Regression analysis can be used to analyze the relationship between variables. A scatter plot should first be created to determine if the variables have a linear relationship required for regression analysis. A regression line is fitted to best describe the linear relationship between the variables, with an R-squared value indicating how well it fits the data. Multiple regression allows for analysis of the relationship between a dependent variable and multiple independent variables and their individual contributions to explaining the variance in the dependent variable.
This document discusses correlation and regression analysis. It defines correlation as a numerical measure of the strength and direction of the linear relationship between two variables. Regression analysis determines whether a linear relationship exists between variables and can be used to predict the value of a dependent variable based on the independent variable. Simple regression involves one independent and one dependent variable, while multiple regression uses two or more independent variables to predict a dependent variable. Scatter plots visually depict the relationship between variables.
A presentation for Multiple linear regression.pptvigia41
Multiple linear regression (MLR) is a statistical method used to predict the value of a dependent variable based on the values of two or more independent variables. MLR produces an equation that estimates the best weighted combination of independent variables to predict the dependent variable. MLR can assess the contribution and relative importance of each predictor variable while controlling for the effects of the other predictors. MLR requires that assumptions of independence, normality, homoscedasticity, and linearity are met.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
This document discusses correlation analysis and regression analysis. It begins by defining correlation as a measure of how two variables vary together. A positive correlation means the variables increase or decrease together, while a negative correlation means one variable increases as the other decreases. Regression analysis investigates the relationship between a dependent variable and one or more independent variables. An example is provided to illustrate calculating a correlation coefficient and testing hypotheses about relationships between variables using a regression model. Key terms discussed include the Pearson correlation coefficient, coefficient of determination, t-statistic, and developing a conceptual model for multiple regression analysis.
This was a presentation I gave to my firm's internal CPE in December 2012. It related to correlation and simple regression models and how we can utilize these statistics in both income and market approaches.
This document discusses correlation coefficient and regression analysis. It defines correlation coefficient as representing the relationship between two variables with a straight line and ranging from -1 to 1. Regression analysis predicts changes in a dependent variable from changes in independent variables. The coefficient of determination measures the proportion of variance explained by the regression model. Multiple regression uses two or more explanatory variables to predict an outcome.
This document discusses statistical analysis techniques such as descriptive analysis, reliability testing, correlation, and regression analysis. It provides details on calculating mean, standard deviation, Cronbach's alpha, Pearson's correlation coefficient, and using regression to analyze relationships between variables and test for mediation. Mediation is tested using Baron and Kenny's four step approach to determine if a third variable mediates the relationship between an independent and dependent variable.
Research method ch09 statistical methods 3 estimation npnaranbatn
This document provides an overview of estimation and correlation analysis techniques used in research methods. It defines key terms like correlation, linear regression, and discusses topics like dealing with collinearity between variables. Non-parametric tests that don't assume a particular distribution are also introduced, such as the Wilcoxon test, chi-square test, and Kruskal-Wallis test. Multivariate techniques like discriminant analysis, multivariate ANOVA, and factor analysis are briefly outlined.
This document discusses correlation and regression analysis. It defines correlation as a numerical measure of the strength and direction of the linear relationship between two variables. Regression analysis determines whether a linear relationship exists between variables and can be used to predict the value of a dependent variable based on the independent variable. Simple regression involves one independent and one dependent variable, while multiple regression uses two or more independent variables to predict a dependent variable. Scatter plots visually depict the relationship between variables.
A presentation for Multiple linear regression.pptvigia41
Multiple linear regression (MLR) is a statistical method used to predict the value of a dependent variable based on the values of two or more independent variables. MLR produces an equation that estimates the best weighted combination of independent variables to predict the dependent variable. MLR can assess the contribution and relative importance of each predictor variable while controlling for the effects of the other predictors. MLR requires that assumptions of independence, normality, homoscedasticity, and linearity are met.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
This document discusses correlation analysis and regression analysis. It begins by defining correlation as a measure of how two variables vary together. A positive correlation means the variables increase or decrease together, while a negative correlation means one variable increases as the other decreases. Regression analysis investigates the relationship between a dependent variable and one or more independent variables. An example is provided to illustrate calculating a correlation coefficient and testing hypotheses about relationships between variables using a regression model. Key terms discussed include the Pearson correlation coefficient, coefficient of determination, t-statistic, and developing a conceptual model for multiple regression analysis.
This was a presentation I gave to my firm's internal CPE in December 2012. It related to correlation and simple regression models and how we can utilize these statistics in both income and market approaches.
This document discusses correlation coefficient and regression analysis. It defines correlation coefficient as representing the relationship between two variables with a straight line and ranging from -1 to 1. Regression analysis predicts changes in a dependent variable from changes in independent variables. The coefficient of determination measures the proportion of variance explained by the regression model. Multiple regression uses two or more explanatory variables to predict an outcome.
