Correlation testing
•Download as PPT, PDF•
1 like•790 views
The document discusses correlation testing and analyzing bivariate data from laboratory experiments. It describes carrying out methods like linear regression and scatterplots to investigate relationships between two variables. A key aspect is interpreting the results of the correlation to understand the data and determine the strength and limitations of the observed relationships based on the R-squared value from trendlines. The levels of Pass, Merit and Distinction are defined based on correctly understanding and evaluating the correlation results and their validity.
Report
Share
Report
Share
Recommended
Bayes’ theorem and logistic regression
Bayes' theorem describes the relationship between conditional and marginal probabilities. It provides a way to update or revise beliefs based on new evidence. The Naive Bayes classifier applies Bayes' theorem using strong independence assumptions between features to classify documents. Logistic regression predicts categorical dependent variables using independent variables. It is commonly used for binary classification problems in fields like medicine.
9 accuracyassessment
The document discusses accuracy assessment for classifications of remotely sensed data. It describes collecting reference data from sources like aerial photos or GPS to determine ground truth. An error matrix compares the reference data to the classified map to calculate different accuracy metrics like total, user's, and producer's accuracy. Total accuracy alone does not reveal errors for specific classes. The kappa statistic measures agreement after accounting for chance. Both reference data errors and classification strategy errors can impact accuracy, and assessing accuracy helps identify ways to improve future classifications.
Factor Analysis and Correspondence Analysis Composite and Indicator Scores of...
Factor analysis and correspondence analysis can be used to generate composite and indicator scores from Likert scale survey data. This allows researchers to distill survey responses down into relevant information about populations and attitudes. The document demonstrates how to calculate these scores in four steps for both factor analysis and correspondence analysis. It also compares the advantages and disadvantages of each method and how the analysis can help organizations think differently about survey data.
Data Science Meetup: DGLARS and Homotopy LASSO for Regression Models
Short overview of two regression model extensions using differential geometry and homotopy continuation. Case study involves an open-source dataset that can be found on my ResearchGate page, along with the R code used in the analysis. Contains a short reference section for readers interested in learning more about the methods.
Probability and statistics (frequency distributions)
- Frequency distributions organize and summarize data by grouping it into classes and counting the frequency of observations in each class. They can be presented in tables or graphically.
- Common graphical representations include histograms, relative frequency histograms, frequency polygons, and ogives.
- Categorical frequency distributions are used for nominal or ordinal data by tallying the frequency of observations in each category.
Textmining Retrieval And Clustering
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors based on the angle between them. K-means clustering partitions documents into k clusters to minimize intra-cluster similarity, while hierarchical clustering merges clusters in a dendogram based on similarity. The EM algorithm computes maximum likelihood estimates of document distributions. Evaluation of clustering assesses the quality based on intra-class and inter-class similarity.
Statistics and optimization (1)
This document discusses foundational concepts in statistics including descriptive statistics, types of data, populations and samples, variables, and graphical representations of quantitative and qualitative data. It provides examples of forming frequency distributions and histograms for quantitative data, as well as bar graphs and pie charts for qualitative data. Key terms are defined such as parameter, population, sample, continuous and discrete variables. Graphical representations including histograms, bar graphs and pie charts are demonstrated.
Presenting Data
This document provides guidance for students on statistical analysis and presentation of data in their research projects. It recommends keeping statistical analysis basic and focused on what is in the curriculum. For numerical data, it advises presenting tables, graphs, and histograms that are properly labeled and scaled. It discusses comparing mean and median scores and using additional statistics like standard deviation cautiously. The document also provides examples and guidance on presenting relationships between variables using scatter plots, lines of best fit, and r and R2 values to describe correlations.
Recommended
Bayes’ theorem and logistic regression
Bayes' theorem describes the relationship between conditional and marginal probabilities. It provides a way to update or revise beliefs based on new evidence. The Naive Bayes classifier applies Bayes' theorem using strong independence assumptions between features to classify documents. Logistic regression predicts categorical dependent variables using independent variables. It is commonly used for binary classification problems in fields like medicine.
9 accuracyassessment
The document discusses accuracy assessment for classifications of remotely sensed data. It describes collecting reference data from sources like aerial photos or GPS to determine ground truth. An error matrix compares the reference data to the classified map to calculate different accuracy metrics like total, user's, and producer's accuracy. Total accuracy alone does not reveal errors for specific classes. The kappa statistic measures agreement after accounting for chance. Both reference data errors and classification strategy errors can impact accuracy, and assessing accuracy helps identify ways to improve future classifications.
Factor Analysis and Correspondence Analysis Composite and Indicator Scores of...
Factor analysis and correspondence analysis can be used to generate composite and indicator scores from Likert scale survey data. This allows researchers to distill survey responses down into relevant information about populations and attitudes. The document demonstrates how to calculate these scores in four steps for both factor analysis and correspondence analysis. It also compares the advantages and disadvantages of each method and how the analysis can help organizations think differently about survey data.
Data Science Meetup: DGLARS and Homotopy LASSO for Regression Models
Short overview of two regression model extensions using differential geometry and homotopy continuation. Case study involves an open-source dataset that can be found on my ResearchGate page, along with the R code used in the analysis. Contains a short reference section for readers interested in learning more about the methods.
Probability and statistics (frequency distributions)
- Frequency distributions organize and summarize data by grouping it into classes and counting the frequency of observations in each class. They can be presented in tables or graphically.
- Common graphical representations include histograms, relative frequency histograms, frequency polygons, and ogives.
- Categorical frequency distributions are used for nominal or ordinal data by tallying the frequency of observations in each category.
Textmining Retrieval And Clustering
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors based on the angle between them. K-means clustering partitions documents into k clusters to minimize intra-cluster similarity, while hierarchical clustering merges clusters in a dendogram based on similarity. The EM algorithm computes maximum likelihood estimates of document distributions. Evaluation of clustering assesses the quality based on intra-class and inter-class similarity.
Statistics and optimization (1)
This document discusses foundational concepts in statistics including descriptive statistics, types of data, populations and samples, variables, and graphical representations of quantitative and qualitative data. It provides examples of forming frequency distributions and histograms for quantitative data, as well as bar graphs and pie charts for qualitative data. Key terms are defined such as parameter, population, sample, continuous and discrete variables. Graphical representations including histograms, bar graphs and pie charts are demonstrated.
