Factor Analysis-Presentation DATA ANALYTICS

This document provides an introduction to exploratory factor analysis (EFA). It discusses key concepts such as factors, factor loadings, communalities, assumptions of EFA, extraction and rotation methods. An example is provided applying EFA to anthropometric and physical performance data from 21 participants. Three factors were extracted accounting for over 80% of the variance: an anthropometric factor with high loadings for weight, height and leg length; a physical performance factor with high loadings for shuttle run, 50m dash and 12m run/walk; and a third factor with high loading for shoulder width only.

This document discusses exploratory factor analysis (EFA). EFA is used to identify underlying factors that explain the pattern of correlations within a set of observed variables. The document outlines the steps of EFA including testing assumptions, constructing a correlation matrix, determining the number of factors, rotating factors, and interpreting the factor loadings. It provides an example of running EFA on a dataset with 11 physical performance and anthropometric variables from 21 participants. The analysis extracts 3 factors that explain over 80% of the total variance.

PCA is a technique used to reduce the dimensionality of large data sets by transforming the data to a new set of variables called principal components. It works by identifying the directions of maximum variance in high-dimensional data and projecting the data onto these directions while preserving as much information as possible. The principal components are the eigenvectors of the covariance matrix and represent the directions with maximum variability in the data. Dimensionality reduction is achieved by keeping only the first few principal components and ignoring the rest based on their eigenvalues.

- Discriminant analysis is a statistical technique used to discriminate between two or more groups based on multiple predictor variables. - A study analyzed data on effective and ineffective extension agents to identify variables that best discriminate between the two groups. Variables like years of experience, communication skills, and positive attitude to work significantly differed between the groups. - Discriminant analysis generated a function to maximize differences between the groups based on predictor variables. The function was statistically significant based on a small Wilks' lambda value, indicating most variability was explained.

This document discusses bivariate linear regression and its understanding. Bivariate linear regression, also called simple linear regression, involves modeling the relationship between a dependent variable (Y) and a single independent variable (X). The regression equation takes the form of Y = β0 + β1X + ε, where β0 is the intercept, β1 is the slope coefficient, and ε is the error term. This equation can be used to predict Y values based on X values, as well as understand how much variation in Y can be explained by X. Parameters β0 and β1 are estimated to maximize the explanatory power of X for Y while minimizing prediction errors.

The document discusses exploratory factor analysis (EFA). EFA is used to identify patterns of correlations among observed variables and group them into fewer unobserved variables called factors. The key steps of EFA include data screening, factor extraction to identify factors, factor rotation for interpretability, and interpretation of results. The document also provides examples of important EFA concepts like communalities, eigenvalues, scree plot, factor loadings, and reliability. It summarizes an EFA conducted on variables related to consumer mobile phone purchasing behavior, which identified 4 factors: after sales services, looks and ranges, availability of parts and add-on technology, and brand and features.

1. Outline the differences between Hoarding power and Encouraging. 2. Explain about the power of Congruency in Leadership. DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrCopy Employee Data set to this page.822.10.962233290915.81FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 1522.60.984233280814.91FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3522.60.984232390415.30FA37230.999232295216.20FAThe column labels in the table mean:1023.11.003233080714.71FAID – Employee sample number Salary – Salary in thousands 2323.11.004233665613.30FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)1123.31.01223411001914.81FASERvice – Years of serviceGender: 0 = male, 1 = female 2623.51.020232295216.20FAMidpoint – salary grade midpoint Raise – percent of last raise3123.61.028232960413.91FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)3623.61.026232775314.30FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint4023.81.034232490206.30MA14241.04523329012161FA4224.21.0512332100815.71FA1924.31.055233285104.61MA25251.0872341704040MA3226.50.855312595405.60MB227.70.895315280703.90MB3428.60.923312680204.91MB3933.91.094312790615.50FB2034.11.1013144701614.80FB1834.51.1133131801115.60FB335.11.132313075513.61FB1341.11.0274030100214.70FC741.31.0324032100815.71FC1642.21.054404490405.70MC4145.81.144402580504.30MC2746.91.172403580703.91MC548.21.0044836901605.71MD3049.31.0274845901804.30MD2456.31.173483075913.80FD4556.91.185483695815.21FD4757.21.003573795505.51ME3357.51.008573590905.51ME4581.01857421001605.51ME3858.81.0325745951104.50ME5059.61.0465738801204.60ME4660.21.0575739752003.91ME2260.31.257484865613.81FD161.61.081573485805.70ME4461.81.0855745901605.21ME49631.1055741952106.60ME1763.71.1185727553131FE1264.71.1355752952204.50ME4869.51.2195734901115.31FE973.91.103674910010041MF4375.61.1286742952015.50FF2976.31.139675295505.40MF2177.21.1526743951306.31MF678.11.1656736701204.51MF2878.31.169674495914.40FF Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descript ...

