Business Analytics Foundation with R Tools Part 1 presented by Beamsync.
If you are looking for analytics training in Bangalore visit: http://beamsync.com/business-analytics-training-bangalore/
Business Analytics Foundation with R tools - Part 2Beamsync
Beamsync is providing analytics training courses in Bangalore. If you are looking business analytics training in Bangalore, then consult Beamsync.
For upcoming training schedules visit: http://beamsync.com/business-analytics-training-bangalore/
Business Analytics Foundation with R Tools - Part 3Beamsync
Beamsync is top training institute for "business analytics" course in Bangalore. If you are looking for classroom training for analytics course visit: http://beamsync.com/business-analytics-training-bangalore/
There are three main types of missing data: missing completely at random, missing at random, and not missing at random. Common techniques for handling missing data include mean/median imputation, hot deck imputation, cold deck imputation, regression imputation, stochastic regression imputation, K-nearest neighbors imputation, and multivariate imputation by chained equations. Modern deep learning methods can also be used to impute missing values, with techniques like Datawig that leverage neural networks.
PCA is a technique used to simplify complex datasets by transforming correlated variables into a set of uncorrelated variables called principal components. It identifies patterns in high-dimensional data and expresses the data in a way that highlights similarities and differences. PCA is useful for analyzing data and reducing dimensionality without much loss of information. It works by rotating the existing axes to capture major variability in the data while ignoring smaller variations.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
Statistics is the science of making effective use of numerical data relating to groups of individuals or experiments. It deals with collecting, analyzing, and interpreting data through surveys, experiments, and statistical models described by probability distributions. Samples drawn from populations are used to infer properties, and histograms created with functions like hist and rose show the distribution of data values across a range.
Regression analysis models the relationship between variables, where the dependent variable is modeled as a function of one or more independent variables. Linear regression models take forms such as straight-line, polynomial, Fourier, and interaction models. Multiple linear regression is useful for understanding variable effects, predicting values, and finding relationships between multiple independent and dependent variables. Methods like robust, stepwise, ridge, and partial least squares regression address issues like outliers, multicollinearity, and correlated predictors. Response surface and generalized linear models extend linear regression to nonlinear relationships. Multivariate regression models multiple dependent variables.
Linear regression is a statistical method used to model the relationship between a scalar dependent variable and one or more explanatory variables. The document discusses linear regression in R, including simple linear regression with one explanatory variable and multiple linear regression with two or more explanatory variables. It also covers evaluating linear regression models using measures like residual standard error, R-squared, and p-values. The document provides an example of modeling bond prices with coupon rates and advertising sales data with multiple advertising expenditures.
Business Analytics Foundation with R tools - Part 2Beamsync
Beamsync is providing analytics training courses in Bangalore. If you are looking business analytics training in Bangalore, then consult Beamsync.
For upcoming training schedules visit: http://beamsync.com/business-analytics-training-bangalore/
Business Analytics Foundation with R Tools - Part 3Beamsync
Beamsync is top training institute for "business analytics" course in Bangalore. If you are looking for classroom training for analytics course visit: http://beamsync.com/business-analytics-training-bangalore/
There are three main types of missing data: missing completely at random, missing at random, and not missing at random. Common techniques for handling missing data include mean/median imputation, hot deck imputation, cold deck imputation, regression imputation, stochastic regression imputation, K-nearest neighbors imputation, and multivariate imputation by chained equations. Modern deep learning methods can also be used to impute missing values, with techniques like Datawig that leverage neural networks.
PCA is a technique used to simplify complex datasets by transforming correlated variables into a set of uncorrelated variables called principal components. It identifies patterns in high-dimensional data and expresses the data in a way that highlights similarities and differences. PCA is useful for analyzing data and reducing dimensionality without much loss of information. It works by rotating the existing axes to capture major variability in the data while ignoring smaller variations.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
Statistics is the science of making effective use of numerical data relating to groups of individuals or experiments. It deals with collecting, analyzing, and interpreting data through surveys, experiments, and statistical models described by probability distributions. Samples drawn from populations are used to infer properties, and histograms created with functions like hist and rose show the distribution of data values across a range.