This document discusses statistical analysis techniques such as descriptive analysis, reliability testing, correlation, and regression analysis. It provides details on calculating mean, standard deviation, Cronbach's alpha, Pearson's correlation coefficient, and using regression to analyze relationships between variables and test for mediation. Mediation is tested using Baron and Kenny's four step approach to determine if a third variable mediates the relationship between an independent and dependent variable.
Research method ch09 statistical methods 3 estimation npnaranbatn
This document provides an overview of estimation and correlation analysis techniques used in research methods. It defines key terms like correlation, linear regression, and discusses topics like dealing with collinearity between variables. Non-parametric tests that don't assume a particular distribution are also introduced, such as the Wilcoxon test, chi-square test, and Kruskal-Wallis test. Multivariate techniques like discriminant analysis, multivariate ANOVA, and factor analysis are briefly outlined.
Correlation describes the relationship between two or more variables. A positive correlation means that as one variable increases, the other also increases, while a negative correlation means that as one variable increases, the other decreases. Correlation is measured numerically using coefficients like the Pearson correlation coefficient r, which ranges from -1 to 1, with values farther from 0 indicating stronger linear relationships and the direction indicating positive or negative correlation. Correlation is used in business and economics to study relationships between variables like price and demand.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
The document discusses various concepts related to time series analysis and correlation. It defines time series as a sequence of data points measured over successive time periods. Time series analysis is used to extract meaningful patterns from temporal data and forecast future values. Correlation analysis examines the relationship between two quantitative variables, and can be positive, negative, linear or non-linear. Regression analysis is used to estimate the value of a dependent variable based on the value of an independent variable. Key components of time series include trends, cyclical variations, seasonal variations, and irregular variations.
Prediction studies attempt to describe predictive relationships between variables. Regression analysis allows prediction of an outcome variable from one or more predictor variables. It is useful for facilitating selection decisions, testing predictive variables, and determining predictive validity. Simple linear regression uses one predictor and criterion variable, while multiple regression uses more than one predictor to predict a criterion variable. [/SUMMARY]
Unit-III Correlation and Regression.pptxAnusuya123
Unit-III describes different types of relationships between variables through correlation and regression analysis. It discusses:
1) Correlation measures the strength and direction of a linear relationship between two variables on a scatter plot. Positive correlation means variables increase together, while negative correlation means one increases as the other decreases.
2) Regression analysis uses independent variables to predict outcomes of a dependent variable. A regression line minimizes the squared errors between predicted and actual values.
3) The correlation coefficient r and coefficient of determination r-squared quantify the strength and direction of linear relationships, with values between -1 and 1. Extreme scores on one measurement tend to regress toward the mean on subsequent measurements.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
This document summarizes a presentation on correlation and regression analysis. It introduces correlation, which measures the strength and direction of association between two variables. It describes Pearson's correlation coefficient and Spearman's correlation coefficient, and when each is appropriate. It then discusses regression, explaining the difference between correlation and regression, and introducing linear regression, logistic regression, and their applications. Examples of running linear and logistic regression in SPSS are provided.
Linear regression is a statistical method used to explain the relationship between variables. The document discusses:
1) An agenda covering regression, diagnostics, differences between linear and logistic regression, assumptions, and interview questions.
2) Details on linear regression including understanding the algorithm, assumptions around linearity, normality, multicollinearity, autocorrelation, and homoscedasticity.
3) How to check if assumptions are violated including residual plots, Q-Q plots, and various statistical tests.
The document provides an in-depth overview of linear regression modeling, assumptions, and how to diagnose potential issues.
This document discusses multiple linear regression analysis performed using SAS. It begins by outlining the assumptions of linear regression, including a linear relationship between variables, normality, no multicollinearity, and homoscedasticity. It then explains that multiple linear regression attempts to model the relationship between multiple explanatory variables and a response variable by fitting a linear equation to observed data. The document goes on to describe the regression analysis process, model selection, interpretation of outputs like R-squared and p-values, and evaluation of diagnostics like autocorrelation. It concludes by listing the predictor variables selected by the stepwise regression model and interpreting their parameter estimates.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
(1) The document provides an introduction to simple linear regression, including definitions of key terms like predictors, target variable, coefficients, and error terms. (2) It presents an example of simple linear regression analysis to predict yield based on temperature. (3) The outputs like regression statistics, line fit plot, and residual plot are interpreted to evaluate the relationship between the variables and check assumptions.
This document discusses the Pearson r correlation coefficient and how to calculate, interpret, and evaluate the significance of relationships between variables. The Pearson r ranges from -1 to 1, with values closer to these extremes indicating a stronger linear relationship and values near 0 a weaker relationship. A correlation of -0.67 represents the strongest relationship among the options given. The correlation coefficient is calculated using the formula presented. An example calculates the correlation between number of hospitals and maternal mortality rate in various cities. Interpretation of the correlation involves evaluating the magnitude, sign, strength of relationship, and significance.