Presenting Data
This document provides guidance for students on statistical analysis and presentation of data in their research projects. It recommends keeping statistical analysis basic and focused on what is in the curriculum. For numerical data, it advises presenting tables, graphs, and histograms that are properly labeled and scaled. It discusses comparing mean and median scores and using additional statistics like standard deviation cautiously. The document also provides examples and guidance on presenting relationships between variables using scatter plots, lines of best fit, and r and R2 values to describe correlations.
Data array and frequency distribution
This document defines key concepts related to data arrays and frequency distributions. It discusses how data can be systematically arranged in arrays and organized into classes for grouped data. Different types of statistical data and concepts like population, sample, ungrouped vs grouped data are also defined. The document concludes by explaining methods to form frequency distributions, calculate class boundaries, relative frequencies, cumulative frequencies, and other metrics.
Lesson 5 data presentation
The document discusses different methods for presenting data, including textual, tabular, and graphical presentations. It provides examples and guidelines for each method, such as describing highlights in a paragraph, organizing values into a table with rows and columns, and using graphs like pie charts to visualize relationships. Frequency distribution tables and histograms are also covered as specialized forms of tabular and graphical presentation used to depict the distribution of quantitative data.
Quantum-Min-Cut/Max-Flow-Based Vertex Importance Ranking
PPT associated with paper submission introducing a quantum computing algorithm for edge importance and vertex ranking in network analysis.
QT1 - 02 - Frequency Distribution
The document discusses frequency distributions and methods for organizing and visualizing data. It defines key terms like population, sample, data array, frequency distribution, relative frequency distribution, classes, histograms, polygons and ogives. It explains how to construct frequency distributions by determining the number and width of classes and calculating the frequency of observations in each class. Visualization methods like histograms and polygons are introduced to illustrate frequency distributions.
Morse-Smale Regression for Risk Modeling
Presentation summarizes main content of Farrelly, C. M. (2017). Extensions of Morse-Smale Regression with Application to Actuarial Science. arXiv preprint arXiv:1708.05712.
Paper was accepted December 2017 by Casualty Actuarial Society.
Frequency Tables, Frequency Distributions, and Graphic Presentation
This document provides an overview of key concepts for describing data through frequency tables, distributions, and graphs. It defines important terms like frequency table, distribution, class, interval and discusses how to organize both qualitative and quantitative data. Guidelines for data collection are provided. Examples are given to demonstrate how to construct frequency tables and distributions and convert them to relative frequencies. Finally, different types of graphs for presenting frequency distributions are described, including histograms, polygons and cumulative distributions.
Organizing-Qualitative-and-Quanti.-data
This document discusses organizing and summarizing qualitative and quantitative data. It defines different types of data such as nominal, ordinal, discrete, and continuous variables. It also discusses frequency distributions, relative frequencies, percentages, bar graphs, and pie charts for organizing qualitative data. For quantitative data, it discusses frequency distributions, means, standard deviations, histograms, and polygons. Overall, the document provides an overview of different methods for organizing, summarizing, and presenting both qualitative and quantitative data.
Quantum generalized linear models
1) Quantum generalized linear models (QGLMs) provide an alternative way to minimize error in statistical models by leveraging the geometry of probability distributions and quantum computing gates.
2) In tests on simulated overdispersed data and a real-world forest fire dataset, QGLMs performed comparably to or better than state-of-the-art machine learning algorithms like random forests and boosted regression.
3) QGLMs have the potential to improve statistical modeling by eliminating the need for link functions and exploiting the underlying geometry of models through quantum computations. This could help extend models to new distributions and problem types.
20151130
The document proposes improvements to distributed graph pattern matching algorithms. It introduces a boundary filter technique that aims to shrink large data graphs by removing boundary nodes. These are nodes that only have one relationship in directed graphs. Experiments show the boundary filter approach significantly reduces running time compared to the original distributed tight simulation algorithm, while also improving accuracy by finding more matching subgraphs. The boundary filtering allows independent evaluation of each vertex and scales well to more complex graph patterns.
High-Dimensional Data Visualization, Geometry, and Stock Market Crashes
Miami Data Science SALON (Nov 2018) talk regarding geometric methods for dimensionality reduction, data visualization, and stock market analysis (India's NSE).
Frequency distribution
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
Exploring Data
This document discusses exploratory data analysis techniques including summary statistics, visualization methods, and online analytical processing (OLAP). It provides examples using the Iris data set to illustrate histogram, box plot, scatter plot, contour plot, matrix plot, parallel coordinates, and OLAP cube and slicing/dicing operations. The goal of exploratory data analysis is to better understand data characteristics through pattern recognition and selecting appropriate tools for preprocessing and analysis.
Describing categorical data one variable
This document discusses organizing and graphing categorical data. It begins by defining frequency distributions and how to construct frequency tables for categorical data. It then discusses relative frequency distributions. The document reviews different types of charts used to display categorical data, including pie charts, bar charts, and Pareto charts. It provides examples of how to construct these charts. Finally, it discusses best practices for data visualization, such as labeling charts clearly and avoiding misleading graphs that violate principles like using area accurately to represent quantities.
slide->title; ?>
The document presents a machine learning method called Prototype Ranking (PR) for selecting stocks. PR uses competitive learning to find representative prototypes from training stock data, then uses k-nearest neighbors to rank and select test stocks. Experiments show PR selects portfolios with higher average returns and risk-adjusted returns than a traditional non-machine learning method, especially for smaller portfolios. PR is well-suited for the noisy and imbalanced nature of stock return data.
Frequency Distribution Table Handout
This is a handout which consists of the basic concepts/terms about Frequency Distribution Table. It also includes the ways on how to properly construct a Frequency Distribution Table.
Chapter 2: Frequency Distribution and Graphs
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
Chapter 2 250110 083240
This document discusses frequency distributions and graphs. It defines frequency distributions as organizing raw data into a table using classes and frequencies. There are three main types: categorical, grouped, and ungrouped. Guidelines are provided for constructing frequency distributions, such as having 5-20 classes of equal width. Common graphs discussed are histograms, frequency polygons, ogives, Pareto charts, time series graphs, and pie charts. These graphs represent frequency distributions in visual formats.
