Get to know more about Prescriptive, Predictive analytics like market basket analytics plus the data structure and variables to apply the analytics.
for more info you can ping me at google #bobrupakroy
Detailed discussion about the types of statistics form Measures of Central Tendency, Measures of Dispersion, Skewness, Kurtosis, Probability Distributions and much more with their uses cases
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Multiple sample test - Anova, Chi-square, Test of association, Goodness of Fit Rupak Roy
Detailed demonstration of Multiple Sample Test like Analysis of Variance (ANOVA), kinds of ANOVA One Way, Two Way, Chi-square with their assumptions and applications using excel, and much more.
Let me know if anything is needed. Happy to help. ping @ #bobrupakroy
Data Preparation with the help of Analytics MethodologyRupak Roy
Get involved with the steps of data preparation and data assessment using widely used methodologies for machine learning data science modeling.
Let me know if anything is required, ping me at google #bobrupakroy
Don't get confused with Summary Statistics. Learn in-depth types of summary statistics from measures of central tendency, measures of dispersion and much more.
Let me know if anything is required. ping me at google #bobrupakroy
Types of Probability Distributions - Statistics IIRupak Roy
Get to know in detail the definitions of the types of probability distributions from binomial, poison, hypergeometric, negative binomial to continuous distribution like t-distribution and much more.
Let me know if anything is required. Ping me at google #bobrupakroy
Inferential statistics use samples to make generalizations about populations. It allows researchers to test theories designed to apply to entire populations even though samples are used. The goal is to determine if sample characteristics differ enough from the null hypothesis, which states there is no difference or relationship, to justify rejecting the null in favor of the research hypothesis. All inferential tests examine the size of differences or relationships in a sample compared to variability and sample size to evaluate how deviant the results are from what would be expected by chance alone.
Machine Learning Decision Tree AlgorithmsRupak Roy
Details discussion about the Tree Algorithms like Gini, Information Gain, Chi-square for categorical and Reduction in variance for continuous variable. Let me know if anything is required. Happy to help. Enjoy machine learning! #bobrupakroy
Detailed discussion about the types of statistics form Measures of Central Tendency, Measures of Dispersion, Skewness, Kurtosis, Probability Distributions and much more with their uses cases
What is regression / Quantification of the impact ?Rupak Roy
Regression analysis is a statistical technique used to quantify the relationship between variables and determine the magnitude and direction of their effect. It can be used to understand how independent variables like price discounts influence dependent variables like sales. The document discusses how regression analysis would allow a hospital employee to determine which factors like diet, workload, medication, and age most influence patients' diabetes diagnoses. It notes regression analysis provides both multiple factor analysis and a quantifiable measure of each factor's impact. The most common types of regression analysis are linear and logistic regression.
Multiple sample test - Anova, Chi-square, Test of association, Goodness of Fit Rupak Roy
Detailed demonstration of Multiple Sample Test like Analysis of Variance (ANOVA), kinds of ANOVA One Way, Two Way, Chi-square with their assumptions and applications using excel, and much more.
Let me know if anything is needed. Happy to help. ping @ #bobrupakroy
Data Preparation with the help of Analytics MethodologyRupak Roy
Get involved with the steps of data preparation and data assessment using widely used methodologies for machine learning data science modeling.
Let me know if anything is required, ping me at google #bobrupakroy
Don't get confused with Summary Statistics. Learn in-depth types of summary statistics from measures of central tendency, measures of dispersion and much more.
Let me know if anything is required. ping me at google #bobrupakroy
Types of Probability Distributions - Statistics IIRupak Roy
Get to know in detail the definitions of the types of probability distributions from binomial, poison, hypergeometric, negative binomial to continuous distribution like t-distribution and much more.
Let me know if anything is required. Ping me at google #bobrupakroy
Inferential statistics use samples to make generalizations about populations. It allows researchers to test theories designed to apply to entire populations even though samples are used. The goal is to determine if sample characteristics differ enough from the null hypothesis, which states there is no difference or relationship, to justify rejecting the null in favor of the research hypothesis. All inferential tests examine the size of differences or relationships in a sample compared to variability and sample size to evaluate how deviant the results are from what would be expected by chance alone.