This document discusses the Pearson r correlation coefficient and how to calculate, interpret, and evaluate the significance of relationships between variables. The Pearson r ranges from -1 to 1, with values closer to these extremes indicating a stronger linear relationship and values near 0 a weaker relationship. A correlation of -0.67 represents the strongest relationship among the options given. The correlation coefficient is calculated using the formula presented. An example calculates the correlation between number of hospitals and maternal mortality rate in various cities. Interpretation of the correlation involves evaluating the magnitude, sign, strength of relationship, and coefficient of determination. Statistical significance can be determined by comparing the correlation to critical values from tables.
A presentation on correlation and regression for engineering students studying probability and statistics. The presentation is designed according to syllabus of Institute of Engineering (IOE), Tribhuvan University. But the course content is similar to that of almost all the engineering universities.
Correlation analysis is a statistical technique used to determine the degree of relationship between two quantitative variables. Scatterplots are used to graphically depict the relationship and identify if it is positive, negative, or no correlation. The correlation coefficient measures the strength and direction of correlation, ranging from -1 to 1. A significance test determines if a correlation is likely to have occurred by chance or is statistically significant. Different types of correlation include simple, multiple, partial, and autocorrelation.
Using multiple regression and logistic regression analyses in SPSS, the document examines relationships between child mortality rates, measles/HIV rates, and marital status data. For multiple regression: child mortality is the dependent variable, measles and HIV are independent variables, and the analysis finds HIV contributes most to predicting child mortality. For logistic regression: marital status is the dichotomous dependent variable, age groups are independent variables, and marriage rates are higher for ages 30-34 than 25-29. Both analyses show dependent variables are associated with and influenced by changes in independent variables.
This document provides an introduction to biostatistics. It defines statistics as the collection, organization, and analysis of data to draw inferences about a sample population. Biostatistics applies statistical methods to biological and medical data. The document discusses why biostatistics is studied, including that more aspects of medicine and public health are now quantified and biological processes have inherent variation. It also covers types of data, methods of data collection like questionnaires and observation, and considerations for designing questionnaires and conducting interviews.
The document provides a summary of topics related to conditional probability, Bayes' theorem, and independent events. It includes examples and formulas for conditional probability, multiplication rule of probability, total probability rule, Bayes' rule, and independent events. It also discusses pairwise and mutually independent events. The document concludes with examples demonstrating applications of conditional probability, Bayes' theorem, multiplication rule, total probability rule, and independent events.
Correlation describes the relationship between two or more variables. A positive correlation means that as one variable increases, the other also increases, while a negative correlation means that as one variable increases, the other decreases. Correlation is measured numerically using coefficients like the Pearson correlation coefficient r, which ranges from -1 to 1, with values farther from 0 indicating stronger linear relationships and the direction indicating positive or negative correlation. Correlation is used in business and economics to study relationships between variables like price and demand.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
The document discusses various concepts related to time series analysis and correlation. It defines time series as a sequence of data points measured over successive time periods. Time series analysis is used to extract meaningful patterns from temporal data and forecast future values. Correlation analysis examines the relationship between two quantitative variables, and can be positive, negative, linear or non-linear. Regression analysis is used to estimate the value of a dependent variable based on the value of an independent variable. Key components of time series include trends, cyclical variations, seasonal variations, and irregular variations.
Prediction studies attempt to describe predictive relationships between variables. Regression analysis allows prediction of an outcome variable from one or more predictor variables. It is useful for facilitating selection decisions, testing predictive variables, and determining predictive validity. Simple linear regression uses one predictor and criterion variable, while multiple regression uses more than one predictor to predict a criterion variable. [/SUMMARY]
Unit-III Correlation and Regression.pptxAnusuya123
Unit-III describes different types of relationships between variables through correlation and regression analysis. It discusses:
1) Correlation measures the strength and direction of a linear relationship between two variables on a scatter plot. Positive correlation means variables increase together, while negative correlation means one increases as the other decreases.
2) Regression analysis uses independent variables to predict outcomes of a dependent variable. A regression line minimizes the squared errors between predicted and actual values.
3) The correlation coefficient r and coefficient of determination r-squared quantify the strength and direction of linear relationships, with values between -1 and 1. Extreme scores on one measurement tend to regress toward the mean on subsequent measurements.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
This document summarizes a presentation on correlation and regression analysis. It introduces correlation, which measures the strength and direction of association between two variables. It describes Pearson's correlation coefficient and Spearman's correlation coefficient, and when each is appropriate. It then discusses regression, explaining the difference between correlation and regression, and introducing linear regression, logistic regression, and their applications. Examples of running linear and logistic regression in SPSS are provided.
