A&S 350 Vr
•Download as PPTX, PDF•
0 likes•183 views
Zach Mosier is a student who grew up and currently lives in Kentucky. He attends the University of Kentucky where he studies sociology and is a member of the sociology club. He has worked at a hospital and hopes to pursue counseling or sociology after graduation. His strengths include being hardworking, creative, insightful, consistent, and adaptable.
Report
Share
Report
Share
Recommended
Stats powerpoint presentation
The study aimed to determine if there is a correlation between age and number of personal electronic devices owned. Data was collected through surveys of randomly selected individuals aged 15-70 across different public locations and times. The results showed a significant negative correlation, with older individuals owning fewer devices than younger individuals.
Rank correlation- some features and an application
This PPT was presented by Kushal Kumar Dey, a B.Stat (undergraduate) student from Indian Statistical Institute, Kolkata for D.Basu Memorial Award
Ols by hiron
This document provides an overview of a presentation on Ordinary Least Squares (OLS) estimation in econometrics. OLS is introduced as a method used to estimate parameters of economic relationships from data by minimizing errors. Key points covered include: what OLS estimates, why it is used in econometrics to estimate regression parameters from a sample regression function in order to approximate the true population regression function, and details on how the OLS criterion minimizes the sum of squared residuals to obtain parameter estimates. Goodness of fit is also discussed as a measure of how well the estimated regression line fits the sample data.
Ordinary least squares linear regression
Ordinary Least Squares Linear Regression is commonly used but often misunderstood and misapplied. It works by minimizing the sum of squared errors between predictions and actual values in the training data to determine coefficients for the linear regression equation. However, it is very sensitive to outliers in the data which can dramatically affect the determined coefficients and reduce prediction accuracy. Alternative regression techniques like least absolute deviations are more robust to outliers but less computationally efficient. Preprocessing data to remove or de-emphasize outliers can help address these issues with Ordinary Least Squares regression.
STATA - Introduction
STATA is data analysis software that can be used via menu options or typed commands. It has a wide range of econometric techniques and can open, examine, and run regressions on datasets. The tutorials on www.STATA.org.uk provide step-by-step guides for using STATA to perform tasks like data management, statistical analysis, importing data, summary statistics, graphs, regressions, and other analyses.
Simple (and Simplistic) Introduction to Econometrics and Linear Regression
A simplified (and some may argue, simplistic) introduction to econometrics with linear regression. No formulas inside!
Report writing
This document provides an overview of report writing. It defines a report as a statement of the results of an investigation or matter where definite information is required. Report writing is an essential skill for professionals in many fields as reports aim to clearly and succinctly inform readers. The document outlines the common structures of reports, including cover letters, titles, executive summaries, introductions, bodies, conclusions, and appendices. It also discusses the process of report writing, including planning, collecting and organizing information, considering the audience, and finishing touches. Reports differ from essays in their objective to present information rather than arguments.
Regression
- Regression analysis is a statistical technique used to measure the relationship between two quantitative variables and make causal inferences.
- A regression model graphs the relationship between a dependent variable (Y axis) and one or more independent variables (X axis). The goal is to find the linear equation that best fits the data.
- The regression equation takes the form Y = a + bX, where a is the intercept, b is the slope coefficient, and X and Y are the variables. The coefficient b indicates the strength and direction of the relationship.
Recommended
Stats powerpoint presentation
The study aimed to determine if there is a correlation between age and number of personal electronic devices owned. Data was collected through surveys of randomly selected individuals aged 15-70 across different public locations and times. The results showed a significant negative correlation, with older individuals owning fewer devices than younger individuals.