Machine Learning Decision Tree AlgorithmsRupak Roy
Details discussion about the Tree Algorithms like Gini, Information Gain, Chi-square for categorical and Reduction in variance for continuous variable. Let me know if anything is required. Happy to help. Enjoy machine learning! #bobrupakroy
Get involved with the steps of Kmeans and Hierarchical clustering and also understand how scaling affects the clustering with Agglomerative and Divise modes.
Do let me know if anything is required. Ping me at google #bobrupakroy
This document discusses descriptive statistics and how they are used to summarize and describe data. Descriptive statistics allow researchers to analyze patterns in data but cannot be used to draw conclusions beyond the sample. Key aspects covered include measures of central tendency like mean, median, and mode to describe the central position in a data set. Measures of dispersion like range and standard deviation are also discussed to quantify how spread out the data values are. Frequency distributions are described as a way to summarize the frequencies of individual data values or ranges.
very detailed illustration of Log of Odds, Logit/ logistic regression and their types from binary logit, ordered logit to multinomial logit and also with their assumptions.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Is the Data Scaled, Ordinal, or Nominal Proportional?Ken Plummer
The document discusses different types of data used in statistical analysis: scaled, ordinal, and nominal data. Scaled data represents quantities where the intervals between values are equal, such as temperature or test scores. Ordinal data uses numbers to represent relative rankings, like placing in an event, but the intervals are not equal. The document uses examples to illustrate the properties of scaled and ordinal data and explains how to determine if a given data set is scaled or ordinal.
Understanding and interpreting the report findingsHoem Seiha
Here is a 150-word paragraph interpreting the given scatter graph:
This scatter graph shows the relationship between the number of study hours and number of sleeping hours for a group of students. There appears to be a strong negative correlation between the two variables. As the number of study hours increases, the number of sleeping hours decreases, and vice versa. Data points are clustered closely around the trend lines, indicating a high degree of correlation. Most students who studied for 2 hours slept for around 10 hours, those who studied for 4 hours slept 9 hours, 6 hours of study correlated with 8 hours of sleep, and so on. The trend lines both have a slope pointing downward to the right, confirming that more time spent studying is associated with less time spent
PG STAT 531 Lecture 4 Exploratory Data AnalysisAashish Patel
The document discusses exploratory data analysis (EDA). It defines EDA as an approach to maximize understanding of a data set by using graphical and quantitative techniques to uncover structure, extract important variables, detect outliers, test assumptions, and develop models. The document contrasts EDA with classical and Bayesian data analysis approaches and discusses specific graphical and quantitative techniques used in EDA like histograms, mean plots, hypothesis testing, and probability distributions.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
The document provides an overview of statistics for data science. It introduces key concepts including descriptive versus inferential statistics, different types of variables and data, probability distributions, and statistical analysis methods. Descriptive statistics are used to describe data through measures of central tendency, variability, and visualization techniques. Inferential statistics enable drawing conclusions about populations from samples using hypothesis testing, confidence intervals, and regression analysis.
Detailed talk about Random Forest and its statistical techniques for classification and regression analysis with termonologies like Out of Bag (OOB) estimate of performance, Bias Variance Trade off, and model validation metrics.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakro
Statistics is the study of collecting, analyzing, and presenting quantitative data. It involves planning data collection through surveys and experiments, as well as analyzing the data using measures of central tendency like the mean, median, and mode. The mean is the average value found by summing all values and dividing by the total number of values. The median is the middle value when data is arranged in order. The mode is the most frequent value. Statistics has limitations as it does not study qualitative data or individuals, and statistical laws may not be universally applicable. Frequency distributions organize data values and their frequencies to understand patterns in the data.
Statistical analysis and its applications can be used in many fields including pharmaceutical research, clinical trials, public health, epidemiology, genetics, and demographics. Some key uses of statistics include evaluating drug effects, comparing drug treatments, exploring associations between diseases and risk factors, and analyzing clinical trial and genomics data. Measures of central tendency, dispersion, and other statistical methodologies help researchers draw conclusions from collected data.
Linear regression is an approach for modeling the relationship between one dependent variable and one or more independent variables.