Cluster analysis
根據您提供的資料,我建議戴平台可以採用以下步驟進行集群分析:
1. 選擇運動員所知覺到的教練行為變項,如自主支持、威脅、控制等,作為集群分析的分類變項。
2. 量化這些教練行為,並測量不同運動員對同一教練這些行為的知覺程度,如利用量表進行評分。
Frequency distribution 6
Frequency distribution is used to organize and summarize data by grouping it into classes. It consists of class intervals, frequencies, and other components. An example frequency distribution table is given showing student ratings of teaching quality organized into classes with their frequencies. Frequency distributions can be represented graphically through histograms and pie charts to visualize the data distribution.
Frequency distribution
This document discusses frequency distributions, which summarize data by arranging it into classes and indicating how many observations fall into each class. It describes the key components of a frequency distribution, including the class, class limits, class mark, class interval, class frequency and tally marks. It provides examples of grouped frequency distributions, where the data is organized into groups, and ungrouped distributions, where the raw individual data points are listed. The objectives of a frequency distribution are to estimate population frequencies, facilitate data analysis, and allow computation of statistical measures.
Bsc cs ii dfs u-4 sorting and searching structure
This document discusses hash tables and their use for storing and retrieving data records. It describes how a hash table uses a hash function to map record keys to array indexes, which can cause collisions when different keys hash to the same index. It also covers inserting and searching for records, handling collisions, and deleting records from the hash table.
Probability Distributions
This document provides an overview of quantitative methods for probability distributions. It discusses key concepts like binomial distribution, normal distribution, standard normal distribution, central limit theorem, point estimates, interval estimates, and confidence intervals. Examples are provided to illustrate how to calculate probabilities, means, and confidence intervals for estimating population parameters based on sample data. Key probability distributions and statistical techniques are defined to analyze and make inferences about data.
More Related Content
What's hot
Data array and frequency distribution
This document defines key concepts related to data arrays and frequency distributions. It discusses how data can be systematically arranged in arrays and organized into classes for grouped data. Different types of statistical data and concepts like population, sample, ungrouped vs grouped data are also defined. The document concludes by explaining methods to form frequency distributions, calculate class boundaries, relative frequencies, cumulative frequencies, and other metrics.
Lesson 5 data presentation
The document discusses different methods for presenting data, including textual, tabular, and graphical presentations. It provides examples and guidelines for each method, such as describing highlights in a paragraph, organizing values into a table with rows and columns, and using graphs like pie charts to visualize relationships. Frequency distribution tables and histograms are also covered as specialized forms of tabular and graphical presentation used to depict the distribution of quantitative data.
Quantum-Min-Cut/Max-Flow-Based Vertex Importance Ranking
PPT associated with paper submission introducing a quantum computing algorithm for edge importance and vertex ranking in network analysis.
QT1 - 02 - Frequency Distribution
The document discusses frequency distributions and methods for organizing and visualizing data. It defines key terms like population, sample, data array, frequency distribution, relative frequency distribution, classes, histograms, polygons and ogives. It explains how to construct frequency distributions by determining the number and width of classes and calculating the frequency of observations in each class. Visualization methods like histograms and polygons are introduced to illustrate frequency distributions.
Morse-Smale Regression for Risk Modeling
Presentation summarizes main content of Farrelly, C. M. (2017). Extensions of Morse-Smale Regression with Application to Actuarial Science. arXiv preprint arXiv:1708.05712.
Paper was accepted December 2017 by Casualty Actuarial Society.
Frequency Tables, Frequency Distributions, and Graphic Presentation
This document provides an overview of key concepts for describing data through frequency tables, distributions, and graphs. It defines important terms like frequency table, distribution, class, interval and discusses how to organize both qualitative and quantitative data. Guidelines for data collection are provided. Examples are given to demonstrate how to construct frequency tables and distributions and convert them to relative frequencies. Finally, different types of graphs for presenting frequency distributions are described, including histograms, polygons and cumulative distributions.
Organizing-Qualitative-and-Quanti.-data
This document discusses organizing and summarizing qualitative and quantitative data. It defines different types of data such as nominal, ordinal, discrete, and continuous variables. It also discusses frequency distributions, relative frequencies, percentages, bar graphs, and pie charts for organizing qualitative data. For quantitative data, it discusses frequency distributions, means, standard deviations, histograms, and polygons. Overall, the document provides an overview of different methods for organizing, summarizing, and presenting both qualitative and quantitative data.
Quantum generalized linear models
1) Quantum generalized linear models (QGLMs) provide an alternative way to minimize error in statistical models by leveraging the geometry of probability distributions and quantum computing gates.
2) In tests on simulated overdispersed data and a real-world forest fire dataset, QGLMs performed comparably to or better than state-of-the-art machine learning algorithms like random forests and boosted regression.
3) QGLMs have the potential to improve statistical modeling by eliminating the need for link functions and exploiting the underlying geometry of models through quantum computations. This could help extend models to new distributions and problem types.
20151130
The document proposes improvements to distributed graph pattern matching algorithms. It introduces a boundary filter technique that aims to shrink large data graphs by removing boundary nodes. These are nodes that only have one relationship in directed graphs. Experiments show the boundary filter approach significantly reduces running time compared to the original distributed tight simulation algorithm, while also improving accuracy by finding more matching subgraphs. The boundary filtering allows independent evaluation of each vertex and scales well to more complex graph patterns.
High-Dimensional Data Visualization, Geometry, and Stock Market Crashes
Miami Data Science SALON (Nov 2018) talk regarding geometric methods for dimensionality reduction, data visualization, and stock market analysis (India's NSE).
Frequency distribution
This presentation gives you a brief idea;
-definition of frequency distribution
- types of frequency distribution
-types of charts used in the distribution
-a problem on creating types of distribution
-advantages and limitations of the distribution
Exploring Data
This document discusses exploratory data analysis techniques including summary statistics, visualization methods, and online analytical processing (OLAP). It provides examples using the Iris data set to illustrate histogram, box plot, scatter plot, contour plot, matrix plot, parallel coordinates, and OLAP cube and slicing/dicing operations. The goal of exploratory data analysis is to better understand data characteristics through pattern recognition and selecting appropriate tools for preprocessing and analysis.