Linear regression is a statistical method used to explain the relationship between variables. The document discusses:
1) An agenda covering regression, diagnostics, differences between linear and logistic regression, assumptions, and interview questions.
2) Details on linear regression including understanding the algorithm, assumptions around linearity, normality, multicollinearity, autocorrelation, and homoscedasticity.
3) How to check if assumptions are violated including residual plots, Q-Q plots, and various statistical tests.
The document provides an in-depth overview of linear regression modeling, assumptions, and how to diagnose potential issues.
This document discusses multiple linear regression analysis performed using SAS. It begins by outlining the assumptions of linear regression, including a linear relationship between variables, normality, no multicollinearity, and homoscedasticity. It then explains that multiple linear regression attempts to model the relationship between multiple explanatory variables and a response variable by fitting a linear equation to observed data. The document goes on to describe the regression analysis process, model selection, interpretation of outputs like R-squared and p-values, and evaluation of diagnostics like autocorrelation. It concludes by listing the predictor variables selected by the stepwise regression model and interpreting their parameter estimates.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
(1) The document provides an introduction to simple linear regression, including definitions of key terms like predictors, target variable, coefficients, and error terms. (2) It presents an example of simple linear regression analysis to predict yield based on temperature. (3) The outputs like regression statistics, line fit plot, and residual plot are interpreted to evaluate the relationship between the variables and check assumptions.
This document discusses the Pearson r correlation coefficient and how to calculate, interpret, and evaluate the significance of relationships between variables. The Pearson r ranges from -1 to 1, with values closer to these extremes indicating a stronger linear relationship and values near 0 a weaker relationship. A correlation of -0.67 represents the strongest relationship among the options given. The correlation coefficient is calculated using the formula presented. An example calculates the correlation between number of hospitals and maternal mortality rate in various cities. Interpretation of the correlation involves evaluating the magnitude, sign, strength of relationship, and significance.
This document discusses the Pearson r correlation coefficient and how to calculate, interpret, and evaluate the significance of relationships between variables. The Pearson r ranges from -1 to 1, with values closer to these extremes indicating a stronger linear relationship and values near 0 a weaker relationship. A correlation of -0.67 represents the strongest relationship among the options given. The correlation coefficient is calculated using the formula presented. An example calculates the correlation between number of hospitals and maternal mortality rate in various cities. Interpretation of the correlation involves evaluating the magnitude, sign, strength of relationship, and coefficient of determination. Statistical significance can be determined by comparing the correlation to critical values from tables.
A presentation on correlation and regression for engineering students studying probability and statistics. The presentation is designed according to syllabus of Institute of Engineering (IOE), Tribhuvan University. But the course content is similar to that of almost all the engineering universities.
Correlation analysis is a statistical technique used to determine the degree of relationship between two quantitative variables. Scatterplots are used to graphically depict the relationship and identify if it is positive, negative, or no correlation. The correlation coefficient measures the strength and direction of correlation, ranging from -1 to 1. A significance test determines if a correlation is likely to have occurred by chance or is statistically significant. Different types of correlation include simple, multiple, partial, and autocorrelation.
Using multiple regression and logistic regression analyses in SPSS, the document examines relationships between child mortality rates, measles/HIV rates, and marital status data. For multiple regression: child mortality is the dependent variable, measles and HIV are independent variables, and the analysis finds HIV contributes most to predicting child mortality. For logistic regression: marital status is the dichotomous dependent variable, age groups are independent variables, and marriage rates are higher for ages 30-34 than 25-29. Both analyses show dependent variables are associated with and influenced by changes in independent variables.
This document provides an introduction to biostatistics. It defines statistics as the collection, organization, and analysis of data to draw inferences about a sample population. Biostatistics applies statistical methods to biological and medical data. The document discusses why biostatistics is studied, including that more aspects of medicine and public health are now quantified and biological processes have inherent variation. It also covers types of data, methods of data collection like questionnaires and observation, and considerations for designing questionnaires and conducting interviews.
The document provides a summary of topics related to conditional probability, Bayes' theorem, and independent events. It includes examples and formulas for conditional probability, multiplication rule of probability, total probability rule, Bayes' rule, and independent events. It also discusses pairwise and mutually independent events. The document concludes with examples demonstrating applications of conditional probability, Bayes' theorem, multiplication rule, total probability rule, and independent events.
This document discusses statistical inference concepts including parameter estimation, hypothesis testing, sampling distributions, and confidence intervals. It provides examples of how to calculate point estimates, construct sampling distributions for sample means and proportions, and determine confidence intervals for population parameters using normal and t-distributions. The key concepts of statistical inference covered include parameter vs statistic, point vs interval estimation, properties of sampling distributions, and the components and calculation of confidence intervals.