Algorithms to minimize the error are
OLS (Ordinary Least Square)
Gradient Descent and much more.
Let me know if anything is required. Ping me at google #bobrupakroy
This document contains lecture slides about statistics for describing, exploring, and comparing data. It discusses measures of center such as the mean, median, and mode. It also discusses variance and standard deviation as measures of spread. Additional topics covered include finding the mode, determining if a value is unusually high or low based on the mean and standard deviation, calculating percentiles, and comparing the detail provided by different graphic displays of data.
A confidence interval provides a range of values that is likely to include an unknown population parameter, based on a given confidence level. A 95% confidence level means there is a 95% chance the interval contains the true population parameter. Confidence intervals are useful because they allow researchers to account for sampling error/variability and make inferences about populations based on sample data. The higher the confidence level, the wider the interval needs to be to achieve that level of confidence.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
This document provides an overview of descriptive statistics, inferential statistics, and regression analysis using PASW Statistics software. It discusses topics such as frequency analysis, measures of central tendency, hypothesis testing, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document is divided into multiple parts that cover opening and manipulating data files, descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. It also discusses importing/exporting data and using scripts in PASW Statistics.
Data science notes for ASDS calicut 2.pptxswapnaraghav
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
This document provides an overview of quantitative data analysis and statistical tests. It discusses research questions, variables, descriptive and inferential statistics. Common statistical tests are explained like the Mann-Whitney U test, Spearman rank correlation, Kruskal-Wallis test, t-test, Pearson correlation, ANOVA, and chi-square test. Factors to consider when selecting a statistical test are highlighted like level of data, number of groups, independent or related groups, and data distribution. The document emphasizes keeping analyses simple and statistics in context of discussion.
(1) The document discusses different types of data that can be used to compare exposure groups in a cohort study: binary, categorical, and continuous.
(2) Binary measures classify subjects into two groups (yes/no), categorical measures classify subjects into two or more ordered or unordered groups, and continuous measures use a scale with equal distances between units like age or blood pressure.
(3) Examples are given of studies measuring palm pilot exposure and brain rot using binary, categorical, and continuous scales.
Get involved with the steps of Kmeans and Hierarchical clustering and also understand how scaling affects the clustering with Agglomerative and Divise modes.
Do let me know if anything is required. Ping me at google #bobrupakroy
This document discusses descriptive statistics and how they are used to summarize and describe data. Descriptive statistics allow researchers to analyze patterns in data but cannot be used to draw conclusions beyond the sample. Key aspects covered include measures of central tendency like mean, median, and mode to describe the central position in a data set. Measures of dispersion like range and standard deviation are also discussed to quantify how spread out the data values are. Frequency distributions are described as a way to summarize the frequencies of individual data values or ranges.
very detailed illustration of Log of Odds, Logit/ logistic regression and their types from binary logit, ordered logit to multinomial logit and also with their assumptions.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Is the Data Scaled, Ordinal, or Nominal Proportional?Ken Plummer
The document discusses different types of data used in statistical analysis: scaled, ordinal, and nominal data. Scaled data represents quantities where the intervals between values are equal, such as temperature or test scores. Ordinal data uses numbers to represent relative rankings, like placing in an event, but the intervals are not equal. The document uses examples to illustrate the properties of scaled and ordinal data and explains how to determine if a given data set is scaled or ordinal.
Understanding and interpreting the report findingsHoem Seiha
Here is a 150-word paragraph interpreting the given scatter graph:
This scatter graph shows the relationship between the number of study hours and number of sleeping hours for a group of students. There appears to be a strong negative correlation between the two variables. As the number of study hours increases, the number of sleeping hours decreases, and vice versa. Data points are clustered closely around the trend lines, indicating a high degree of correlation. Most students who studied for 2 hours slept for around 10 hours, those who studied for 4 hours slept 9 hours, 6 hours of study correlated with 8 hours of sleep, and so on. The trend lines both have a slope pointing downward to the right, confirming that more time spent studying is associated with less time spent
PG STAT 531 Lecture 4 Exploratory Data AnalysisAashish Patel
The document discusses exploratory data analysis (EDA). It defines EDA as an approach to maximize understanding of a data set by using graphical and quantitative techniques to uncover structure, extract important variables, detect outliers, test assumptions, and develop models. The document contrasts EDA with classical and Bayesian data analysis approaches and discusses specific graphical and quantitative techniques used in EDA like histograms, mean plots, hypothesis testing, and probability distributions.
