This document provides an overview and instructions for running and interpreting a multinomial logistic regression model in SPSS. It begins with a recap of previous sessions on logistic regression and variable selection. Instructions are then given on running a multinomial logistic regression with education level as the dependent variable. The results of the model are interpreted, including goodness of fit tests, likelihood ratio tests of individual predictors, and parameter estimates comparing the categories of the dependent to the reference category. Finally, an exercise is proposed interpreting selected odds ratios from the model. The summary is provided in under 3 sentences.
This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.
This document provides an overview of introducing SPSS and quantifying data for analysis. It discusses the different types of data in SPSS including nominal, ordinal, interval/ratio scales. It covers entering data from questionnaires or other sources into SPSS and constructing a codebook. The document then explains how to conduct basic analyses in SPSS including frequency counts, measures of central tendency and dispersion, charts, contingency tables, and chi-square tests. It emphasizes correctly preparing and working with data in SPSS before conducting analyses.
Assignment 1 (to be submitted through the assignment submisslicservernoida
The document provides instructions for an assignment with 5 questions analyzing datasets. It includes tasks like generating scatterplots, identifying outliers, computing correlation coefficients, conducting regression analyses and hypothesis tests, and interpreting the results. Students are asked to compile their answers in a Microsoft Word document and show their work. They should pay careful attention to formatting details like rounding decimals and labeling figures.
This section discusses analyzing categorical data:
- It introduces categorical variables and how to construct frequency tables and graphs like bar graphs and pie charts to display categorical variable distributions.
- It explains how to construct and interpret two-way tables to analyze relationships between two categorical variables, and how to examine marginal and conditional distributions.
- It emphasizes organizing statistical problems using a four step approach of stating the question, planning an approach, doing calculations/graphs, and concluding.
This document discusses bias and variance in machine learning models. It begins by introducing bias as a stronger force that is always present and harder to eliminate than variance. Several examples of bias are provided. Through simulations of sampling from a normal distribution, it is shown that sample statistics like the mean and standard deviation are always biased compared to the population parameters. Sample size also impacts bias, with larger samples having lower bias. Variance refers to a model's ability to generalize, with higher variance indicating overfitting. The tradeoff between bias and variance is that reducing one increases the other. Several techniques for optimizing this tradeoff are discussed, including cross-validation, bagging, boosting, dimensionality reduction, and changing the model complexity.
Logistic regression is used to predict categorical outcomes. The presented document discusses logistic regression, including its objectives, assumptions, key terms, and an example application to predicting basketball match outcomes. Logistic regression uses maximum likelihood estimation to model the relationship between a binary dependent variable and independent variables. The document provides an illustrated example of conducting logistic regression in SPSS to predict match results based on variables like passes, rebounds, free throws, and blocks.
This document discusses logistic regression, a classification algorithm used to predict the probability of discrete outcomes. It provides examples of classification problems like customer churn, credit risk, fraud detection. Logistic regression models the log odds of the dependent variable using the sigmoid function. The document outlines the steps to develop a logistic regression model using a default prediction dataset: preprocessing data, fitting a model on training data, interpreting coefficients, assessing fit, making predictions on test data, and evaluating the model's performance.
This document provides a summary of visual tools for interpreting machine learning models based on partial dependency plots and their variants. It introduces novel visual concepts such as overall, collapsed, and marginal partial dependency plots and shows how they can help with model interpretation. An example is provided using a simple dataset with 6 predictors and a binary target variable to classify fraudulent vs. valid insurance claims. Model interpretation focuses on identifying important variables and their effects rather than explaining individual predictions.
This document provides guidance on performing and interpreting logistic regression analyses in SPSS. It discusses selecting appropriate statistical tests based on variable types and study objectives. It covers assumptions of logistic regression like linear relationships between predictors and the logit of the outcome. It also explains maximum likelihood estimation, interpreting coefficients, and evaluating model fit and accuracy. Guidelines are provided on reporting logistic regression results from SPSS outputs.