Regression analysis models the relationship between variables, where the dependent variable is modeled as a function of one or more independent variables. Linear regression models take forms such as straight-line, polynomial, Fourier, and interaction models. Multiple linear regression is useful for understanding variable effects, predicting values, and finding relationships between multiple independent and dependent variables. Methods like robust, stepwise, ridge, and partial least squares regression address issues like outliers, multicollinearity, and correlated predictors. Response surface and generalized linear models extend linear regression to nonlinear relationships. Multivariate regression models multiple dependent variables.
Linear regression is a statistical method used to model the relationship between a scalar dependent variable and one or more explanatory variables. The document discusses linear regression in R, including simple linear regression with one explanatory variable and multiple linear regression with two or more explanatory variables. It also covers evaluating linear regression models using measures like residual standard error, R-squared, and p-values. The document provides an example of modeling bond prices with coupon rates and advertising sales data with multiple advertising expenditures.
Descriptive statistics summarize and describe data through measures like mean, median, range, and standard deviation. Inferential statistics make inferences about an unknown population based on a sample, using methods like estimation, hypothesis testing, and regression. Regression finds the linear relationship between variables to estimate a dependent variable's value given an independent variable. It should only be used if there is significant linear correlation and the independent variable values are close to the original data.
There are three main areas of statistics: descriptive statistics, inferential statistics, and regression. Descriptive statistics describes data through measures of central tendency like mean, median, and mode and measures of dispersion like range, variance, and standard deviation. Inferential statistics makes predictions and comparisons about populations using sample data through techniques like t-tests and the general linear model. Regression analyzes the relationships between variables using methods such as analysis of variance, nonlinear regression, and rank correlation.
The document discusses finding trend lines and lines of best fit from scatter plot data. It provides examples of finding the slope and y-intercept of a line from two points on a scatter plot, and using a graphing calculator to determine the equation of the line of best fit with the highest correlation value r. The line of best fit will provide the most accurate trend line for the data based on the correlation between the variables.
Firebird: cost-based optimization and statistics, by Dmitry Yemanov (in English)Alexey Kovyazin
This document discusses cost-based optimization and statistics in Firebird. It covers:
1) Rule-based optimization uses heuristics while cost-based optimization uses statistical data to estimate the cost of different access paths and choose the most efficient.
2) Statistics like selectivity, cardinality, and histograms help estimate costs by providing information on data distribution and amounts.
3) The optimizer aggregates costs from the bottom up and chooses the access path with the lowest total cost based on the statistical information.
This document discusses various metrics for summarizing data, including the mean, median, mode, geometric mean, harmonic mean, and mid-range. It explains how to calculate each metric and describes scenarios in which each would be most appropriate. The mean is used when outliers should be accounted for, the median for datasets with outliers, and the mode for categorical data. The document also covers robustness and breakdown points, noting the median is the most robust statistic.
Understandung Firebird optimizer, by Dmitry Yemanov (in English)Alexey Kovyazin
The document discusses Firebird's query optimizer. It explains that the optimizer analyzes statistical information to retrieve data in the most efficient way. It can use rule-based or cost-based strategies. Rule-based uses heuristics while cost-based calculates costs based on statistics. The optimizer prepares queries, calculates costs of different plans, and chooses the most efficient plan based on selectivity, cardinality, and cost metrics. It relies on up-to-date statistics stored in the database to estimate costs and make optimization decisions.
Scatter plots are a quality tool used to show the relationship between two variables. They graph pairs of numerical data with one variable on each axis to look for correlation. If the variables are correlated, the data points will fall along a line or curve, indicating a relationship. Scatter plots are useful for determining potential causes of problems by identifying which process elements are related and how strongly. They involve collecting paired data, plotting the independent variable on the x-axis and dependent variable on the y-axis, and examining the shape and slope of the resulting cluster of points.
PCA projects data onto principal components to reduce dimensionality while retaining most information. It works by (1) zero-centering the data, (2) calculating the covariance matrix to measure joint variability, (3) computing eigenvalues and eigenvectors of the covariance matrix to identify principal components with most variation, and (4) mapping the zero-centered data to a new space using the eigenvectors. This transforms the data onto a new set of orthogonal axes oriented in the directions of maximum variance.