Describing categorical data one variable
This document discusses organizing and graphing categorical data. It begins by defining frequency distributions and how to construct frequency tables for categorical data. It then discusses relative frequency distributions. The document reviews different types of charts used to display categorical data, including pie charts, bar charts, and Pareto charts. It provides examples of how to construct these charts. Finally, it discusses best practices for data visualization, such as labeling charts clearly and avoiding misleading graphs that violate principles like using area accurately to represent quantities.
slide->title; ?>
The document presents a machine learning method called Prototype Ranking (PR) for selecting stocks. PR uses competitive learning to find representative prototypes from training stock data, then uses k-nearest neighbors to rank and select test stocks. Experiments show PR selects portfolios with higher average returns and risk-adjusted returns than a traditional non-machine learning method, especially for smaller portfolios. PR is well-suited for the noisy and imbalanced nature of stock return data.
Frequency Distribution Table Handout
This is a handout which consists of the basic concepts/terms about Frequency Distribution Table. It also includes the ways on how to properly construct a Frequency Distribution Table.
Chapter 2: Frequency Distribution and Graphs
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
Chapter 2 250110 083240
This document discusses frequency distributions and graphs. It defines frequency distributions as organizing raw data into a table using classes and frequencies. There are three main types: categorical, grouped, and ungrouped. Guidelines are provided for constructing frequency distributions, such as having 5-20 classes of equal width. Common graphs discussed are histograms, frequency polygons, ogives, Pareto charts, time series graphs, and pie charts. These graphs represent frequency distributions in visual formats.
Cluster analysis
根據您提供的資料,我建議戴平台可以採用以下步驟進行集群分析:
1. 選擇運動員所知覺到的教練行為變項,如自主支持、威脅、控制等,作為集群分析的分類變項。
2. 量化這些教練行為,並測量不同運動員對同一教練這些行為的知覺程度,如利用量表進行評分。
Frequency distribution 6
Frequency distribution is used to organize and summarize data by grouping it into classes. It consists of class intervals, frequencies, and other components. An example frequency distribution table is given showing student ratings of teaching quality organized into classes with their frequencies. Frequency distributions can be represented graphically through histograms and pie charts to visualize the data distribution.
Frequency distribution
This document discusses frequency distributions, which summarize data by arranging it into classes and indicating how many observations fall into each class. It describes the key components of a frequency distribution, including the class, class limits, class mark, class interval, class frequency and tally marks. It provides examples of grouped frequency distributions, where the data is organized into groups, and ungrouped distributions, where the raw individual data points are listed. The objectives of a frequency distribution are to estimate population frequencies, facilitate data analysis, and allow computation of statistical measures.
What's hot (20)
Quantum-Min-Cut/Max-Flow-Based Vertex Importance Ranking
Quantum-Min-Cut/Max-Flow-Based Vertex Importance Ranking
Frequency Tables, Frequency Distributions, and Graphic Presentation
Frequency Tables, Frequency Distributions, and Graphic Presentation
High-Dimensional Data Visualization, Geometry, and Stock Market Crashes
High-Dimensional Data Visualization, Geometry, and Stock Market Crashes
Viewers also liked
Bsc cs ii dfs u-4 sorting and searching structure
This document discusses hash tables and their use for storing and retrieving data records. It describes how a hash table uses a hash function to map record keys to array indexes, which can cause collisions when different keys hash to the same index. It also covers inserting and searching for records, handling collisions, and deleting records from the hash table.
Probability Distributions
This document provides an overview of quantitative methods for probability distributions. It discusses key concepts like binomial distribution, normal distribution, standard normal distribution, central limit theorem, point estimates, interval estimates, and confidence intervals. Examples are provided to illustrate how to calculate probabilities, means, and confidence intervals for estimating population parameters based on sample data. Key probability distributions and statistical techniques are defined to analyze and make inferences about data.
Thiyagu statistics
Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data. It helps make better decisions by extracting information from data. There are two main types: descriptive statistics which describe data through methods like averages and distributions, and inferential statistics which make estimates, predictions, or generalizations about a population based on a sample. Key concepts in statistics include populations, samples, parameters which describe populations, and statistics which describe samples. The level of measurement of data, such as nominal, ordinal, interval, or ratio, determines what calculations and tests can be done.
Chi square
This document provides an overview of chi-square and analysis of variance (ANOVA) statistical tests. It defines chi-square as a test of independence using contingency tables and as a test of goodness of fit. It also defines ANOVA as a test used to compare three or more population means and determine if they are equal or different. Examples are provided to demonstrate how to perform chi-square and one-way ANOVA calculations and analyses.
Questionnaires
The document provides guidance on improving questionnaire questions by avoiding embarrassing, leading, or subjective questions. It recommends questions cover all options without repetition and have easy to answer formats like checkboxes to select an age range or frequency rather than open-ended answers.
Bsc agri 2 pae u-2.3 capitalformation
Capital formation is the process of increasing a country's capital assets through investments that boost productivity and economic development. It relies on domestic savings from households, businesses, and governments, as well as external resources like foreign aid. While capital is important, economic development also depends on other factors like education, government effectiveness, social attitudes, and human resources. Overall, capital formation is necessary but not sufficient for economic progress, which results from a complex interplay of social, cultural, political and economic conditions.
Research Design
There are three main types of research design: exploratory, descriptive, and causal. Exploratory research is used to gain background information, define terms, and clarify problems and hypotheses. Descriptive research describes and measures phenomena at a point in time using cross-sectional or longitudinal studies. Causal research determines causality using experiments that manipulate independent variables to see their effect on dependent variables while controlling for extraneous variables. The choice of design depends on research objectives and what is known about the problem.
Handling data scattergraph
The document discusses scattergraphs and correlation by:
1) Explaining the three types of correlation: positive, zero, and negative.
2) Describing strong, moderate, and weak correlation and providing examples of each for a negative correlation.
3) Presenting sample math test score data in an example scattergraph plotting algebra scores against handling data scores to illustrate the correlation between performance on the two tests.
Les5e ppt 01
This document provides an overview of key concepts in statistics, including:
- Defining statistics, populations, samples, parameters, and statistics.
- Distinguishing between descriptive and inferential statistics.
- Classifying data as qualitative or quantitative, and discussing the four levels of measurement.