This document provides an overview of introducing SPSS and quantifying data for analysis. It discusses the different types of data in SPSS including nominal, ordinal, interval/ratio scales. It covers entering data from questionnaires or other sources into SPSS and constructing a codebook. The document then explains how to conduct basic analyses in SPSS including frequency counts, measures of central tendency and dispersion, charts, contingency tables, and chi-square tests. It emphasizes correctly preparing and working with data in SPSS before conducting analyses.
Assignment 1 (to be submitted through the assignment submisslicservernoida
The document provides instructions for an assignment with 5 questions analyzing datasets. It includes tasks like generating scatterplots, identifying outliers, computing correlation coefficients, conducting regression analyses and hypothesis tests, and interpreting the results. Students are asked to compile their answers in a Microsoft Word document and show their work. They should pay careful attention to formatting details like rounding decimals and labeling figures.
This section discusses analyzing categorical data:
- It introduces categorical variables and how to construct frequency tables and graphs like bar graphs and pie charts to display categorical variable distributions.
- It explains how to construct and interpret two-way tables to analyze relationships between two categorical variables, and how to examine marginal and conditional distributions.
- It emphasizes organizing statistical problems using a four step approach of stating the question, planning an approach, doing calculations/graphs, and concluding.
This document discusses bias and variance in machine learning models. It begins by introducing bias as a stronger force that is always present and harder to eliminate than variance. Several examples of bias are provided. Through simulations of sampling from a normal distribution, it is shown that sample statistics like the mean and standard deviation are always biased compared to the population parameters. Sample size also impacts bias, with larger samples having lower bias. Variance refers to a model's ability to generalize, with higher variance indicating overfitting. The tradeoff between bias and variance is that reducing one increases the other. Several techniques for optimizing this tradeoff are discussed, including cross-validation, bagging, boosting, dimensionality reduction, and changing the model complexity.
Logistic regression is used to predict categorical outcomes. The presented document discusses logistic regression, including its objectives, assumptions, key terms, and an example application to predicting basketball match outcomes. Logistic regression uses maximum likelihood estimation to model the relationship between a binary dependent variable and independent variables. The document provides an illustrated example of conducting logistic regression in SPSS to predict match results based on variables like passes, rebounds, free throws, and blocks.
This document discusses logistic regression, a classification algorithm used to predict the probability of discrete outcomes. It provides examples of classification problems like customer churn, credit risk, fraud detection. Logistic regression models the log odds of the dependent variable using the sigmoid function. The document outlines the steps to develop a logistic regression model using a default prediction dataset: preprocessing data, fitting a model on training data, interpreting coefficients, assessing fit, making predictions on test data, and evaluating the model's performance.
This document provides a summary of visual tools for interpreting machine learning models based on partial dependency plots and their variants. It introduces novel visual concepts such as overall, collapsed, and marginal partial dependency plots and shows how they can help with model interpretation. An example is provided using a simple dataset with 6 predictors and a binary target variable to classify fraudulent vs. valid insurance claims. Model interpretation focuses on identifying important variables and their effects rather than explaining individual predictions.
Binary dependent variable classification model in context of large databases: interpretation via visual tools such as partial dependency plots for 1, 2, 3, and 4 variables and other plots. Presentation focuses on overall and not individual observation interpretation, and is still work in progress.
This document discusses bivariate linear regression and its understanding. Bivariate linear regression, also called simple linear regression, involves modeling the relationship between a dependent variable (Y) and a single independent variable (X). The regression equation takes the form of Y = β0 + β1X + ε, where β0 is the intercept, β1 is the slope coefficient, and ε is the error term. This equation can be used to predict Y values based on X values, as well as understand how much variation in Y can be explained by X. Parameters β0 and β1 are estimated to maximize the explanatory power of X for Y while minimizing prediction errors.
This document provides an overview of quantitative data analysis techniques including descriptive statistics, reliability analysis, factor analysis, and various statistical tests. Descriptive statistics involve calculating frequencies, percentages, means, and cross-tabulations to summarize demographic and other variables. Reliability analysis using Cronbach's alpha is described to measure the internal consistency of scales. The steps for conducting an exploratory factor analysis are outlined. Finally, guidance is provided on selecting appropriate statistical tests such as t-tests, ANOVA, regression, chi-square, and Mann-Whitney U based on the variables' levels of measurement and number of groups being compared.