The standard normal distribution, also called the Z-distribution, is a normal distribution with a mean of 0 and standard deviation of 1. To convert a random variable X with mean μ and standard deviation σ to the standard normal form Z, we calculate (X - μ)/σ. The normal distribution is widely used in statistics because many sampling distributions and transformations of variables tend toward normality for large samples. It also finds applications in approximating other distributions and in statistical quality control.
The document discusses three probability applications: 1) The probability of having 0, 1, 2, etc. boys before the first girl for a couple planning children. 2) The probability that the first, second, etc. anti-depressant drug tried is effective for a newly diagnosed patient, given a 60% effectiveness rate. 3) The expected number of donors that need to be tested to find a matching kidney donor for transplant, given a 10% probability of a random donor being a match.
The chi-square distribution is related to the normal distribution, as it is the distribution of the sum of squared normal random variables. The F distribution is the ratio of two chi-square random variables, each divided by its degrees of freedom. Both the chi-square and F distributions are used to test hypotheses about variances and compare variance estimates. To test if two samples have equal variances, the F test compares the ratio of the two sample variance estimates to the critical values of the F distribution with the degrees of freedom of each sample.
This document discusses measures of central tendency. It defines measures of central tendency as summary statistics that represent the center point of a distribution. The three main measures discussed are the mean, median, and mode. The mean is the sum of all values divided by the total number of values. There are different types of means including the arithmetic mean, weighted mean, and geometric mean. The document provides formulas for calculating each type of mean and discusses their properties and applications.
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.
This document provides an introduction to biostatistics for health science students at Debre Tabor University in Ethiopia. It defines biostatistics as the application of statistical methods to medical and public health problems. The introduction outlines topics that will be covered, including defining key statistical concepts, classifying variables, and discussing the importance and limitations of biostatistics. Contact information is provided for the lecturer, Asaye Alamneh.
This document provides an overview of the R programming language and environment. It discusses why R is useful, outlines its interface and workspace, describes how to access help and tutorials, install packages, and input/output data. The interactive nature of R is highlighted, where results from one function can be used as input for another.
This document outlines a study on jointly modeling multivariate longitudinal measures of hypertension (blood pressure and pulse rate) and time to develop cardiovascular disease among hypertensive outpatients in Ethiopia. The study aims to identify factors affecting changes in blood pressure and pulse rate over time as well as time to develop cardiovascular complications. The study will collect longitudinal data on blood pressure, pulse rate and cardiovascular events from 178 hypertensive patients and analyze it using joint longitudinal-survival models. Preliminary results show changes in blood pressure and pulse rate over time differ between patients who did and did not develop cardiovascular events. Key factors like diabetes, family history of hypertension and clinical stage of hypertension affect both longitudinal outcomes and survival.
This document provides an introduction to common statistical terms and concepts used in biostatistics. It defines key terms like data, variables, independent and dependent variables. It also discusses populations and samples, and how random samples and random assignment are used in research. The document outlines descriptive statistics and different levels of measurement. It also explains concepts like measures of central tendency, frequency distributions, normal distributions, and skewed distributions. Finally, it discusses properties of normal curves and what the standard deviation represents.
Hypertension and it's role of physiotherapy in it.Vishal kr Thakur
This particular slides consist of- what is hypertension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is summary of hypertension -
Hypertension, also known as high blood pressure, is a serious medical condition that occurs when blood pressure in the body's arteries is consistently too high. Blood pressure is the force of blood pushing against the walls of blood vessels as the heart pumps it. Hypertension can increase the risk of heart disease, brain disease, kidney disease, and premature death.
The story of Dr. Ranjit Jagtap's daughters is more than a tale of inherited responsibility; it's a narrative of passion, innovation, and unwavering commitment to a cause greater than oneself. In Poulami and Aditi Jagtap, we see the beautiful continuum of a father's dream and the limitless potential of compassion-driven healthcare.
Emotional and Behavioural Problems in Children - Counselling and Family Thera...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Health Tech Market Intelligence Prelim Questions -Gokul Rangarajan
The Ultimate Guide to Setting up Market Research in Health Tech part -1
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
This lays foundation of scoping research project what are the
Before embarking on a research project, especially one aimed at scoping and defining parameters like the one described for health tech IT, several crucial considerations should be addressed. Here’s a comprehensive guide covering key aspects to ensure a well-structured and successful research initiative:
1. Define Research Objectives and Scope
Clear Objectives: Define specific goals such as understanding market needs, identifying new opportunities, assessing risks, or refining pricing strategies.
Scope Definition: Clearly outline the boundaries of the research in terms of geographical focus, target demographics (e.g., age, socio-economic status), and industry sectors (e.g., healthcare IT).