This document introduces the concepts of data analysis. It defines key terms like individuals, variables, categorical vs. quantitative variables, and distribution. It explains that the goal of data analysis is to organize, display, summarize and ask questions about data in order to make inferences about populations based on samples. The next sections will cover analyzing categorical data with graphs and tables, as well as describing quantitative data with numerical summaries.
Missing data occurs when no data value is stored for a variable in an observation, usually due to manual errors or incorrect measurements. There are three types of missing data: missing completely at random, missing at random, and missing not at random. Several methods can be used to deal with missing data, including reducing the dataset, treating missing values as a special value, replacing with the mean, replacing with the most common value, and using the closest fit to impute missing values. Proper handling of missing data is important to avoid bias and distortions in analyzing the data.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
On Statistical Analysis and Optimization of Information Retrieval Effectivene...Jun Wang
This document discusses statistical analysis and optimization of information retrieval effectiveness metrics. It proposes viewing the retrieval process as estimating the joint probability of document relevance given a query, then ranking documents stochastically based on an information retrieval metric to maximize expected metric value. The approach allows calculating expected values of major metrics like average precision under relevance uncertainty. While challenging to obtain relevance probabilities, click data and document score correlations can provide approximations. The framework provides a statistical perspective on retrieval and potential for evaluation accounting for relevance uncertainty.
Ratio and Product Type Estimators Using Stratified Ranked Set Samplinginventionjournals
In this paper, we propose a class of estimators for estimating the population mean of variable of interest using information on an auxiliary variable in stratified ranked set sampling. The bias and Mean Squared Error of proposed class of estimators are obtained to first degree of approximation. It has been shown that these methods are highly beneficial to the estimation based on Stratified Simple Random Sampling. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in Stratified Random Sampling
Applied Mathematical Modeling with Apache Solr - Joel Bernstein, LucidworksLucidworks
This document discusses using Apache Solr for mathematical modeling. It covers using Solr to train regression models on data, assess the models by analyzing residuals, and use the models for prediction and anomaly detection. Specific regression techniques discussed include linear regression, polynomial curve fitting, and K-nearest neighbors regression. Probability distributions are also covered as a way to model risk and detect outliers. The document walks through an example of using simple linear regression to model network response times and detect anomalies.
This document discusses exploratory data analysis techniques including boxplots and five-number summaries. It explains how to organize and graph data using histograms, frequency polygons, stem-and-leaf plots, and box-and-whisker plots. The five important values used in a boxplot are the minimum, first quartile, median, third quartile, and maximum. An example constructs a boxplot for a stockbroker's daily client numbers over 11 days.
The RuLIS approach to outliers (Marcello D'Orazio,FAO)FAO
Expert consultation on methodology for an information system on rural livelihoods and Sustainable Development Goals indicators on smallholder productivity and income 7 - 8 December, FAO headquarters
This document summarizes an exploratory data analysis project on a credit card application dataset. The analysis involved examining relationships between variables, identifying variables that best distinguish between positive and negative application outcomes, and calculating statistical metrics. Key variables like A2, A3, A8, and A14 showed differences in distributions between positive and negative classes. Correlation and R-squared analyses revealed that variables A2, A3, A8, and A11 explained the most variance in the classification variable. The analysis uncovered useful insights that will help build an effective predictive model.
The document presents the portfolio theory of information retrieval. It draws an analogy between ranking documents and selecting a portfolio of stocks, where the relevance scores of documents are uncertain and correlated. The portfolio theory models a ranked list as having an expected relevance and variance, and aims to optimize this by maximizing expected relevance while minimizing variance. Experiments show the portfolio theory approach outperforms probability ranking and diversity-based reranking on standard evaluation metrics.
Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.
Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.
Descriptive statistics summarize and describe data through measures like mean, median, range, and standard deviation. Inferential statistics make inferences about an unknown population based on a sample, using methods like estimation, hypothesis testing, and regression. Regression finds the linear relationship between variables to estimate a dependent variable's value given an independent variable. It should only be used if there is significant linear correlation and the independent variable values are close to the original data.