- Outlining the steps for designing statistical studies and experiments, and discussing methods for data collection and sampling.
Emotional and Behavioral Disorders (EBD) and Grade I Pupils' Achievements
This study examined the relationship between emotional and behavioral disorders (EBD) and academic achievements among Grade 1 pupils in the Philippines. The study found that 67% of pupils exhibited moderate EBD, while 19% exhibited severe EBD, especially with attention deficit hyperactivity disorder (ADHD). ADHD was the only disorder found to negatively correlate with pupils' curricular and extra-curricular performance. Male pupils exhibited higher levels of disorders than females but lower academic performance. Pupils from highly urbanized areas exhibited higher EBD levels than those from less urbanized areas. The study recommends further research using a stable questionnaire to better understand pupils' EBD.
Applied Statistical Techniques
The document describes several research projects related to applied statistical techniques, including:
1) A project to develop a program for frail seniors to reduce hospital readmissions and emergency department visits using statistical analysis methods like logistic regression.
2) A study to implement and assess an injury prevention program for pediatric patients using statistical tests like repeated measures ANOVA to analyze barriers and utilization rates.
3) A study screening injured emergency department patients for alcohol abuse and evaluating rates of treatment seeking using multilevel logistic models.
1.1 An Overview of Statistics
This document provides an introduction to key concepts in statistics including data, populations, samples, parameters, statistics, descriptive statistics, and inferential statistics. It defines data as information from observations or measurements. Statistics is defined as collecting, organizing, analyzing and interpreting data to make decisions. Descriptive statistics involves summarizing and displaying data, while inferential statistics uses sample data to draw conclusions about populations. Examples are provided to illustrate identifying populations and samples, and distinguishing between parameters and statistics.
Descriptive Statistic in Assessment 1
This document discusses various measures of central tendency and variability. It provides details on calculating the median, mean, and mode from both raw data and grouped data. The median is the middle value of a data set and is not affected by outliers. The mean is the average and is more affected by outliers. The mode is the most frequent value. Formulas and step-by-step processes are provided to compute each measure from ungrouped and grouped data using methods like class intervals and frequency distributions.
Statistic Chart
This document contains statistics related to the length of blocks in a cube. It provides the mean, median, mode, range, and standard deviation of the block lengths. It also includes a frequency table showing the number of blocks that fall within certain length bins.
Sopheap's Using Spss presentation
This document contains numerical data in a 4x5 matrix as well as instructions to analyze the data through descriptive statistics such as calculating the mean, standard deviation, and frequency and to present results in graphs such as a histogram.
Nets
A net is a two-dimensional pattern that can be cut out and folded to form a three-dimensional shape. The document provides examples of nets that can be folded to form a cuboid and square-based pyramid. Students are instructed to draw nets for different shapes and identify what three-dimensional forms they represent.
Statistical techniques
This document discusses statistical techniques for analyzing data, including measures of location (mean, median, mode) which represent a single value for the data set, and measures of dispersion (range, standard deviation) which represent the spread of the data set. It provides examples of calculating the mean, median, and mode of sample data sets. The mean is most affected by extreme values, while the mode may be more useful than the median or mean for data with multiple instances of the same value. The advantage of the mean is that it uses all data values in its calculation.
Chapter018
Here are the key points about how each factor affects blood pressure:
- Vasodilation decreases blood pressure by widening blood vessels.
- Decreased stretching of baroreceptors reduces their ability to sense and respond to changes in blood pressure, leading to higher pressure.
- Hypoxemia (low oxygen) initially causes pulmonary vasoconstriction, then systemic vasodilation, increasing cardiac output and initially raising then lowering pressure.
- Inhibiting ACE (angiotensin-converting enzyme) prevents formation of vasoconstrictor angiotensin II, lowering pressure.
- Beta blockers inhibit sympathetic stimulation of heart rate and contractility, lowering pressure.
-
Xtream presentations
The document announces a startup competition hosted by Tetuan Valley, with three teams giving 5-minute presentations on their startup ideas, followed by a 15-minute period for teams to pivot their ideas based on feedback. The winning team will receive a $100k reward. Tetuan Valley is a startup incubator/accelerator founded by Luis Rivera and located in Spain.
Extreme semaphore 12 pax id290
The villa Extreme Semaphore is a newly constructed minimalist villa located 5 minutes from beaches in Ibiza, Spain. It can sleep up to 12 people across 5 bedrooms, each with countryside views. Features include an open plan living and dining area, modern kitchen, master bedroom with sea views, 25m swimming pool, and furnished outdoor spaces. Rates are available upon request and the villa offers tailored hospitality services for clients.
Viewers also liked (20)
Emotional and Behavioral Disorders (EBD) and Grade I Pupils' Achievements
Emotional and Behavioral Disorders (EBD) and Grade I Pupils' Achievements
Similar to Correlation testing
SOC2002 Lecture 11
The document discusses various techniques for quantitative data analysis, including descriptive analysis, exploratory analysis, and statistical analysis. Descriptive analysis involves frequency tables, charts, and summary statistics to describe individual and groups of variables. Exploratory analysis examines relationships between two or more variables using cross-tabulations and correlations. Statistical analysis tests for significant relationships using techniques like chi-squared tests, t-tests, and regression analysis. The remainder of the document provides examples and explanations of these analytical methods.
Review of "Survey Research Methods & Design in Psychology"
This document provides an overview and summary of a lecture on survey research and design in psychology. It discusses key topics like the research process, survey design, data analysis, reliability and validity, sampling, and reporting results. Assessment involves a lab report on survey-based research and a final exam testing knowledge of research methods and statistical analysis techniques.
Linearity cochran test
1. Linearity is evaluated by visually inspecting a plot of analytical results versus analyte concentration and determining if the relationship is linear.
2. If linear, the data should be statistically analyzed using methods like regression analysis and calculation of the correlation coefficient.