PSYCH 625 MENTOR Become Exceptional--psych625mentor.comshanaabe77
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
This document summarizes an R boot camp focusing on statistics. It includes an agenda that covers introducing the lab component, R basics, descriptive statistics in R, revisiting installation instructions, and measures of variability in R. Descriptive statistics are presented as ways to characterize data through measures of central tendency, shape, and variability. Examples are provided in R for calculating the mean, median, mode, range, percentiles, variance, standard deviation, and coefficient of variation. The central limit theorem and standardizing scores are also discussed. Real-world applications of R for clean and messy data are mentioned.
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
This document discusses descriptive statistics and numerical measures used to describe data sets. It introduces measures of central tendency including the mean, median, and mode. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value. The document also discusses measures of dispersion like range and standard deviation which describe how spread out the data is. Examples are provided to demonstrate calculating the mean, median and other descriptive statistics.
1. A regression of price on lot size for 832 housing observations found that lot size was a statistically significant predictor of price, with an estimated slope parameter of 1.38850 (p<0.00001).
2. Tests for heteroskedasticity found evidence that the error variances were not constant, violating the homoskedasticity assumption.
3. Rerunning the regression with heteroskedasticity-robust standard errors produced larger standard errors compared to the original OLS standard errors, better accounting for the heteroskedasticity in the data.
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
This document provides information and assignments for a PSYCH 625 class, including assignments for each week covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the class.
PSYCH 625 MENTOR Education for Service-- psych625mentor.comKeatonJennings36
This document provides information and assignments for a PSYCH 625 class, including assignments for each week of the course covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the course.
This document contains information about assignments for a PSYCH 625 course, including weekly worksheets and a multi-part statistics project. The worksheets cover topics like descriptive statistics, probability, statistical tests, and analyzing research studies. The statistics project involves analyzing a dataset using Excel to calculate descriptive statistics, form a hypothesis, and test it using appropriate statistical methods like t-tests. Completing the assignments will help students learn and apply statistical analysis skills.
PSYCH 625 MENTOR Knowledge is divine--psych625mentor.comkarthik10037
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
This document outlines objectives and concepts for a unit on statistical analysis in IB Diploma Biology. It discusses types of data, graphs, and statistics including mean, standard deviation, correlation, and significance testing. Key concepts covered are descriptive statistics like mean and standard deviation to summarize data, the importance of variability, and inferential statistics like hypothesis testing and p-values to draw conclusions about populations from samples. The goals are to calculate basic statistics, choose appropriate graphs, understand significance, and apply proper lab techniques and formats.
This document provides information about getting fully solved assignments for various postgraduate programs and semesters. Students can send their semester and specialization details to the provided email ID or call the given phone number to get assignments. It includes details of subject codes, credits, and marks for assignments related to research methodology for programs like MBA, PGDM, PGDHRM etc. for semesters 1 and 3.
This document provides an overview of basic statistics concepts including descriptive statistics, measures of central tendency, variability, sampling, and distributions. It defines key terms like mean, median, mode, range, standard deviation, variance, and quantiles. Examples are provided to demonstrate how to calculate and interpret these common statistical measures.
This module discusses measures of variability such as range and standard deviation. It provides examples of computing the range of various data sets as the difference between the highest and lowest values. Standard deviation is introduced as a more reliable measure that considers how far all values are from the mean. Students learn to calculate standard deviation by finding the deviation of each value from the mean, squaring the deviations, taking the average of the squared deviations, and extracting the square root. They practice computing and interpreting the range and standard deviation of sample data sets.
Binary dependent variable classification model in context of large databases: interpretation via visual tools such as partial dependency plots for 1, 2, 3, and 4 variables and other plots. Presentation focuses on overall and not individual observation interpretation, and is still work in progress.
This document discusses bivariate linear regression and its understanding. Bivariate linear regression, also called simple linear regression, involves modeling the relationship between a dependent variable (Y) and a single independent variable (X). The regression equation takes the form of Y = β0 + β1X + ε, where β0 is the intercept, β1 is the slope coefficient, and ε is the error term. This equation can be used to predict Y values based on X values, as well as understand how much variation in Y can be explained by X. Parameters β0 and β1 are estimated to maximize the explanatory power of X for Y while minimizing prediction errors.