Detailed discussion about decision tree regressor and the classifier with finding the right algorithm to split
Let me know if anything is required. Ping me at google #bobrupakroy
The document provides an overview of statistics for data science. It introduces key concepts including descriptive versus inferential statistics, different types of variables and data, probability distributions, and statistical analysis methods. Descriptive statistics are used to describe data through measures of central tendency, variability, and visualization techniques. Inferential statistics enable drawing conclusions about populations from samples using hypothesis testing, confidence intervals, and regression analysis.
Detailed talk about Random Forest and its statistical techniques for classification and regression analysis with termonologies like Out of Bag (OOB) estimate of performance, Bias Variance Trade off, and model validation metrics.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakro
Statistics is the study of collecting, analyzing, and presenting quantitative data. It involves planning data collection through surveys and experiments, as well as analyzing the data using measures of central tendency like the mean, median, and mode. The mean is the average value found by summing all values and dividing by the total number of values. The median is the middle value when data is arranged in order. The mode is the most frequent value. Statistics has limitations as it does not study qualitative data or individuals, and statistical laws may not be universally applicable. Frequency distributions organize data values and their frequencies to understand patterns in the data.
Statistical analysis and its applications can be used in many fields including pharmaceutical research, clinical trials, public health, epidemiology, genetics, and demographics. Some key uses of statistics include evaluating drug effects, comparing drug treatments, exploring associations between diseases and risk factors, and analyzing clinical trial and genomics data. Measures of central tendency, dispersion, and other statistical methodologies help researchers draw conclusions from collected data.
Linear regression is an approach for modeling the relationship between one dependent variable and one or more independent variables.
Algorithms to minimize the error are
OLS (Ordinary Least Square)
Gradient Descent and much more.
Let me know if anything is required. Ping me at google #bobrupakroy
This document contains lecture slides about statistics for describing, exploring, and comparing data. It discusses measures of center such as the mean, median, and mode. It also discusses variance and standard deviation as measures of spread. Additional topics covered include finding the mode, determining if a value is unusually high or low based on the mean and standard deviation, calculating percentiles, and comparing the detail provided by different graphic displays of data.
A confidence interval provides a range of values that is likely to include an unknown population parameter, based on a given confidence level. A 95% confidence level means there is a 95% chance the interval contains the true population parameter. Confidence intervals are useful because they allow researchers to account for sampling error/variability and make inferences about populations based on sample data. The higher the confidence level, the wider the interval needs to be to achieve that level of confidence.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
This document provides an overview of descriptive statistics, inferential statistics, and regression analysis using PASW Statistics software. It discusses topics such as frequency analysis, measures of central tendency, hypothesis testing, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document is divided into multiple parts that cover opening and manipulating data files, descriptive statistics, tests of significance, regression analysis, and chi-square/ANOVA. It also discusses importing/exporting data and using scripts in PASW Statistics.
Data science notes for ASDS calicut 2.pptxswapnaraghav
Data science involves both statistics and practical hacking skills. It is the engineering of data - applying tools and theoretical understanding to data in a practical way. Statistical modeling is the process of using mathematical models to analyze and understand data in order to make general predictions. There are several statistical modeling techniques including linear regression, classification, resampling, non-linear models, tree-based methods, and neural networks. Unsupervised learning identifies patterns in data without pre-existing categories by techniques like clustering. Time series forecasting predicts future values based on patterns in historical time series data.
This document provides an overview of quantitative data analysis and statistical tests. It discusses research questions, variables, descriptive and inferential statistics. Common statistical tests are explained like the Mann-Whitney U test, Spearman rank correlation, Kruskal-Wallis test, t-test, Pearson correlation, ANOVA, and chi-square test. Factors to consider when selecting a statistical test are highlighted like level of data, number of groups, independent or related groups, and data distribution. The document emphasizes keeping analyses simple and statistics in context of discussion.
(1) The document discusses different types of data that can be used to compare exposure groups in a cohort study: binary, categorical, and continuous.