3. The Cochran test is used to determine if linearity data is homoscedastic or heteroscedastic by comparing the ratio of the largest variance to the total variances to critical values. If the ratio is lower, the data is homoscedastic.
cannonicalpresentation-110505114327-phpapp01.pdf
This document summarizes a study that used canonical correlation analysis to detect potential bias in faculty promotion scores at American University of Nigeria. The study aimed to test if canonical correlation could identify bias scoring, determine the influence of individual assessors' scores, and discriminate between promotable and non-promotable candidates. The results showed that canonical correlation could detect bias and influence with over 90% confidence and correctly classified candidates into promotable and non-promotable groups, rejecting the null hypotheses. Thus, canonical correlation was found to be an effective statistical tool for unbiased promotion scoring and decision making at the university.
Ad test
This document explains how to conduct a chi-square test for independence to determine if there is a significant association between two categorical variables. The test involves stating the null and alternative hypotheses, formulating an analysis plan specifying the significance level and test method, analyzing sample data to calculate degrees of freedom, expected frequencies, the test statistic, and p-value, then interpreting the results by comparing the p-value to the significance level. An example considers using this test to see if gender is related to voting preference in an election survey.
Lesson 8 Linear Correlation And Regression
1. The document discusses linear correlation and regression between plasma amphetamine levels and amphetamine-induced psychosis scores using data from 10 patients.
2. A positive correlation was found between the two variables, and a linear regression equation was established to predict psychosis scores from amphetamine levels.
3. However, further statistical tests were needed to determine if the correlation and regression model could be generalized to the overall patient population.
SPSS statistics - get help using SPSS
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.
Chem unit 1 presentation
Data can provide information about the natural world. It can be collected through observation and measurement, and expressed quantitatively or qualitatively. Data patterns and relationships can be identified through graphing and analysis. The scientific method involves making observations, developing hypotheses, designing experiments to test hypotheses, and drawing conclusions. Data is key to advancing scientific theories and applying research findings.
Introduction to Regression Analysis and R
The document discusses multiple linear regression analysis to predict gasoline mileage using automobile data. It covers the basics of regression modeling, assessing model fit, and diagnostics. Key steps include fitting a linear regression model of miles per gallon as the response variable against predictors like vehicle weight, engine size, and more. The document also demonstrates how to perform the multiple regression analysis in R using the automobile data set.
Correlation and Regression
Chapter 14 Power Points for Essentials of Statistics for the Behavioral Sciences, Gravetter & Wallnau, 8th ed
Business Quantitative Lecture 3
1) The assumptions of the regression model include errors being independent, normally distributed with a mean of zero and constant variance. Violations of these assumptions can be seen in residual plots.
2) Analysis of variance (ANOVA) decomposes the total variation in a dependent variable into variation explained by independent variables and unexplained variation.
3) For a company example, the regression model was found to be statistically significant using an F-test, meaning the relationship between the independent and dependent variables was unlikely due to chance. This validated the regression results.
Parameter Optimisation for Automated Feature Point Detection
Parameter optimization for an automated feature point detection model was explored. Increasing the number of random displacements up to 20 improved performance but additional increases did not. Larger patch sizes consistently improved performance. Increasing the number of decision trees did not affect performance for this single-stage model, unlike previous findings for a two-stage model. Overall, some parameter tuning was found to enhance the model's accuracy but not all parameters significantly impacted results.
Lect w8 w9_correlation_regression
This document discusses statistical techniques for investigating relationships between variables, including correlation, regression, and t-tests. Correlation determines if a linear relationship exists between two variables, while simple linear regression describes the relationship. Scatterplots are used to visualize paired data and the correlation coefficient measures the strength of the linear relationship. Regression finds the line of best fit that minimizes the residuals between observed and predicted values.
Advanced statistics for librarians
The document discusses research design and statistical concepts for evaluating library statistics. It covers topics like validity, reliability, generalizability, research questions, hypotheses, data definitions, sampling, data collection, scales of measurement, distributions, variables, and statistical tests. Examples of case studies analyzing citation analysis, usage analysis and service analysis in libraries are provided to demonstrate key concepts.
1608 probability and statistics in engineering
This document provides an overview of probability, statistics, and their applications in engineering. It defines key probability and statistics concepts like trials, outcomes, random experiments, and frequency distributions. It explains how engineers use statistics and probability to analyze data from tests and experiments to better understand product quality and failure rates. Examples are given of measures of central tendency like mean and median, measures of variation like standard deviation and variance, and the normal distribution curve. Engineering applications include using these analytical techniques to assess results from a class and compare two data histograms.
Hybrid Distatis
I presents a new method called Hybrid DISTATIS that can be applied to the analysis of sorting task and object characteristics data. Hybrid DISTATIS allows to project the object, the characteristics, and the assessor for each object in a map, which is a combining Principal Component Analysis Biplot (PCA Biplot) and DISTATIS method. In these maps, the proximity between two objects or assessor points reflects their similarities, the proximity between characteristic and axis vector reflects their correlations, and therefore these maps can be read using the same rules as standard metric multidimensional scaling (MDS) and PCA Biplot methods. Technically, Hybrid DISTATIS started by transforming the individual sorting task data into cross-product matrices as in classical MDS and evaluating the similarity between these matrices initially. Computes a compromise matrix which is the best aggregate of the individual cross-product matrices, than analyzes it with PCA. After that computes a column effect matrix as a characteristic coordinates with PCA Biplot. The individual matrices and characteristic coordinates are then projected onto the compromise space. The quality of Hybrid DISTATIS map obtained based on the eigenvalue cumulative percent of the compromise matrix. In this paper, the application using sorting task from the college ranks in 2010 is presented, which is published on a website by Webometrics, 4ICU, and QS, as an assessor and the college that became the objects are ITB, ITS, IPB, Unair, Undip, UGM, UI, and Unpad, with the characteristics variable are the number of students and the accreditation values.
Advanced Statistics.pptx
The document provides examples to calculate and compare measures of central tendency (mean, median, mode) for data sets. It explains that the mean is the average value calculated by summing all values and dividing by the total number of values. The median is the middle value of a data set arranged in numerical order. The mode is the value that occurs most frequently in a data set. Calculating these measures helps analyze and describe the central or typical values in a data set.
Analyzing survey data
The presentation covered key steps in analyzing survey data including defining goals, designing valid and reliable survey questions, collecting data, cleaning data, conducting descriptive statistics and correlations, comparing mean differences between groups, and clearly presenting results along with conclusions and recommendations. Piloting surveys and continuously improving methods was also emphasized.