There are three main areas of statistics: descriptive statistics, inferential statistics, and regression. Descriptive statistics describes data through measures of central tendency like mean, median, and mode and measures of dispersion like range, variance, and standard deviation. Inferential statistics makes predictions and comparisons about populations using sample data through techniques like t-tests and the general linear model. Regression analyzes the relationships between variables using methods such as analysis of variance, nonlinear regression, and rank correlation.
The document discusses finding trend lines and lines of best fit from scatter plot data. It provides examples of finding the slope and y-intercept of a line from two points on a scatter plot, and using a graphing calculator to determine the equation of the line of best fit with the highest correlation value r. The line of best fit will provide the most accurate trend line for the data based on the correlation between the variables.
Firebird: cost-based optimization and statistics, by Dmitry Yemanov (in English)Alexey Kovyazin
This document discusses cost-based optimization and statistics in Firebird. It covers:
1) Rule-based optimization uses heuristics while cost-based optimization uses statistical data to estimate the cost of different access paths and choose the most efficient.
2) Statistics like selectivity, cardinality, and histograms help estimate costs by providing information on data distribution and amounts.
3) The optimizer aggregates costs from the bottom up and chooses the access path with the lowest total cost based on the statistical information.
This document discusses various metrics for summarizing data, including the mean, median, mode, geometric mean, harmonic mean, and mid-range. It explains how to calculate each metric and describes scenarios in which each would be most appropriate. The mean is used when outliers should be accounted for, the median for datasets with outliers, and the mode for categorical data. The document also covers robustness and breakdown points, noting the median is the most robust statistic.
Understandung Firebird optimizer, by Dmitry Yemanov (in English)Alexey Kovyazin
The document discusses Firebird's query optimizer. It explains that the optimizer analyzes statistical information to retrieve data in the most efficient way. It can use rule-based or cost-based strategies. Rule-based uses heuristics while cost-based calculates costs based on statistics. The optimizer prepares queries, calculates costs of different plans, and chooses the most efficient plan based on selectivity, cardinality, and cost metrics. It relies on up-to-date statistics stored in the database to estimate costs and make optimization decisions.
Scatter plots are a quality tool used to show the relationship between two variables. They graph pairs of numerical data with one variable on each axis to look for correlation. If the variables are correlated, the data points will fall along a line or curve, indicating a relationship. Scatter plots are useful for determining potential causes of problems by identifying which process elements are related and how strongly. They involve collecting paired data, plotting the independent variable on the x-axis and dependent variable on the y-axis, and examining the shape and slope of the resulting cluster of points.
PCA projects data onto principal components to reduce dimensionality while retaining most information. It works by (1) zero-centering the data, (2) calculating the covariance matrix to measure joint variability, (3) computing eigenvalues and eigenvectors of the covariance matrix to identify principal components with most variation, and (4) mapping the zero-centered data to a new space using the eigenvectors. This transforms the data onto a new set of orthogonal axes oriented in the directions of maximum variance.
This document introduces the concepts of data analysis. It defines key terms like individuals, variables, categorical vs. quantitative variables, and distribution. It explains that the goal of data analysis is to organize, display, summarize and ask questions about data in order to make inferences about populations based on samples. The next sections will cover analyzing categorical data with graphs and tables, as well as describing quantitative data with numerical summaries.
Missing data occurs when no data value is stored for a variable in an observation, usually due to manual errors or incorrect measurements. There are three types of missing data: missing completely at random, missing at random, and missing not at random. Several methods can be used to deal with missing data, including reducing the dataset, treating missing values as a special value, replacing with the mean, replacing with the most common value, and using the closest fit to impute missing values. Proper handling of missing data is important to avoid bias and distortions in analyzing the data.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
On Statistical Analysis and Optimization of Information Retrieval Effectivene...Jun Wang
This document discusses statistical analysis and optimization of information retrieval effectiveness metrics. It proposes viewing the retrieval process as estimating the joint probability of document relevance given a query, then ranking documents stochastically based on an information retrieval metric to maximize expected metric value. The approach allows calculating expected values of major metrics like average precision under relevance uncertainty. While challenging to obtain relevance probabilities, click data and document score correlations can provide approximations. The framework provides a statistical perspective on retrieval and potential for evaluation accounting for relevance uncertainty.