This document provides an overview of quantitative data analysis techniques including descriptive statistics, reliability analysis, factor analysis, and various statistical tests. Descriptive statistics involve calculating frequencies, percentages, means, and cross-tabulations to summarize demographic and other variables. Reliability analysis using Cronbach's alpha is described to measure the internal consistency of scales. The steps for conducting an exploratory factor analysis are outlined. Finally, guidance is provided on selecting appropriate statistical tests such as t-tests, ANOVA, regression, chi-square, and Mann-Whitney U based on the variables' levels of measurement and number of groups being compared.
PSYCH 625 MENTOR Become Exceptional--psych625mentor.comshanaabe77
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
This document summarizes an R boot camp focusing on statistics. It includes an agenda that covers introducing the lab component, R basics, descriptive statistics in R, revisiting installation instructions, and measures of variability in R. Descriptive statistics are presented as ways to characterize data through measures of central tendency, shape, and variability. Examples are provided in R for calculating the mean, median, mode, range, percentiles, variance, standard deviation, and coefficient of variation. The central limit theorem and standardizing scores are also discussed. Real-world applications of R for clean and messy data are mentioned.
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus)
This document discusses descriptive statistics and numerical measures used to describe data sets. It introduces measures of central tendency including the mean, median, and mode. The mean is the average value calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequently occurring value. The document also discusses measures of dispersion like range and standard deviation which describe how spread out the data is. Examples are provided to demonstrate calculating the mean, median and other descriptive statistics.
1. A regression of price on lot size for 832 housing observations found that lot size was a statistically significant predictor of price, with an estimated slope parameter of 1.38850 (p<0.00001).
2. Tests for heteroskedasticity found evidence that the error variances were not constant, violating the homoskedasticity assumption.
3. Rerunning the regression with heteroskedasticity-robust standard errors produced larger standard errors compared to the original OLS standard errors, better accounting for the heteroskedasticity in the data.
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
This document provides information and assignments for a PSYCH 625 class, including assignments for each week covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the class.
PSYCH 625 MENTOR Education for Service-- psych625mentor.comKeatonJennings36
This document provides information and assignments for a PSYCH 625 class, including assignments for each week of the course covering topics like descriptive statistics, probability, hypothesis testing, comparing means, correlation, and chi-square tests. It includes details of assignments involving worksheets, data analysis projects in Microsoft Excel, and a final presentation. The assignments involve analyzing various datasets to describe and make inferences about the data using statistical techniques taught in the course.
This document contains information about assignments for a PSYCH 625 course, including weekly worksheets and a multi-part statistics project. The worksheets cover topics like descriptive statistics, probability, statistical tests, and analyzing research studies. The statistics project involves analyzing a dataset using Excel to calculate descriptive statistics, form a hypothesis, and test it using appropriate statistical methods like t-tests. Completing the assignments will help students learn and apply statistical analysis skills.
PSYCH 625 MENTOR Knowledge is divine--psych625mentor.comkarthik10037
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
PSYCH 625 Assignment Week 4 Comparing Means Worksheet (New Syllabus
FOR MORE CLASSES VISIT
www.psych625mentor.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
PSYCH 625 Assignment Week 3 Identifying Statistical Tests in the Literature Worksheet (New Syllabus)
This document outlines objectives and concepts for a unit on statistical analysis in IB Diploma Biology. It discusses types of data, graphs, and statistics including mean, standard deviation, correlation, and significance testing. Key concepts covered are descriptive statistics like mean and standard deviation to summarize data, the importance of variability, and inferential statistics like hypothesis testing and p-values to draw conclusions about populations from samples. The goals are to calculate basic statistics, choose appropriate graphs, understand significance, and apply proper lab techniques and formats.
This document provides information about getting fully solved assignments for various postgraduate programs and semesters. Students can send their semester and specialization details to the provided email ID or call the given phone number to get assignments. It includes details of subject codes, credits, and marks for assignments related to research methodology for programs like MBA, PGDM, PGDHRM etc. for semesters 1 and 3.
This document provides an overview of basic statistics concepts including descriptive statistics, measures of central tendency, variability, sampling, and distributions. It defines key terms like mean, median, mode, range, standard deviation, variance, and quantiles. Examples are provided to demonstrate how to calculate and interpret these common statistical measures.