Research Procedure
This part of the thesis describes the methodology section which provides details of the research activities, data collection strategies, and administration of questionnaires and interviews to achieve the study objectives and address the problem. It discusses preparing and testing questionnaires, identifying persons responsible for data collection, and approaches for administering questionnaires and conducting interviews.
Data analysis
This document discusses various stages and techniques in data analysis, including data processing, distribution, tabulation, analysis, interpretation, and presentation. It describes common statistical analyses like descriptive analysis, inferential analysis, differences analysis, associative analysis, and predictive analysis. Specific techniques covered include measures of central tendency, measures of variability, correlation, regression, hypothesis testing, and types of errors in decision making.
Similar to Correlation testing (20)
Review of "Survey Research Methods & Design in Psychology"
Review of "Survey Research Methods & Design in Psychology"
Parameter Optimisation for Automated Feature Point Detection
Parameter Optimisation for Automated Feature Point Detection
More from Steve Bishop
Cognitive load theory
Cognitive load theory describes the different types of cognitive load on working memory: intrinsic load from the difficulty of the material, extraneous load from poor instructional design, and germane load that aids learning. Various instructional design techniques can manage cognitive load, such as worked examples, completion problems, integrating rather than splitting sources of information, presenting words and pictures together rather than separately, and eliminating redundant information. The effectiveness of techniques depends on learner expertise and the complexity of the material.
Blockbusters #soccd
This document provides instructions for playing a game called "Player Key". The game is played in pairs, with one player making a chain from side to side and the other from top to bottom on a grid. Players take turns answering trivia questions, coloring in a block if they answer correctly. Players can also block their opponent's chain by choosing a question. The first player to complete their chain across or down wins.
Blockbusters template
Players work in pairs to build chains across or down the game board by answering questions correctly. Each player uses a different color to mark their chain, and can choose questions to block their partner's chain. The first player to complete a chain from side to side or top to bottom wins.
Blockbusters #socfam
Work in pairs to create chains by colouring in blocks when answering questions correctly. One player makes a chain from side to side, the other from top to bottom. Players use different colours and can block their partner's chain by choosing questions. The first to complete their chain across or down wins.
2.6 acids bases and salts
This document discusses acids, bases, and salts. It defines pH and the pH scale, explaining that acids have a pH below 7 while bases have a pH above 7. Neutral solutions have a pH of 7. Acids contain H+ ions while bases contain OH- ions. When an acid and base react, they form a salt and water through neutralization. Common acids and bases are given along with examples of acid-base reactions and properties. Methods for making soluble salts by reacting acids with metals or insoluble bases are also described.
C2.5 exothermic and endothermic reactions
This document discusses exothermic and endothermic chemical reactions. It provides examples of common exothermic reactions like combustion and oxidation, which release energy to their surroundings. Endothermic reactions like the decomposition of calcium carbonate absorb energy from their surroundings. The document uses examples like hand warmers and sports injury cold packs to illustrate exothermic and endothermic processes. It also contains questions to test the reader's understanding of whether specific reactions are exothermic or endothermic.
C2.3.3 quantitative chemistry
Farmer Giles wants to determine which fertilizer contains more nitrogen. To do this, he needs to calculate the percentage of nitrogen by mass in each fertilizer. KNO3 has a higher percentage of nitrogen than NaNO3 because KNO3 has a lower molar mass and therefore more of its mass is made up of nitrogen. Calculating percentage by mass involves using relative atomic masses and molar masses. For example, the percentage of nitrogen in ammonia is calculated by taking the mass of nitrogen atoms, dividing by the molar mass of ammonia, and multiplying by 100.
C2.2 how structure influences
Substances with simple molecular, giant ionic and giant covalent structures have very different properties. Ionic, covalent and metallic bonds are strong, while forces between molecules are weaker. Nanomaterials have new properties due to their small size on the scale of 10 atoms. The structures of substances influence their properties and uses.
C2.1 structure and bonding
1. The document discusses different types of chemical bonding including ionic bonding, covalent bonding, and metallic bonding.
2. Ionic bonding involves the transfer of electrons between atoms to form ions with opposite charges that are attracted in a giant lattice structure.
3. Covalent bonding can form either simple molecules held together by shared electron pairs or giant covalent structures with thousands of atoms bonded together.
C2.1 structure and bonding
Ionic compounds form giant lattice structures when oppositely charged ions bond via electrostatic forces. This ionic bonding results in high melting points and the ability to conduct electricity when molten or dissolved. Covalent bonds involve electron sharing and can form either simple molecules with weak intermolecular forces or giant covalent structures with very high melting points due to numerous strong covalent bonds. Metallic bonding involves delocalized electrons that act as glue between positive metal ions, allowing them to slide past one another.
B2.8 speciation
Speciation is the formation of new biological species. The document provides three key points about speciation and its relationship to natural selection:
1. Speciation can occur when a single population becomes separated into two isolated groups. Over time, each group evolves differently through genetic variation and natural selection, resulting in reproductively isolated species.
2. Evidence for evolution includes the fossil record, which shows organisms changing over time and new species arising as others become extinct.
3. Natural selection plays a role in speciation when environmental changes cause isolated populations to evolve adaptations better suited to their local conditions, leading to reproductive isolation and the formation of new species over prolonged periods of separation.
B2.7 meiosis and mitosis
Meiosis and mitosis are two types of cell division. Meiosis occurs in sex organs and produces gametes through two divisions, resulting in four cells each with half the number of chromosomes, while mitosis occurs throughout the body and involves one division to produce two identical cells with the same number of chromosomes. The main differences are that meiosis produces gametes and reduces chromosome number, while mitosis produces somatic cells to grow and repair tissues.
B2.7 genetic disorders
This document discusses several genetic disorders including polydactyly, cystic fibrosis, and Huntington's disease. It provides details on the inheritance patterns and characteristics of each disorder. Potential treatments and ethical issues related to genetic testing, gene therapy, cloning, and preimplantation genetic diagnosis are also examined.
B2.7 cell division and inheritance
Cell division and inheritance allow characteristics to be passed from parents to offspring. Chromosomes, made of DNA and containing genes, are found in cell nuclei and segregate into gametes during meiosis. Offspring receive half their chromosomes from each parent, determining traits like earlobe attachment and sex, which is determined by XX or XY sex chromosomes. Genetic crosses can predict the likelihood offspring will inherit dominant or recessive alleles.