Ratio and Product Type Estimators Using Stratified Ranked Set Samplinginventionjournals
In this paper, we propose a class of estimators for estimating the population mean of variable of interest using information on an auxiliary variable in stratified ranked set sampling. The bias and Mean Squared Error of proposed class of estimators are obtained to first degree of approximation. It has been shown that these methods are highly beneficial to the estimation based on Stratified Simple Random Sampling. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in Stratified Random Sampling
Applied Mathematical Modeling with Apache Solr - Joel Bernstein, LucidworksLucidworks
This document discusses using Apache Solr for mathematical modeling. It covers using Solr to train regression models on data, assess the models by analyzing residuals, and use the models for prediction and anomaly detection. Specific regression techniques discussed include linear regression, polynomial curve fitting, and K-nearest neighbors regression. Probability distributions are also covered as a way to model risk and detect outliers. The document walks through an example of using simple linear regression to model network response times and detect anomalies.
This document discusses exploratory data analysis techniques including boxplots and five-number summaries. It explains how to organize and graph data using histograms, frequency polygons, stem-and-leaf plots, and box-and-whisker plots. The five important values used in a boxplot are the minimum, first quartile, median, third quartile, and maximum. An example constructs a boxplot for a stockbroker's daily client numbers over 11 days.
The RuLIS approach to outliers (Marcello D'Orazio,FAO)FAO
Expert consultation on methodology for an information system on rural livelihoods and Sustainable Development Goals indicators on smallholder productivity and income 7 - 8 December, FAO headquarters
This document summarizes an exploratory data analysis project on a credit card application dataset. The analysis involved examining relationships between variables, identifying variables that best distinguish between positive and negative application outcomes, and calculating statistical metrics. Key variables like A2, A3, A8, and A14 showed differences in distributions between positive and negative classes. Correlation and R-squared analyses revealed that variables A2, A3, A8, and A11 explained the most variance in the classification variable. The analysis uncovered useful insights that will help build an effective predictive model.
The document presents the portfolio theory of information retrieval. It draws an analogy between ranking documents and selecting a portfolio of stocks, where the relevance scores of documents are uncertain and correlated. The portfolio theory models a ranked list as having an expected relevance and variance, and aims to optimize this by maximizing expected relevance while minimizing variance. Experiments show the portfolio theory approach outperforms probability ranking and diversity-based reranking on standard evaluation metrics.
Hey friends, here is my "query tree" assignment. :-) I have searched a lot to get this master piece :p and I can guarantee you that this one gonna help you In Sha ALLAH more than any else document on the subject. Have a good day :-)
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.
Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.
Linear regression is a machine learning algorithm that finds a linear relationship between a dependent variable and one or more independent variables. It can be simple linear regression with one independent variable or multiple linear regression with more than one. The goal is to find the "best fit" line that minimizes the error between predicted and actual values. This is done by calculating coefficients using a cost function and gradient descent. Model performance is evaluated using metrics like the R-squared value, with a higher value indicating a better fit. Key assumptions for linear regression include a linear relationship between variables, little multicollinearity, homoscedasticity, normal error term distribution, and no autocorrelation.
Linear regression is a supervised machine learning technique used to model the relationship between a continuous dependent variable and one or more independent variables. It finds the line of best fit that minimizes the distance between the observed data points and the regression line. The slope of the regression line is determined using the least squares method. R-squared measures how well the regression line represents the data, with values closer to 1 indicating a stronger relationship. The standard error of the estimate quantifies the accuracy of predictions made by the linear regression model. Linear regression performs well when data is linearly separable, but has limitations such as an assumption of linear relationships and sensitivity to outliers and multicollinearity.
This presentation educates you about Linear Regression, SPSS Linear regression, Linear regression method, Why linear regression is important?, Assumptions of effective linear regression and Linear-regression assumptions.
For more topics stay tuned with Learnbay.