(2) Binary measures classify subjects into two groups (yes/no), categorical measures classify subjects into two or more ordered or unordered groups, and continuous measures use a scale with equal distances between units like age or blood pressure.
(3) Examples are given of studies measuring palm pilot exposure and brain rot using binary, categorical, and continuous scales.
(1) The document discusses different types of data that can be used to compare exposure groups in a cohort study: binary, categorical, and continuous.
(2) Binary measures classify subjects into two groups (yes/no), categorical measures classify subjects into two or more ordered or unordered groups, and continuous measures use numerical values that can be averaged.
(3) Examples are given of studies measuring palm pilot exposure and brain rot using each type of data: binary (yes/no to palm pilot ownership and brain rot), categorical (severity of brain rot), and continuous (Glasgow Coma scores).
This document provides an outline for a course on probability and statistics. It begins with an introduction to statistics, including definitions and general uses. It then discusses topics that will be covered, such as measures of central tendency, probability, discrete and continuous distributions, and hypothesis testing. References for textbooks are also provided. The document differentiates between descriptive and inferential statistics. It defines key statistical concepts such as population, sample, variable, and the different variable types. It also covers the different scales of measurement for variables. An assignment is included asking students to list statisticians' contributions, give a real-life application example, and define independent and dependent variables.
Bivariate RegressionRegression analysis is a powerful and comm.docxhartrobert670
Bivariate Regression
Regression analysis is a powerful and commonly used tool in business research. One important step in regression is to determine the dependent and independent variable(s).
In a bivariate regression, which variable is the dependent variable and which one is the independent variable?
· What does the intercept of a regression tell? What does the slope of a regression tell?
· What are some of the main uses of a regression?
Provide an example of a situation wherein a bivariate regression would be a good choice for analyzing data.
Justify your answers using examples and reasoning. Comment on the postings of at least two peers and state whether you agree or disagree with their views.
Types of Regression Analyses
There are two major types of regression analysis—simple and multiple regression analysis. Both types consist of dependent and independent variables. Simple linear regression has two variables—dependent and independent. Multiple regression consists of dependent variable and two or more independent variables.
· How does a multiple regression compare with a simple linear regression?
· What are the various ways to determine what variables should be included in a multiple regression equation?
· Compare and contrast the following processes: forward selection, backward elimination, and stepwise selection.
Justify your answers using examples and reasoning.
Critical Analysis
Critical analysis involves thinking about what you're reading and interpreting it and evaluating it.
Critical analysis of the books, papers, articles, and research that you read for your classes is an important skill. It is also an important skill in the workplace. Generally speaking, when you engage in critical analysis, you do the following things:
Critical Analysis Principles
Example Questions or Statements
Identify and challenge starting assumptions
Questions:
Did the authors base their conclusions on the appropriate facts? Did the author consider the social conditions of the appropriate time period? Did the author use the appropriate resources to adequately address the question?
Example:
The author used widely-held social beliefs in 2007 to explain social changes that occurred in 1910.
Distinguish facts from opinions, and distinguish objectivity from bias
Questions:
Has the author stated the facts from a research study, or did he just give us his opinion? Has the author explained the situation fairly? Did the author allow her personal opinion or involvement to prejudice her explanation and cloud her judgment?
Example:
This drug has been reported to be an effective treatment. However, all the reports come from the company that created and is selling the drug. There are no independent reports from uninvolved parties that support this claim.
Make inferences from the facts
Questions:
What do these findings mean? What are the implications of these findings? Do these findings impact other areas or concepts? Did the author interpret the findings in a reas ...
This chapter discusses research methods and procedures. It describes the descriptive method of research, which involves observing and describing phenomena without influencing it. Common data collection methods like interviews and questionnaires are discussed. The document also covers developing a good research instrument, sampling design including different probability sampling techniques, and guidelines for selecting appropriate statistical analysis procedures.