This module discusses measures of variability such as range and standard deviation. It provides examples of computing the range of various data sets as the difference between the highest and lowest values. Standard deviation is introduced as a more reliable measure that considers how far all values are from the mean. Students learn to calculate standard deviation by finding the deviation of each value from the mean, squaring the deviations, taking the average of the squared deviations, and extracting the square root. They practice computing and interpreting the range and standard deviation of sample data sets.
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Gender and Mental Health - Counselling and Family Therapy Applications and In...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!
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
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How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
2. Introduction
• Recap – Last Week
• Workshop Feedback
• Multinomial Logistic Regression in SPSS
• Model Interpretation
• In Class Exercise
• Writing-Up
• Summary
3. Recap – Last Week
• Variable selection
• Binary logistic regression in SPSS
• Model interpretation
• Intuitive results?
4. Workshop Feedback
TASK:
To run and interpret a binary logistic regression model with
‘Sex’ as the dependent variable using your own choice of
independent variables
Were your models successful?
Did you have any problems or issues?
TODAY: I will show you how to run and interpret a multinomial logistic model in
SPSS. I will use a different dependent variable (‘edlev7’) and the same dataset.
Did you find anything interesting (interpretation of odds ratios)?
Did you have difficulty in interpretation?
5. Multinomial Logistic Regression in
SPSS I
• Very similar to binary logistic regression
• For a categorical dependent variable with more than two categories
• ‘edlev7’ asks for the highest educational qualification of a respondent and
has three categories: ‘Higher Education’, ‘Other Qualification’ and ‘None’
• One of these categories has to be designated a ‘reference category’ to
which the others will be compared
• E.g. if ‘None’ is the ‘reference category’…
– respondents who had Higher Education qualifications were more likely to be
female (odds increase of 2.3) than respondents with no qualifications
– Respondents who had other qualifications were less likely to be female (odds
decrease of 0.45) than respondent with no qualifications
It is not possible to compare groups that are not the ‘reference category’ i.e. we cannot
draw comparisons between ‘Higher Education’ and ‘Other Qualification’ directly
6. Multinomial Logistic Regression in
SPSS II
Education Level - 2000 (3 groups)
Frequency Percent Valid Percent
Cumulative
Percent
Valid HIGHER EDUCAT 2015 24.5 31.2 31.2
OTHER QUAL 2826 34.4 43.8 75.0
NONE 1614 19.6 25.0 100.0
Total 6455 78.5 100.0
Missing NEV WENT SCH 16 .2
NA 4 .0
AGEOUT,MSPR 1745 21.2
System 1 .0
Total 1766 21.5
Total 8221 100.0
Deciding on a ‘reference category’ should be an informed decision – what
do we want to compare?
As a rule of
thumb, the
‘reference
category’ should
be the most
populated
response (highest
frequency), but
this can be over-
ruled by your
research agenda
In this case I am going to use ‘Other Qualification’ for
several reasons: largest group, median point and
interesting from a theoretical perspective (difference
between ‘Other Qual’ and ‘Higher Education’ might
question value of studying at university…
7. Multinomial Logistic Regression in
SPSS III
• You still need to select your variables carefully
• Consider hypotheses, frequencies, recoding, relationships and
multicolinearity
• My variables (including recodes):
– ‘manual2’ (non-manual/manual)
– ‘ethnic2’ (white/non-white)
– ‘marital2’ (married/cohabiting/single/widowed/divorced or separated)
– ‘seefrnd2’ (weekly/monthly/less than monthly/not in last year)
– ‘cntctmp’ (yes/no)
– ‘age’ (in years)
– ‘alcdrug2’ (very big problem/fairly big problem/minor problem/not a
problem/happens but is not a problem)
– ‘influence2’ (yes/no)
Excluded due to multicolinearity – could be interesting…