1. Quantitative data can be summarized using measures of center (mean, median), spread (range, IQR, standard deviation), and position (quartiles, percentiles, z-scores). 2. The mean is more affected by outliers than the median. The median is more resistant to outliers and a better measure of center for skewed data. 3. Additional summaries like the five-number summary and boxplots provide a graphical view of the distribution and identify potential outliers.

This document provides an overview of basic statistics concepts including descriptive statistics, measures of central tendency, variability, sampling, and distributions. It defines key terms like mean, median, mode, range, standard deviation, variance, and quantiles. Examples are provided to demonstrate how to calculate and interpret these common statistical measures.

1) The document discusses characteristics of good community leaders based on a study of 102 respondents. It identifies 4 key factors that describe good leadership: positive characteristics (vision, communication skills, character, personality), spontaneous decision characteristics (spending time with subordinates, fearless attitude), negative characteristics (not being punctual, honest, lacking monitoring skills), and spiritual characteristics (thinking about enlisting help rather than problems, not being aggressive). 2) Factor analysis was used to identify these 4 factors, which together account for about 61% of the variability in responses. Variables were grouped under each factor based on their loadings in the rotated component matrix. 3) The 4 factors provide an overview of the characteristics respondents associated with good

This document provides an overview of measures of relative standing and boxplots. It defines key terms like percentiles, quartiles, and outliers. Percentiles and quartiles divide a data set into groups based on the number of data points that fall below each value. The document also provides examples of calculating percentiles and quartiles for a data set of cell phone data speeds. Boxplots use the five-number summary (minimum, Q1, Q2, Q3, maximum) to visually depict a data set's center and spread through its quartiles and outliers.

Factor analysis is a statistical technique used to reduce a large number of variables into a smaller number of underlying factors. It identifies patterns of correlations between observed variables and groups variables that are highly correlated into factors. The key steps in factor analysis are constructing a correlation matrix, determining the appropriate number of factors to extract, rotating the factors to improve interpretability, and selecting surrogate variables to represent the factors in subsequent analyses. Interpreting the results involves looking at which variables have high loadings on each factor to understand what each factor represents conceptually.

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The document provides an overview of factor analysis, including: - Factor analysis is a statistical technique used to reduce a large number of variables into a smaller number of underlying factors or components according to patterns of correlation between variables. - The two main types are exploratory factor analysis, which is used when the underlying factors are unknown, and confirmatory factor analysis, which is used to test hypotheses about a predetermined factor structure. - Key steps in factor analysis include determining the appropriateness of the data, extracting factors using various criteria, rotating factors to improve interpretation, and interpreting the results including factor loadings and communalities.

This document provides an introduction to using logistic regression in R to analyze case-control studies. It explains how to download and install R, perform basic operations and calculations, handle data, load libraries, and conduct both conditional and unconditional logistic regression. Conditional logistic regression is recommended for matched case-control studies as it provides unbiased results. The document demonstrates how to perform logistic regression on a lung cancer dataset to analyze the association between disease status and genetic and environmental factors.