When to use, What Statistical Test for data Analysis modified.pptxAsokan R
This document discusses choosing the appropriate statistical test for data analysis. It begins by defining key terminology like independent and dependent variables. It then discusses the different types of variables, including quantitative, categorical, and their subtypes. Hypothesis testing and its key steps are explained. The document outlines assumptions that statistical tests make and categorizes common parametric and non-parametric tests. It provides guidance on choosing a test based on the research question, data structure, variable type, and whether the data meets necessary assumptions. Specific statistical tests are matched to questions about differences between groups, association between variables, and agreement between assessment techniques.
This document provides an overview of psychological research. It defines research as a careful, systematic study to establish facts or principles. Psychology is defined as the study of mental processes and behavior. There are three main types of psychological research: correlational research, descriptive research, and experimental research. Researchers use tools like questionnaires, interviews, observation, and checklists. The purpose of research is to describe behavior, understand why events occur, and apply knowledge to problems.
This lecture will help Research scholars at the starting of their research issues regarding definitions of variables, what is theory and creating a sapling map..
Magindren Kuppusamy is a certified project management and big data trainer with qualifications including a PMP certification and MBA. They have received several awards for their work including an Asia Pacific Entrepreneurship Award. Their training covers topics such as big data analytics, data visualization, and data storytelling over three days. Big data analytics involves examining large datasets to uncover hidden patterns, correlations, market trends, and customer preferences that can help organizations make business decisions. Correlations refer to relationships between two or more variables in data, which can be positive, negative, zero, or spurious. Market trends analyze past market behavior and consumer preferences to provide insights.
This document discusses key concepts related to correlations, t scores, and inferential statistics. It defines populations and samples, and explains that samples are used to make generalizations about populations. It also discusses variables, data, and different types of research methods including correlational and experimental designs. Specifically, it explains what correlations measure, how to interpret positive, negative, and no correlations. It emphasizes that correlation does not imply causation and discusses reasons for this. Finally, it introduces t scores as a way to standardize comparisons between distributions.
This document discusses different types of variables and research designs. It defines constructs, indicators, and operational definitions. It also describes different types of variables like independent, dependent, attribute and extraneous variables. Finally, it explains quasi-experimental designs like non-equivalent groups, interrupted time series, and regression discontinuity designs. It also covers single-case designs like A-B-A, multiple baseline, and changing criterion designs. The document provides examples and diagrams to illustrate these research concepts and designs.
This document provides an overview of key concepts in statistics including:
- What statistics is and its two branches of descriptive and inferential statistics
- Key terms like population, sample, parameter, statistic, individual, and variable
- Types of variables including qualitative, quantitative discrete, and quantitative continuous
- Common sampling methods like simple random sampling, stratified sampling, systematic sampling, and cluster sampling
- Examples are provided to demonstrate how to identify and define the terms for different statistical studies
The document discusses hypotheses in research. A hypothesis is a testable statement about the relationship between two variables. Researchers propose a null hypothesis, which states there is no relationship between the variables, and an alternative or experimental hypothesis, which predicts a relationship. Statistical tests are used to analyze data and determine whether to reject the null hypothesis in favor of the alternative hypothesis. The document provides examples of different types of hypotheses and statistical tests used, including t-tests and z-tests.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and hypothesis testing. The document provides examples of how statistics is used in various fields. It also defines key statistical concepts like population and sample, variables, and different scales of measurement. Finally, it discusses data collection methods and ways to represent data through tables and graphs.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of each variable scale and ends with assignments for students.
This document provides an outline for a course on probability and statistics. It begins with an introduction to statistics, including definitions and general uses. It then covers topics like measures of central tendency, probability, discrete and continuous distributions, and hypothesis testing. References for textbooks on statistics and counterexamples in probability are also provided. Assignments ask students to list contributors to statistics, apply statistics in real life, define independent and dependent variables, and understand scales of measurement. Methods of data collection, tabular and graphical representation of data, and measures of central tendency and location are also discussed.
This document provides an outline for a course on probability and statistics. It begins with an introduction to key concepts like measures of central tendency, dispersion, correlation, and probability distributions. It then lists common probability distributions and the textbook and references used. Later sections define important statistical terms like population, sample, variable types, data collection methods, and ways of presenting data through tables and graphs. It provides examples of how statistics is used and ends with examples of different variable scales.
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Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
2. There are three types of analysis that really
support a business.