This document discusses correlation, trend analysis, and different correlation procedures. It defines correlation as a statistical relationship between dependent variables. Bivariate correlations measure the relationship between two variables, while partial correlations control for additional variables. Distances calculate similarity or dissimilarity statistics between variables or cases. Trend analysis describes historical patterns and allows projections of past or future trends. It can extract underlying patterns in time series data hidden by noise. Regression analysis is commonly used to analyze trends between a continuous independent variable, like weekly reading hours, and a continuous dependent variable, like reading achievement scores.
Linear regression models establish relationships between independent and dependent variables by fitting a linear equation to the data. Simple linear regression uses one independent variable to predict the dependent variable, while multiple linear regression uses multiple independent variables. The linear regression line is created using the ordinary least squares method to minimize the sum of squared errors between actual and predicted dependent variable values.
Linear regression is a supervised machine learning technique used to model the relationship between a continuous dependent variable and one or more independent variables. It is commonly used for prediction and forecasting. The regression line represents the best fit line for the data using the least squares method to minimize the distance between the observed data points and the regression line. R-squared measures how well the regression line represents the data, on a scale of 0-100%. Linear regression performs well when data is linearly separable but has limitations such as assuming linear relationships and being sensitive to outliers and multicollinearity.
The document provides an overview of regression analysis. It defines regression analysis as a technique used to estimate the relationship between a dependent variable and one or more independent variables. The key purposes of regression are to estimate relationships between variables, determine the effect of each independent variable on the dependent variable, and predict the dependent variable given values of the independent variables. The document also outlines the assumptions of the linear regression model, introduces simple and multiple regression, and describes methods for model building including variable selection procedures.
Regression analysis is a statistical technique used to model relationships between variables. It allows one to predict the average value of a dependent variable based on the value of one or more independent variables. The key ideas are that the dependent variable is influenced by the independent variables in a linear or curvilinear fashion, and regression provides an equation to estimate the dependent variable given values of the independent variables. Common applications of linear regression include forecasting, determining relationships between variables, and estimating how changes in one variable impact another.
A presentation for Multiple linear regression.pptvigia41
Multiple linear regression (MLR) is a statistical method used to predict the value of a dependent variable based on the values of two or more independent variables. MLR produces an equation that estimates the best weighted combination of independent variables to predict the dependent variable. MLR can assess the contribution and relative importance of each predictor variable while controlling for the effects of the other predictors. MLR requires that assumptions of independence, normality, homoscedasticity, and linearity are met.
This document summarizes key concepts in regression analysis for developing cost estimating relationships. Simple regression uses a single independent variable to predict a dependent variable based on a straight line model. The coefficient of determination, standard error of the estimate, and T-test are used to measure how well the regression equation fits the data. Regression is commonly used to establish cost estimating relationships, analyze indirect cost rates over time, and forecast trends while controlling for other influencing factors.
This document provides an overview and summary of linear regression analysis theory and computing. It discusses linear regression models and the goals of regression analysis. It also introduces some key topics that will be covered in the book, including simple and multiple linear regression, model diagnosis, generalized linear models, Bayesian linear regression, and computational methods like least squares estimation. The book aims to serve as a one-semester textbook on fundamental regression analysis concepts for graduate students.
Linear regression is a statistical method used to explain the relationship between variables. The document discusses:
1) An agenda covering regression, diagnostics, differences between linear and logistic regression, assumptions, and interview questions.
2) Details on linear regression including understanding the algorithm, assumptions around linearity, normality, multicollinearity, autocorrelation, and homoscedasticity.
3) How to check if assumptions are violated including residual plots, Q-Q plots, and various statistical tests.
The document provides an in-depth overview of linear regression modeling, assumptions, and how to diagnose potential issues.
Regression analysis models the relationship between a dependent (target) variable and one or more independent (predictor) variables. Linear regression predicts continuous variables using a linear equation. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The goal is to find the "best fit" line that minimizes error between predicted and actual values. Feature selection identifies important predictors by removing irrelevant or redundant features. Techniques include wrapper, filter, and embedded methods. Overfitting and underfitting occur when models are too complex or simple, respectively. Dimensionality reduction through techniques like principal component analysis (PCA) transform correlated variables into linearly uncorrelated components.