B2.5 proteins enzymes
Enzymes are protein molecules that act as biological catalysts, increasing the rate of chemical reactions. They are specific and only catalyze one reaction. Enzymes are found in animals, plants and microorganisms and are used widely in the home and industry. Some advantages of using enzymes include enabling reactions to occur at lower temperatures and making industrial processes cheaper. However, enzymes are sensitive to temperature and pH changes.
Aerobic & anaerobic respiration
Aerobic respiration uses oxygen to break down glucose, releasing energy. This occurs in mitochondria and produces significantly more ATP than anaerobic respiration. During intense exercise, the body shifts to anaerobic respiration which takes place in the cytoplasm when oxygen demand outstrips supply. It generates lactic acid and ATP more quickly but to a lesser extent. The human body repays this oxygen debt through deep breathing after exercise to oxidize lactic acid buildup.
B1.4 red v grey
Environmental changes affect the distribution and survival of both plant and animal species through biotic and abiotic factors. Biotic factors like competition for resources and disease transmission have led to changes in species populations over time, such as the disappearance of pollinating insects and the decline of red squirrel numbers in the UK due to the introduction and spread of grey squirrels who outcompete reds for food and carry squirrel pox virus to which they are immune but kills reds. Non-biotic environmental changes in temperature, oxygen levels, water, and light availability from sources like pollution have also impacted species distributions.
B1.4 pollution detectors
Lichens are useful as bio-indicators of pollution levels because they absorb nutrients and pollutants from the air. Different lichen species show varying sensitivities and abilities to accumulate pollutants. They can survive in almost all environments and seasons. Indicator species found in water can also indicate pollution levels, with mayfly nymphs and caddis fly larvae found in cleaner water, and water lice and sludgeworms found in more polluted water, allowing conclusions about water quality to be drawn. Samples should be taken at the same time each year for valid comparisons, and other factors like oxygen, pH, metals or chemicals can also act as non-biological pollution indicators.
B1.4 plants adaptation
Plants have developed various adaptations to survive in different environments. Some plants in cold climates are small, dark-colored, and have small waxy leaves or needles to minimize heat loss. Desert plants often have silver or hairy leaves to reflect sunlight, store water in stems or leaves, or have deep root systems to reach water underground. Adaptations help plants acquire resources and endure environmental stresses.
B1.4 animals adaptations
This document discusses animal adaptations for survival in different environments. It provides examples of how Arctic and desert animals have adapted through physical traits like thick fur or thin coats and body shapes that minimize heat loss or gain. Sharks are used to demonstrate both general aquatic adaptations like streamlined bodies and gills as well as specific predatory adaptations like sensitive smell and replaceable teeth. Surface area to volume ratio is discussed as an important factor in an animal's ability to retain or shed heat.
More from Steve Bishop (20)
Recently uploaded
How to Download & Install Module From the Odoo App Store in Odoo 17
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Contiguity Of Various Message Forms - Rupam Chandra.pptx
Contiguity Of Various Message Forms - Rupam Chandra.pptx
220711130082 Srabanti Bag Internet Resources For Natural Science
Internet resources for natural science
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as discipline
Educational Technology in the Health Sciences
Plenary presentation at the NTTC Inter-university Workshop, 18 June 2024, Manila Prince Hotel.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
(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.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Accounting for Restricted Grants When and How To Record Properly
In this webinar, member learned how to stay in compliance with generally accepted accounting principles (GAAP) for restricted grants.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
Recently uploaded (20)
How to Download & Install Module From the Odoo App Store in Odoo 17
How to Download & Install Module From the Odoo App Store in Odoo 17
Contiguity Of Various Message Forms - Rupam Chandra.pptx
Contiguity Of Various Message Forms - Rupam Chandra.pptx
220711130082 Srabanti Bag Internet Resources For Natural Science
220711130082 Srabanti Bag Internet Resources For Natural Science
220711130097 Tulip Samanta Concept of Information and Communication Technology
220711130097 Tulip Samanta Concept of Information and Communication Technology
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Geography as a Discipline Chapter 1 __ Class 11 Geography NCERT _ Class Notes...
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) Curriculum
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Accounting for Restricted Grants When and How To Record Properly
Accounting for Restricted Grants When and How To Record Properly
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
Correlation testing
- 1. Correlation testing Unit 8 Strand 4
- 2. Correlation testing : graphical test, eg line of best fit; linear regression, eg using a calculator in linear regression mode; testing for power law, eg radioactivity experiments, electrical experiments, any other science-related example (bloodspatter)
- 3. P4 carry out an appropriate correlation method to investigate data collected from a laboratory experiment. M4 interpret the results of the correlation. D4 evaluate the validity of the interpretation of the results of the correlation.
- 4. Evaluation of the test Interpret the test: Understand a set of data from the collection stage Distinction Merit Pass
- 5. Evaluation of the test: Interpret the test: Understand a set of data from the collection stage Distinction Merit Pass
- 6. Evaluation of the test: Interpret the test: What does it tell us? Understand a set of data from the collection stage Distinction Merit Pass
- 7. Evaluation of the test: Interpret the test: What does it tell us? Understand a set of data from the collection stage Distinction Merit Pass
- 8. Evaluation of the test: general and specific limitations Interpret the test: What does it tell us? Understand a set of data from the collection stage Distinction Merit Pass
- 10. Bivariate data Two variables
- 11. Bivariate data Two variables Scattergraph
- 12. Bivariate data Two variables Correlation Scattergraph
- 13. Bivariate data Two variables Correlation Scattergraph
- 14. Correlation gives us a measure of the relationship: -1 to +1
- 15. Correlation gives us a measure of the relationship: -1 to +1 BUT !
- 16. Enter data on a spreadsheet In chart options: XY scatter Right click a data point - add (linear) trendline; in options select Display R-squared value on chart
- 17. Right click a data point - add (linear) trendline in options select Display R-squared value on chart
- 18. Use the data collected when you investigated the link between femur length and height Enter the data on a spreadsheet add a trendline and find the R 2 value ( P4 ) What does this value tell you? ( M4 ) How sure of the relationship? What are the limitations of this correlation test in this specific instance? ( D4 )