Prescriptive – This type of analysis reveals what actions should be
taken. This is the most valuable kind of analysis and usually results in
rules and recommendations for the next steps.
For example: If you have a flood data then you analyze the data,
why? and what? are the reasons of floods and the losses related .
Assume that the data says its the drainage systems that is already
jam or block, we can actually fix them and prevent the over flood
from happening next time by analyzing and providing a conclusion
and/or a recommendation
Predictive – It is an analysis of likely or similar scenarios of what
might happen. The outcomes are usually a predictive forecast.
For example: Flood data it simply tells you what will be the
outcome if the flood continues the same, or contrary that will be a
probability to be damage by flood data after taking the
necessarily preventive steps.
Rupak Roy
3. Descriptive – it is what is happening now
by giving you a quick peek of the new
incoming data to be able to describe it.
For example: The frequency of median ,
maximum, minimum of the flood data will
simply describes the summary of the
data.
Rupak Roy
4. Applicable examples of predictive analytics:
If a person is buying a particular product then
this person is likely to buy another product
related to that particular product.
This is often used in retail markets which is also
known as, market basket analysis , allowing to
make predictions on probabilities for a
particular product and related to other similar
products.
Rupak Roy
5. Market Basket Analysis
Is a modeling technique based on the
theory that if you buy a certain group of
items, you are more (or less) likely to buy
another group of items. For example,
people who buy flour and casting sugar,
also tend to buy eggs because a high
chance or probability of them that they are
planning to bake a cake.
Rupak Roy
6. Prescriptive Analytics
It is a new kind of analytics that has raised
where it describes, predicts and
recommends what to do.
Prescriptive analytics is also the third and
the final phase of analytics related to both
descriptive analytics and
predictive analytics.
Rupak Roy
8. The Structure of Data
Data can be found in any form and
anything that can be digitized can be
analyzed.
Rupak Roy
9. The data Structure
is organize in a typical
tabular design, data is
made up of rows and
columns .
Each row represents a
record and
observation in the
data and each
column represents
the field of
information.
Each data in the
column represents a
variable .
10. What is a variable ?
“A specific piece of information about an
observation or record in a data set “
Rupak Roy
11. Types of Variables
1. Categorical variable: are the variables
that can be categorized. For example
male/female or good/bad.
2. Continuous variable: are the infinite
number of values that can be collected
for example weight and height or in a
mathematical setting the definition of
infinite number values that can be
generate between 0 to 1 for example
0.01,0.001,0.000001… to infinity.
12. Types of Variables
3. Discrete Variable: is a variable that can
only have certain number of values.
For example: The number of cars in a
parking lot is discrete because a parking lot
can’t only hold so many cars.
4. Independent variable: is a variable that
is not affected by anything or by any other
variables .
Rupak Roy
13. Types of Variables
5. Dependent variable: is a contrary to independent
variable that is reliant on other variables.
Dependent variable vs. Independent variables:
In the direction to determine by: How long a student
sleeps affects test scores?.
The independent variable is the length of time spent
sleeping (since test scores have no effect on the time
spent sleeping)
The dependent variable is the test score(as the time
spent sleeping has affected the test score ).
14. Types of Variables
6. Nominal variables: It is an another name for categorical
variable that holds more than two categories, such as red
/green /blue cars.
7. Ordinal variables: It is similar to a categorical variables, but
there is a clear order. For example: The ranks of income
levels in order from low/medium/high.
8. Dummy variables: It is used in regression analysis when you
want to assign relationships to unconnected categorical
variables.
For example categories like young, middle age and old age,
can be expanded into 3 dummy each describing its
relationships to each other, for instance assign 1 to a variable
if the age is young else 0, the same can be repeated for
another dummy variable if the age is middle than mark it as 1
else 0 and again for the 3rd dummy variable if age is old
assign 1 else 0
15. Types of Variables
9) Indicator variable: It is another way to define
a dummy variable .
10) Binary variable: It is a variable that can have
two values usually 1/0 or yes/no.
11) Derived Variables: Are the variable that are
originates from other variables when
combining individual variables in to a whole
new variable.
For example:(radio + TV ) = Media
Rupak Roy
16. Next summary statistics and their types
with Skewness and Kurtosis .