The document provides an overview of regression analysis techniques, including linear regression and logistic regression. It explains that regression analysis is used to understand relationships between variables and can be used for prediction. Linear regression finds relationships when the dependent variable is continuous, while logistic regression is used when the dependent variable is binary. The document also discusses selecting the appropriate regression model and highlights important considerations for linear and logistic regression.
Research is a systematic and scientific method of finding solutions by obtaining various types of data and systematic analysis of the multiple aspects of the issues related.
The techniques or the specific procedure which helps to identify, choose, process, and analyze information about a subject is called Research Methodology
Experimental design is a statistical tool for improving product design and solving production problems.
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In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
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𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
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A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
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Business Analytics Foundation with R Tools Part 1
1. BUSINESS ANALYTICS FOUNDATION WITH R
TOOLS
Lesson 4 - Predictive Modeling Techniques
Part 1
Copyright 2016,Beamsync, All rights reserved.
2. ● Understand regression analysis and types of regression models
● Know and build a simple linear regression model
● Understand and develop a logistic regression model
● Learn cluster analysis, types and methods to form clusters
● Know time series and its components
● Decompose seasonal and non seasonal time series
● Understand different exponential smoothing methods
● Know the advantages and disadvantages of exponential smoothing
● Understand the concepts of white noise and correlogram
● Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc.
● Understand all the analysis techniques with case studies
OBJECTIVE SLIDE
After completing
this course, you will
be able to:
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3. REGRESSION ANALYSIS
● Regression analysis is a statistical tool for determining causal effect of one or more variables upon
another (or more) variables.
● The variables that associated are thought to be systematically connected by a relationship.
● The variables that are assumed to be the cause are called predictor and the variables that are
assumed to be effect are called the response or target variables.
● The identified relation between these variables is called the regression equation. We say it as the
target is regressed by the predictor.
● Typically, a regression analysis is used for
● Prediction (i.e. forecasting) of the target variable.
● Modeling the relationship between the variables.
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4. TYPES OF REGRESSION MODELS.
Regression
Models
Univariate
Linear
Simple Multiple
NonLinear
Multivariate
Linear NonLinear
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5. • It’s a common technique to determine how one variable of interest is affected by another.
• It is used for three main purposes:
• For describing the linear dependence of one variable on the other.
• For prediction of values of other variable from the one which has more data.
• Correction of linear dependence of one variable on the other.
• A line is fitted through the group of plotted data.
• The distance of the plotted points from the line gives the residual value.
• The residual value is a discrepancy between the actual and the predicted value.
• The procedure to find the best fit is called the least-squares method.
SIMPLE LINEAR REGRESSION
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6. • The equation that represents how an independent variable is related to a dependent variable and
an error term is a regressionmodel.
y = β0 + β1x + ε
Where, β0 and β1 are called parameters of the model,
ε is a random variable called error term.
• Getting the estimates of β0 and β1, i.e. E(Y|X) means finding the best straight line that can be drawn
through the scatter plot Y vs X. This is done by Least Square(LS) estimates.
LINEAR REGRESSION MODEL
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7. SIMPLE LINEAR REGRESSION – GRAPHICAL UNDERSTANDING
y intercept
An observed value of x when x
equals x0
Mean value of y
when x equals x0
X
Y
x0 = A specific value of x, the
independent variable.
β0
Error term
Straight line defined by the equation
y = β0 + β1x
β1
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8. PROCESS TO BUILD A REGRESSION MODEL
Evaluate the model
Identify the target variables
Identify the predictors
Data collection
Decide the relationship
Fit the model
Copyright 2016,Beamsync, All rights reserved.
9. • The predictor variable x is non – random.
• The error term ε is random.
• Error term follows normal distribution.
• Standard deviation of error is independent of x.
• The data being used to estimate the parameters should be independent of each other.
• If any of the above assumptions are violated, modelling procedure must be modified.
LINEAR REGRESSION MODEL ASSUMPTIONS
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10. Thank You
Beamsync is providing business analytics training in Bangalore along with R
tool. If you are looking your career into analytics schedule you’re training here:
http://beamsync.com/business-analytics-training-bangalore/
Copyright 2016,Beamsync, All rights reserved.