8. Multinomial Logistic Regression in
SPSS IV
1) To begin, go to ‘Analyze’, ‘Regression’ and select ‘Multinomial Logistic…’
2) Your dependent
goes here
3) Click on ‘Reference
Category…’
By default SPSS will use the last category in your independent categorical variables as
the ‘reference category’
9. Multinomial Logistic Regression in
SPSS V
You need to tell SPSS which response
for the dependent variable you want
to be used as the ‘reference category’
4) Because ‘Other Qualification’ is
coded as ‘2’ in our dataset and we
want to use this as the ‘reference
category’ we select ‘Custom’ and type
the value (‘2’)
‘Category Order’ is important when
specifying ‘First Category’ or ‘Last
Category’ – always a good idea to
specify a custom value manually
5) Click ‘Continue’
10. Multinomial Logistic Regression in
SPSS VI
Notice that the dependent is now follows by ‘(Custom)’
6) Your
categorical
independent
variables (factors)
go here
7) Your interval
independent
variables
(covariates) go
here
8) Click on
‘Statistics…’
11. Multinomial Logistic Regression in
SPSS VII
9) Select ‘Information Criteria’, ‘Cell
probabilities’, ‘Classification table’
and ‘Goodness-of-fit’
Note that some options are already
selected – leave them as they are
10) Click ‘Continue’
13. Multinomial Logistic Regression in
SPSS IX
12) Select ‘Estimated
response probabilities’,
‘Predicted category’,
‘Predicted category
probability’ and ‘Actual
category probability’
These values will be saved
as variables on the
datasheet for later analysis
Ignore this option as we
are not interested in
exporting the model
13) Click ‘Continue’
15. Model Interpretation I
Case Processing Summary
N
Marginal
Percentage
Education Level - 2000 (3
groups)
HIGHER EDUCAT 1942 32.2%
OTHER QUAL 2575 42.7%
NONE 1515 25.1%
Manual or non manual Non-Manual 3558 59.0%
Manual 2474 41.0%
Ethnicity White 5760 95.5%
Non-White 272 4.5%
Marital status married 3043 50.4%
cohabiting&SSC 547 9.1%
single 1291 21.4%
widowed 277 4.6%
div/sep 874 14.5%
See friends Weekly 4620 76.6%
Monthly 871 14.4%
Less Than Monthly 429 7.1%
Not In Last Year 112 1.9%
contacted MP no 5344 88.6%
yes 688 11.4%
Valid 6032 100.0%
Missing 2189
Total 8221
Subpopulation 1511
a
a. The dependent variable has only one value observed in 846 (56.0%)
subpopulations.
This table tells us the
frequencies and percentages of
respondents from the dataset
that fall into each category for all
the categorical variables
(including the dependent)
We need to look out for low
frequencies – but this shouldn’t
be a problem if you’ve chosen
your variables rigorously!
Notice the number of valid cases
– i.e. cases without missing data
(remember the assumptions!)
16. Model Interpretation II
Model Fitting Information
Model Model Fitting Criteria Likelihood Ratio Tests
AIC BIC
-2 Log
Likelihood Chi-Square df Sig.
Intercept Only 6820.102 6833.512 6816.102
Final 5074.633 5235.549 5026.633 1789.468 22 .000
This table tells us whether our
model is a significant improvement
on the ‘intercept only’ (null) model
p<0.05 means rejecting the null hypothesis
that there is no difference between the
‘intercept only’ and populated model
17. Model Interpretation III
Goodness-of-Fit
Chi-Square df Sig.
Pearson 3211.136 2998 .003
Deviance 3114.276 2998 .068
Pseudo R-Square
Cox and Snell .257
Nagelkerke .291
McFadden .138
The pseudo R-square tells us how much
of the variance in the dependent variable
is explained by the model – low values
are normal in logistic regression (think
about variance in dependent!)
Both of these statistics test
how well the model fits that
data (expected and actual
values) and p<0.05 means that
there is a significant difference
between the two i.e. the model
is not a good fit!
According to the Pearson statistic
the model is a bad fit, but the
Deviance statistic suggests
otherwise (not not by much!)
This could be due to low frequencies in
crosstabs or ‘overdispersion’ (see Field
2009:308) – subjective judgment…
18. Model Interpretation V
Likelihood Ratio Tests
Effect Model Fitting Criteria Likelihood Ratio Tests
AIC of
Reduced
Model
BIC of
Reduced
Model
-2 Log
Likelihood of
Reduced
Model Chi-Square df Sig.
