. References for further learning are also provided.
randomized clinical trials I
This document provides an overview of randomized controlled trials (RCTs), including their definition, characteristics, and critical appraisal. RCTs are prospective studies that randomly assign participants to experimental and control groups. The key characteristics discussed include randomization and allocation concealment to distribute confounding variables equally between groups, blinding to reduce bias, pre-specified outcomes, sample size calculation, and intention-to-treat analysis. Critical appraisal involves assessing the validity, relevance, and risk of bias in RCTs to determine the reliability and generalizability of results.
WHAT-IS-RESEARCH.pptx
Research is the systematic inquiry into a topic using scientific methods to describe, explain, predict, or control observed phenomena. It involves both inductive and deductive reasoning. Inductive methods analyze observations to develop generalizations while deductive methods test hypotheses. There are two main types of research: qualitative, which uses open-ended questions to understand perspectives, and quantitative, which uses measurable variables to establish relationships. Common qualitative methods include interviews and case studies, while quantitative methods include surveys, descriptive studies, and correlational analysis. Accurate research requires identifying trends, tracking frequencies, evaluating findings, addressing gaps, and reviewing results.
BASIC CONCEPTS OF ESTIMATION STATISTICS AND PROBABILITY.pptx
This document discusses basic concepts in estimation, including defining estimation as using a sample to calculate unknown population parameters. It distinguishes between point estimates, which are single values approximating a parameter, and interval estimates, which provide a range of values within which the parameter falls. The document also outlines properties of good estimators such as being unbiased, consistent, and efficient in order to accurately predict population values from samples.
Epidemiological statistics II
This document discusses various types of data used in epidemiological statistics and sampling techniques in epidemiological studies. It describes demographic data, vital statistics data, surveillance data, health status and behavioral data, and other types of data. It also explains different sampling methods like random sampling, multi-stage sampling, systematic sampling, stratification, purposive selection, and paired sampling. Finally, it briefly discusses methods for obtaining data like interviews and measurements and measures for statistical analysis of epidemiological data.
Similar to Statistical parameter estimation (CADDS) (20)
Basics of Educational Statistics (Inferential statistics)
Basics of Educational Statistics (Inferential statistics)
Basic Biostatistics_1.1. Introduction the Course.pdf
Basic Biostatistics_1.1. Introduction the Course.pdf
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
Research and Scientific Journal Publication support services | Research pape...
Research and Scientific Journal Publication support services | Research pape...
BASIC CONCEPTS OF ESTIMATION STATISTICS AND PROBABILITY.pptx
BASIC CONCEPTS OF ESTIMATION STATISTICS AND PROBABILITY.pptx
Recently uploaded
How to Manage Reception Report in Odoo 17
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف أحرف الخلاف للقراء العشرةأعد أحرف الخلاف بالتلوين وصلا سمير بسيوني غفر الله له
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
How Barcodes Can Be Leveraged Within Odoo 17
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Educational Technology in the Health Sciences
Plenary presentation at the NTTC Inter-university Workshop, 18 June 2024, Manila Prince Hotel.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Bossa N’ Roll Records by Ismael Vazquez.
Bossa N Roll Records presentation by Izzy Vazquez for Music Retail and Distribution class at Full Sail University
Accounting for Restricted Grants When and How To Record Properly
In this webinar, member learned how to stay in compliance with generally accepted accounting principles (GAAP) for restricted grants.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
Wound healing PPT
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
CHUYÊN ĐỀ ÔN TẬP VÀ PHÁT TRIỂN CÂU HỎI TRONG ĐỀ MINH HỌA THI TỐT NGHIỆP THPT ...
CHUYÊN ĐỀ ÔN TẬP VÀ PHÁT TRIỂN CÂU HỎI TRONG ĐỀ MINH HỌA THI TỐT NGHIỆP THPT ...Nguyen Thanh Tu Collection
https://app.box.com/s/qspvswamcohjtbvbbhjad04lg65waylfA Visual Guide to 1 Samuel | A Tale of Two Hearts
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Recently uploaded (20)
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
مصحف القراءات العشر أعد أحرف الخلاف سمير بسيوني.pdf
Juneteenth Freedom Day 2024 David Douglas School District
Juneteenth Freedom Day 2024 David Douglas School District
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
Accounting for Restricted Grants When and How To Record Properly
Accounting for Restricted Grants When and How To Record Properly
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
CHUYÊN ĐỀ ÔN TẬP VÀ PHÁT TRIỂN CÂU HỎI TRONG ĐỀ MINH HỌA THI TỐT NGHIỆP THPT ...
CHUYÊN ĐỀ ÔN TẬP VÀ PHÁT TRIỂN CÂU HỎI TRONG ĐỀ MINH HỌA THI TỐT NGHIỆP THPT ...
Statistical parameter estimation (CADDS)
- 1. School Of Pharmaceutical Sciences C.S.J.M. University, Kanpur
- 2. • Inferential statistics are used to determine the likelihood that a conclusion, based on the analysis of the data from a sample, is true and represents the population studied • The two common forms of statistical inference are: 1. Estimation 2. Null hypothesis tests of significance (NHTS) 2 SCHOOL OF PHARMACEUTICAL SCIENCES, C.S.J.M UNIVERSITY,KANPUR
- 3. ESTIMATION •Estimation theory is a branch of statistics based on measured/empirical data that has a random component •In statistics, estimation refers to the process by which one makes inferences about a population, based on information obtained from a sample •Estimation of a population parameter is to obtain a guess or an estimate of unknown value of the parameter •The estimation of the parameters of a statistical model is one of the fundamental issue in statistics •Choosing an appropriate estimator ,that is ‘best’ in one or another respect, is an important task SCHOOL OF PHARMACEUTICAL SCIENCES, C.S.J.M UNIVERSITY,KANPUR 3
- 4. • OBJECTIVES- Describe the characteristics of the normal distribution in statistical terms • Explain the concept of a confidence interval and how it relates to an estimated parameter 4 SCHOOL OF PHARMACEUTICAL SCIENCES, C.S.J.M UNIVERSITY,KANPUR
- 5. POINT ESTIMATE v/s INTERVAL ESTIMATE POINT ESTIMATE INTERVAL ESTIMATE (CONFIDANCE INTERVAL) A point estimate of a population parameteris a single value of a statistic An interval estimate is defined by two numbers, between which a population parameteris said to lie. For example, the sample mean x is a point estimateof the population mean μ For example, a < x < b is an interval estimateof the population mean p. Similarly, the sample proportion p is a point estimate of the population proportionP. It indicates that the population mean is greater than a but less than b 5 SCHOOL OF PHARMACEUTICAL SCIENCES, C.S.J.M UNIVERSITY,KANPUR
- 6. 6 SCHOOL OF PHARMACEUTICAL SCIENCES, C.S.J.M UNIVERSITY,KANPUR