3. Review Existing Literature and Resources
Literature Review: Conduct a thorough review of existing research, market reports, and relevant literature to build foundational knowledge.
Gap Analysis: Identify gaps in existing knowledge or areas where further exploration is needed.
4. Select Research Methodology and Tools
Methodological Approach: Choose appropriate research methods such as surveys, interviews, focus groups, or data analytics.
Tools and Resources: Select tools like Google Forms for surveys, analytics platforms (e.g., SimilarWeb, Statista), and expert consultations.
5. Ethical Considerations and Compliance
Ethical Approval: Ensure compliance with ethical guidelines for research involving human subjects.
Data Privacy: Implement measures to protect participant confidentiality and adhere to data protection regulations (e.g., GDPR, HIPAA).
6. Budget and Resource Allocation
Resource Planning: Allocate resources including time, budget, and personnel required for each phase of the research.
Contingency Planning: Anticipate and plan for unforeseen challenges or adjustments to the research plan.
7. Develop Research Instruments
Survey Design: Create well-structured surveys using tools like Google Forms to gather quantitative data.
Interview and Focus Group Guides: Prepare detailed scripts and discussion points for qualitative data collection.
8. Sampling Strategy
Sampling Design: Define the sampling frame, size, and method (e.g., random sampling, stratified sampling) to ensure representation of target demographics.
Participant Recruitment: Plan recruitment strategies to reach and engage the intended participant groups effectively.
9. Data Collection and Analysis Plan
Data Collection: Implement methods for data gathering, ensuring consistency and validity.
Analysis Techniques: Decide on analytical approaches (e.g., statistical
Enhancing Hip and Knee Arthroplasty Precision with Preoperative CT and MRI Im...Pristyn Care Reviews
Precision becomes a byword, most especially in such procedures as hip and knee arthroplasty. The success of these surgeries is not just dependent on the skill and experience of the surgeons but is extremely dependent on preoperative planning. Recognizing this important need, Pristyn Care commits itself to the integration of advanced imaging technologies like CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) into the surgical planning process.
Fit to Fly PCR Covid Testing at our Clinic Near YouNX Healthcare
A Fit-to-Fly PCR Test is a crucial service for travelers needing to meet the entry requirements of various countries or airlines. This test involves a polymerase chain reaction (PCR) test for COVID-19, which is considered the gold standard for detecting active infections. At our travel clinic in Leeds, we offer fast and reliable Fit to Fly PCR testing, providing you with an official certificate verifying your negative COVID-19 status. Our process is designed for convenience and accuracy, with quick turnaround times to ensure you receive your results and certificate in time for your departure. Trust our professional and experienced medical team to help you travel safely and compliantly, giving you peace of mind for your journey.www.nxhealthcare.co.uk
India Medical Devices Market: Size, Share, and In-Depth Competitive Analysis ...Kumar Satyam
According to TechSci Research report, “India Medical Devices Market Industry Size, Share, Trends, Competition, Opportunity and Forecast, 2019-2029,” the India Medical Devices Market was valued at USD 15.35 billion in 2023 and is anticipated to witness impressive growth in the forecast period, with a Compound Annual Growth Rate (CAGR) of 5.35% through 2029. This growth is driven by various factors, including strategic collaborations and partnerships among leading companies, a growing population, and the increasing demand for advanced healthcare solutions.
Recent Trends
Strategic Collaborations and Partnerships
One of the most significant trends driving the India Medical Devices Market is the increasing number of collaborations and partnerships among leading companies. These alliances aim to merge the expertise of individual companies to strengthen their market position and enhance their product offerings. For instance, partnerships between local manufacturers and international companies bring advanced technologies and manufacturing techniques to the Indian market, fostering innovation and improving product quality.
Browse over XX market data Figures and spread through XX Pages and an in-depth TOC on " India Medical Devices Market.” - https://www.techsciresearch.com/report/india-medical-devices-market/8161.html
R3 Stem Cell Therapy: A New Hope for Women with Ovarian FailureR3 Stem Cell
Discover the groundbreaking advancements in stem cell therapy by R3 Stem Cell, offering new hope for women with ovarian failure. This innovative treatment aims to restore ovarian function, improve fertility, and enhance overall well-being, revolutionizing reproductive health for women worldwide.
Solution manual for managerial accounting 18th edition by ray garrison eric n...rightmanforbloodline
Solution manual for managerial accounting 18th edition by ray garrison eric noreen and peter brewer_compressed
Solution manual for managerial accounting 18th edition by ray garrison eric noreen and peter brewer_compressed
2. Scatter plots
• Regression analysis requires interval
and ratio-level data.
• To see if your data fits the models of
regression, it is wise to conduct a
scatter plot analysis.
• The reason?
– Regression analysis assumes a linear
relationship. If you have a curvilinear
relationship or no relationship,
regression analysis is of little use.