Intercept 5074.633 5235.549 5026.633 .000 0 .
age 5605.268 5752.774 5561.268 534.634 2 .000
manual2 6018.795 6166.302 5974.795 948.162 2 .000
Ethnic2 5074.901 5222.408 5030.901 4.268 2 .118
marital2 5087.697 5194.974 5055.697 29.064 8 .000
seefrnd2 5075.437 5196.124 5039.437 12.804 6 .046
cntctmp 5096.844 5244.350 5052.844 26.210 2 .000
The chi-square statistic is the difference in -2 log-likelihoods between the final model and a
reduced model. The reduced model is formed by omitting an effect from the final model. The null
hypothesis is that all parameters of that effect are 0.
This table tells us which independent variables had a significant effect in our model
Ethnicity
(‘Ethnic2’) is the
only predictor
that does not
significantly
effect the
highest
educational
qualification of a
respondent in
the model
19. Model Interpretation VI
Parameter Estimates
Education Level - 2000 (3 groups)
a
B Std. Error Wald df Sig. Exp(B)
95% Confidence Interval for
Exp(B)
Lower Bound Upper Bound
HIGHER
EDUCAT
Intercept -.988 .372 7.063 1 .008
age .000 .003 .028 1 .867 1.000 .994 1.005
[manual2=1.00] 1.282 .073 309.342 1 .000 3.602 3.123 4.156
[manual2=2.00] 0
b
. . 0 . . . .
[Ethnic2=1.00] -.298 .146 4.181 1 .041 .742 .558 .988
[Ethnic2=2.00] 0
b
. . 0 . . . .
[marital2=1.00] .113 .098 1.340 1 .247 1.120 .925 1.356
[marital2=2.00] .268 .134 3.992 1 .046 1.307 1.005 1.701
[marital2=3.00] .123 .114 1.156 1 .282 1.130 .904 1.413
[marital2=4.00] -.310 .207 2.242 1 .134 .734 .489 1.100
[marital2=5.00] 0
b
. . 0 . . . .
[seefrnd2=1.00] .204 .301 .461 1 .497 1.226 .680 2.211
[seefrnd2=2.00] .193 .309 .391 1 .532 1.213 .662 2.222
[seefrnd2=3.00] .305 .321 .906 1 .341 1.357 .724 2.543
[seefrnd2=4.00] 0
b
. . 0 . . . .
[cntctmp=0] -.249 .094 6.993 1 .008 .780 .649 .938
[cntctmp=1] 0
b
. . 0 . . . .
Because we are comparing both ‘Higher Education’ and ‘No Qualification’ with the
reference category ‘Other Qualification’ we are given two parameter estimate tables
This is the parameter estimates table comparing respondents with a ‘Higher Education
Qualification’ with respondents with a ‘Other Qualification’
20. Model Interpretation VII
NONE Intercept -2.705 .357 57.555 1 .000
age .065 .003 428.739 1 .000 1.068 1.061 1.074
[manual2=1.00] -1.184 .074 255.802 1 .000 .306 .265 .354
[manual2=2.00] 0
b
. . 0 . . . .
[Ethnic2=1.00] -.164 .182 .806 1 .369 .849 .594 1.214
[Ethnic2=2.00] 0
b
. . 0 . . . .
[marital2=1.00] -.215 .100 4.618 1 .032 .806 .663 .981
[marital2=2.00] -.195 .165 1.384 1 .239 .823 .595 1.138
[marital2=3.00] .093 .125 .550 1 .458 1.097 .859 1.401
[marital2=4.00] .062 .174 .128 1 .721 1.064 .757 1.496
[marital2=5.00] 0
b
. . 0 . . . .
[seefrnd2=1.00] -.468 .240 3.811 1 .051 .627 .392 1.002
[seefrnd2=2.00] -.664 .255 6.781 1 .009 .515 .312 .848
[seefrnd2=3.00] -.273 .270 1.018 1 .313 .761 .448 1.293
[seefrnd2=4.00] 0
b
. . 0 . . . .