Rank correlation- some features and an application
This PPT was presented by Kushal Kumar Dey, a B.Stat (undergraduate) student from Indian Statistical Institute, Kolkata for D.Basu Memorial Award
Ols by hiron
This document provides an overview of a presentation on Ordinary Least Squares (OLS) estimation in econometrics. OLS is introduced as a method used to estimate parameters of economic relationships from data by minimizing errors. Key points covered include: what OLS estimates, why it is used in econometrics to estimate regression parameters from a sample regression function in order to approximate the true population regression function, and details on how the OLS criterion minimizes the sum of squared residuals to obtain parameter estimates. Goodness of fit is also discussed as a measure of how well the estimated regression line fits the sample data.
Ordinary least squares linear regression
Ordinary Least Squares Linear Regression is commonly used but often misunderstood and misapplied. It works by minimizing the sum of squared errors between predictions and actual values in the training data to determine coefficients for the linear regression equation. However, it is very sensitive to outliers in the data which can dramatically affect the determined coefficients and reduce prediction accuracy. Alternative regression techniques like least absolute deviations are more robust to outliers but less computationally efficient. Preprocessing data to remove or de-emphasize outliers can help address these issues with Ordinary Least Squares regression.
STATA - Introduction
STATA is data analysis software that can be used via menu options or typed commands. It has a wide range of econometric techniques and can open, examine, and run regressions on datasets. The tutorials on www.STATA.org.uk provide step-by-step guides for using STATA to perform tasks like data management, statistical analysis, importing data, summary statistics, graphs, regressions, and other analyses.
Simple (and Simplistic) Introduction to Econometrics and Linear Regression
A simplified (and some may argue, simplistic) introduction to econometrics with linear regression. No formulas inside!
Report writing
This document provides an overview of report writing. It defines a report as a statement of the results of an investigation or matter where definite information is required. Report writing is an essential skill for professionals in many fields as reports aim to clearly and succinctly inform readers. The document outlines the common structures of reports, including cover letters, titles, executive summaries, introductions, bodies, conclusions, and appendices. It also discusses the process of report writing, including planning, collecting and organizing information, considering the audience, and finishing touches. Reports differ from essays in their objective to present information rather than arguments.
Regression
- Regression analysis is a statistical technique used to measure the relationship between two quantitative variables and make causal inferences.
- A regression model graphs the relationship between a dependent variable (Y axis) and one or more independent variables (X axis). The goal is to find the linear equation that best fits the data.
- The regression equation takes the form Y = a + bX, where a is the intercept, b is the slope coefficient, and X and Y are the variables. The coefficient b indicates the strength and direction of the relationship.
Correlation
Overviews non-parametric and parametric approaches to (bivariate) linear correlation. See also: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Correlation
Simple linear regression (final)
The document presents the results of a simple linear regression analysis conducted by a black belt to predict the number of calls answered (dependent variable) based on staffing levels (independent variable) using data collected over 240 samples in a call center. The regression equation found 83.4% of the variation in calls answered was explained by staffing levels. Notable outliers and leverage points were identified that could impact the strength of the predicted relationship between calls answered and staffing.
Pearson Correlation, Spearman Correlation &Linear Regression
This document discusses correlation and linear regression. It defines correlation as a statistic that measures the strength and direction of the linear relationship between two continuous variables. Positive correlation indicates that as one variable increases, so does the other. Negative correlation means the variables are inversely related. Linear regression can be used to predict a continuous outcome variable based on a continuous predictor variable using the regression equation y=a+bx. The regression line minimizes the sum of squared differences between the data points and the line. The slope coefficient b indicates the strength of the linear prediction and can be tested for significance.
Regression analysis
- Regression analysis is a statistical tool used to examine relationships between variables and can help predict future outcomes. It allows one to assess how the value of a dependent variable changes as the value of an independent variable is varied.
- Simple linear regression involves one independent variable, while multiple regression can include any number of independent variables. Regression analysis outputs include coefficients, residuals, and measures of fit like the R-squared value.
- An example uses home size and price data from 10 houses to generate a linear regression equation predicting that price increases by around $110 for each additional square foot. This model explains 58% of the variation in home prices.