4. Scatter plot
15.0 20.0 25.0 30.0 35.0
Percent of Population 25 years and Over with Bachelor's Degree or More,
March 2000 estimates
20000
25000
30000
35000
40000
Personal
Income
Per
Capita,
current
dollars,
1999
Percent of Population with Bachelor's Degree by Personal Income Per Capita
•This is a linear
relationship
•It is a positive
relationship.
•As population with
BA’s increases so does
the personal income
per capita.
5. Regression Line
15.0 20.0 25.0 30.0 35.0
Percent of Population 25 years and Over with Bachelor's Degree or More,
March 2000 estimates
20000
25000
30000
35000
40000
Personal
Income
Per
Capita,
current
dollars,
1999
Percent of Population with Bachelor's Degree by Personal Income Per Capita
R Sq Linear = 0.542
•Regression line is
the best straight line
description of the
plotted points and
use can use it to
describe the
association between
the variables.
•If all the lines fall
exactly on the line
then the line is 0 and
you have a perfect
relationship.
6. Things to remember
• Regressions are still focuses on
association, not causation.
• Association is a necessary
prerequisite for inferring causation,
but also:
1. The independent variable must preceded
the dependent variable in time.
2. The two variables must be plausibly lined
by a theory,
3. Competing independent variables must
be eliminated.
7. Regression Table
•The regression
coefficient is not a
good indicator for the
strength of the
relationship.
•Two scatter plots with
very different
dispersions could
produce the same
regression line.
15.0 20.0 25.0 30.0 35.0
Percent of Population 25 years and Over with Bachelor's Degree or More,
March 2000 estimates
20000
25000
30000
35000
40000
Personal
Income
Per
Capita,
current
dollars,
1999
Percent of Population with Bachelor's Degree by Personal Income Per Capita
R Sq Linear = 0.542
0.00 200.00 400.00 600.00 800.00 1000.00 1200.00
Population Per Square Mile
20000
25000
30000
35000
40000
Personal
Income
Per
Capita,
current
dollars,
1999
Percent of Population with Bachelor's Degree by Personal Income Per Capita
R Sq Linear = 0.463
8. Regression coefficient
• The regression coefficient is the slope of
the regression line and tells you what
the nature of the relationship between
the variables is.
• How much change in the independent
variables is associated with how much
change in the dependent variable.
• The larger the regression coefficient the
more change.
9. Pearson’s r
• To determine strength you look at how
closely the dots are clustered around the
line. The more tightly the cases are
clustered, the stronger the relationship,
while the more distant, the weaker.
• Pearson’s r is given a range of -1 to + 1
with 0 being no linear relationship at all.
10. Reading the tables
•When you run regression analysis on SPSS you get a
3 tables. Each tells you something about the
relationship.
•The first is the model summary.
•The R is the Pearson Product Moment Correlation
Coefficient.
•In this case R is .736
•R is the square root of R-Squared and is the
correlation between the observed and predicted
values of dependent variable.
Model Summary
.736a
.542 .532 2760.003
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), Percent of Population 25 years
and Over with Bachelor's Degree or More, March 2000
estimates
a.
11. R-Square
•R-Square is the proportion of variance in the
dependent variable (income per capita) which can be
predicted from the independent variable (level of
education).
•This value indicates that 54.2% of the variance in
income can be predicted from the variable
education. Note that this is an overall measure of the
strength of association, and does not reflect the
extent to which any particular independent variable
is associated with the dependent variable.
•R-Square is also called the coefficient of
Model Summary
.736a
.542 .532 2760.003
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), Percent of Population 25 years
and Over with Bachelor's Degree or More, March 2000
estimates
a.
12. Adjusted R-square
•As predictors are added to the model, each predictor will explain
some of the variance in the dependent variable simply due to
chance.
•One could continue to add predictors to the model which would
continue to improve the ability of the predictors to explain the
dependent variable, although some of this increase in R-square
would be simply due to chance variation in that particular sample.
•The adjusted R-square attempts to yield a more honest value to
estimate the R-squared for the population. The value of R-square
was .542, while the value of Adjusted R-square was .532. There
isn’t much difference because we are dealing with only one
variable.
•When the number of observations is small and the number of
predictors is large, there will be a much greater difference between
R-square and adjusted R-square.
•By contrast, when the number of observations is very large
compared to the number of predictors, the value of R-square and
Model Summary
.736a
.542 .532 2760.003
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), Percent of Population 25 years
and Over with Bachelor's Degree or More, March 2000
estimates
a.
13. ANOVA
•The p-value associated with this F value is very small
(0.0000).
•These values are used to answer the question "Do the
independent variables reliably predict the dependent
variable?".
•The p-value is compared to your alpha level (typically 0.05)
and, if smaller, you can conclude "Yes, the independent
variables reliably predict the dependent variable".