[cntctmp=0] .392 .121 10.525 1 .001 1.480 1.168 1.875
[cntctmp=1] 0
b
. . 0 . . . .
a. The reference category is: OTHER QUAL.
b. This parameter is set to zero because it is redundant.
This is the parameter estimates table comparing respondents with a ‘No Qualification’
with respondents with a ‘Other Qualification’
The interpretation of results is exactly the same as for binary logistic regression – SPSS
doesn’t provide a parameter coding table, so you need to work this out manually
21. Model Interpretation VIII
Classification
Observed Predicted
HIGHER
EDUCAT OTHER QUAL NONE Percent Correct
HIGHER EDUCAT 1405 402 135 72.3%
OTHER QUAL 1217 943 415 36.6%
NONE 319 428 768 50.7%
Overall Percentage 48.8% 29.4% 21.9% 51.7%
Finally you are given a classification table that tells you how well the predictive model
performed – look for misclassifications and ask yourself why… you can always run a
new and improved model!
The model has trouble with ‘Other Qualification’ respondents – it
tries to assign many of the to ‘Higher Education’
51.7% correctly predicted is okay – but the model is best at predicting respondents
with ‘Higher Education’ qualifications… can you do better?
22. In Class Exercise
• Work in small groups to interpret the results of my model
(the odds ratios) for ‘manual2’ and ‘seefrnd2’
• Remember to…
– Look for significance
– Negative or positive coefficient?
– Interpret the Exp(B) (odds ratio)
– We are not comparing ‘No Qual’ with ‘HE Qual’
You need to know that…
[‘manual2’ = 1.00] refers to non-manual respondent
[‘manual2’ = 2.00] refers to manual respondent (reference category)
[‘seefrnd2’ = 1.00] refers to seeing friends weekly
[‘seefrnd2’ = 2.00] refers to seeing friends monthly
[‘seefrnd2’ = 3.00] refers to seeing friends less than monthly
[‘seefrnd2’ = 4.00] refers to seeing friends not in the last year (reference category)
23. Writing-Up I
• Report the test results from the output – always give the test statistic, degrees of
freedom (if appropriate) and the p-value
• Always explain what the test result means for your model
• Remember – if your model doesn’t fit then there’s no point in writing about it!
• Report which coefficients are not significant – offer an explanation as to why (why
were your hypotheses and bivariate tests wrong?... complexity of interactions?)
• Regarding reporting odds ratios:
– Report whether the odds increase or decrease
– Give the odds ratio (or percentage point increase if you prefer)
– Give the degrees of freedom
– Give the Wald statistic
• Remember to say ‘all other things being equal’ every now and again!
24. Writing-Up II
EXAMPLE:
The coefficient for the variable ‘manual2’ (whether a respondent has a manual or
non-manual occupation) was significant for both respondents with a higher education
and no qualification.
Non-manual respondents were much more likely to have a higher education than an
‘other’ qualification than manual respondents (odds = 3.6, 1 d.f., Wald = 309.34) all
other things being equal.
Also, non-manual respondents were much less likely not to have any qualifications
than to have an ‘other’ qualification than manual respondents (odds = 0.31, 1 d.f.,
Wald = 255.80) all other things being equal.
Although the language is awkward we can summarise by saying that respondents with
higher education qualifications are more likely to have non-manual jobs than
respondents with ‘other’ qualifications. Also, respondents with no qualifications are
less likely to have non-manual jobs than respondents with ‘other’ qualifications. Both
of these statements are made in reference to respondents who have manual
occupations (the dummy ref cat.) and with ‘other’ qualifications (DV ref cat.)
25. Summary
• Binary and multinomial models are very
similar, but notice the subtle differences
• Again interpretation of the coefficients and
Exp(B) are the tricky bit
• The models are very powerful, even when
saying ‘more likely’ or ‘less likely’
26. Workshop Task
• Run a multinomial logistic regression model with the dependent variable
‘edlev7’
• See if you can get a better prediction rate than me!
• Use everything you’ve learnt over the past weeks, starting with the proper
procedure for variable selection
• Use these slides to check that the model works (follow my step-by-step
guide to operation and interpretation)
• Interpret the odds ratios and draw some conclusions about your model
• If your model doesn’t work then work in pairs
• This technique is advanced, so ask for help if you are unsure