•If the p-value were greater than 0.05, you would say that the
group of independent variables does not show a statistically
significant relationship with the dependent variable, or that
the group of independent variables does not reliably predict
the dependent variable.
ANOVAb
4.32E+08 1 432493775.8 56.775 .000a
3.66E+08 48 7617618.586
7.98E+08 49
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), Percent of Population 25 years and Over with Bachelor's
Degree or More, March 2000 estimates
a.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
b.
14. Coefficients
•B - These are the values for the regression equation
for predicting the dependent variable from the
independent variable.
•These are called unstandardized coefficients
because they are measured in their natural
units. As such, the coefficients cannot be compared
with one another to determine which one is more
influential in the model, because they can be
measured on different scales.
Coefficientsa
10078.565 2312.771 4.358 .000
688.939 91.433 .736 7.535 .000
(Constant)
Percent of Population
25 years and Over
with Bachelor's
Degree or More,
March 2000 estimates
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
a.
15. Coefficients
•This chart looks at two variables and shows how
the different bases affect the B value. That is why
you need to look at the standardized Beta to see
the differences.
Coefficientsa
13032.847 1902.700 6.850 .000
517.628 78.613 .553 6.584 .000
7.953 1.450 .461 5.486 .000
(Constant)
Percent of Population
25 years and Over
with Bachelor's
Degree or More,
March 2000 estimates
Population Per
Square Mile
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
a.
16. Coefficients
•Beta - The are the standardized coefficients.
•These are the coefficients that you would obtain if you
standardized all of the variables in the regression, including
the dependent and all of the independent variables, and ran
the regression.
•By standardizing the variables before running the regression,
you have put all of the variables on the same scale, and you
can compare the magnitude of the coefficients to see which
one has more of an effect.
•You will also notice that the larger betas are associated with
Coefficientsa
10078.565 2312.771 4.358 .000
688.939 91.433 .736 7.535 .000
(Constant)
Percent of Population
25 years and Over
with Bachelor's
Degree or More,
March 2000 estimates
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
a.
17. How to translate a typical table
Regression Analysis Level of Education by Income per capita
Income per capita
Independent variables b Beta
Percent population with BA 688.939 .736
R2
.542
Number of Cases 49
18. Part of the Regression Equation
• b represents the slope of the line
– It is calculated by dividing the change in
the dependent variable by the change in
the independent variable.
– The difference between the actual value
of Y and the calculated amount is called
the residual.
– The represents how much error there is
in the prediction of the regression
equation for the y value of any
individual case as a function of X.
19. Comparing two variables
• Regression analysis is useful for
comparing two variables to see whether
controlling for other independent variable
affects your model.
• For the first independent variable,
education, the argument is that a more
educated populace will have higher-paying
jobs, producing a higher level of per capita
income in the state.
• The second independent variable is
included because we expect to find better-
paying jobs, and therefore more
opportunity for state residents to obtain
them, in urban rather than rural areas.
20. Single
Model Summary
.849a
.721 .709 2177.791
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), Population Per Square Mile,
Percent of Population 25 years and Over with
Bachelor's Degree or More, March 2000 estimates
a.
ANOVAb
5.75E+08 2 287614518.2 60.643 .000a
2.23E+08 47 4742775.141
7.98E+08 49
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), Population Per Square Mile, Percent of Population 25 years
and Over with Bachelor's Degree or More, March 2000 estimates
a.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
b.
Coefficientsa
13032.847 1902.700 6.850 .000
517.628 78.613 .553 6.584 .000
7.953 1.450 .461 5.486 .000
(Constant)
Percent of Population
25 years and Over
with Bachelor's
Degree or More,
March 2000 estimates
Population Per
Square Mile
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
a.
Model Summary
.736a
.542 .532 2760.003
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), Percent of Population 25 years
and Over with Bachelor's Degree or More, March 2000
estimates
a.
ANOVAb
4.32E+08 1 432493775.8 56.775 .000a
3.66E+08 48 7617618.586
7.98E+08 49
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), Percent of Population 25 years and Over with Bachelor's
Degree or More, March 2000 estimates
a.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
b.
Coefficientsa
10078.565 2312.771 4.358 .000
688.939 91.433 .736 7.535 .000
(Constant)
Percent of Population
25 years and Over
with Bachelor's
Degree or More,
March 2000 estimates
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: Personal Income Per Capita, current dollars, 1999
a.
Multiple
Regression
21. Single Regression
Income per capita
Independent variables b Beta
Percent population with BA 688.939 .736
R2
.542
Number of Cases 49
Multiple Regression
Income per capita
Independent variables b Beta
Percent population with BA 517.628 .553
Population Density 7.953 .461
R2
.721
Adjusted R2
.709
Number of Cases 49