Lecture 4
This document summarizes the key assumptions and properties of Ordinary Least Squares (OLS) regression. OLS aims to minimize the sum of squared residuals by estimating the beta coefficients. It provides the best linear unbiased estimates if its assumptions are met. The key assumptions are: 1) the regression is linear in parameters; 2) the error term has a mean of zero; 3) the error term is uncorrelated with the independent variables; 4) there is no serial correlation or autocorrelation in the error term; 5) the error term has constant variance (homoskedasticity); and 6) there is no perfect multicollinearity among independent variables. When all assumptions are met, OLS estimates
Data Science - Part IV - Regression Analysis & ANOVA
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Correlation and regression
This document provides an introduction to correlation and regression analysis. It defines correlation as a measure of the association between two variables and regression as using one variable to predict another. The key aspects covered are:
- Calculating correlation using Pearson's correlation coefficient r to measure the strength and direction of association between variables.
- Performing simple linear regression to find the "line of best fit" to predict a dependent variable from an independent variable.
- Using a TI-83 calculator to graphically display scatter plots of data and calculate the regression equation and correlation coefficient.
More Related Content
Viewers also liked
Correlation
Overviews non-parametric and parametric approaches to (bivariate) linear correlation. See also: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Correlation
Simple linear regression (final)
The document presents the results of a simple linear regression analysis conducted by a black belt to predict the number of calls answered (dependent variable) based on staffing levels (independent variable) using data collected over 240 samples in a call center. The regression equation found 83.4% of the variation in calls answered was explained by staffing levels. Notable outliers and leverage points were identified that could impact the strength of the predicted relationship between calls answered and staffing.
Pearson Correlation, Spearman Correlation &Linear Regression
This document discusses correlation and linear regression. It defines correlation as a statistic that measures the strength and direction of the linear relationship between two continuous variables. Positive correlation indicates that as one variable increases, so does the other. Negative correlation means the variables are inversely related. Linear regression can be used to predict a continuous outcome variable based on a continuous predictor variable using the regression equation y=a+bx. The regression line minimizes the sum of squared differences between the data points and the line. The slope coefficient b indicates the strength of the linear prediction and can be tested for significance.
Regression analysis
- Regression analysis is a statistical tool used to examine relationships between variables and can help predict future outcomes. It allows one to assess how the value of a dependent variable changes as the value of an independent variable is varied.
- Simple linear regression involves one independent variable, while multiple regression can include any number of independent variables. Regression analysis outputs include coefficients, residuals, and measures of fit like the R-squared value.
- An example uses home size and price data from 10 houses to generate a linear regression equation predicting that price increases by around $110 for each additional square foot. This model explains 58% of the variation in home prices.
Lecture 4
This document summarizes the key assumptions and properties of Ordinary Least Squares (OLS) regression. OLS aims to minimize the sum of squared residuals by estimating the beta coefficients. It provides the best linear unbiased estimates if its assumptions are met. The key assumptions are: 1) the regression is linear in parameters; 2) the error term has a mean of zero; 3) the error term is uncorrelated with the independent variables; 4) there is no serial correlation or autocorrelation in the error term; 5) the error term has constant variance (homoskedasticity); and 6) there is no perfect multicollinearity among independent variables. When all assumptions are met, OLS estimates
Data Science - Part IV - Regression Analysis & ANOVA
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Correlation and regression
This document provides an introduction to correlation and regression analysis. It defines correlation as a measure of the association between two variables and regression as using one variable to predict another. The key aspects covered are:
- Calculating correlation using Pearson's correlation coefficient r to measure the strength and direction of association between variables.
- Performing simple linear regression to find the "line of best fit" to predict a dependent variable from an independent variable.
- Using a TI-83 calculator to graphically display scatter plots of data and calculate the regression equation and correlation coefficient.
Viewers also liked (7)
Pearson Correlation, Spearman Correlation &Linear Regression
Pearson Correlation, Spearman Correlation &Linear Regression
Data Science - Part IV - Regression Analysis & ANOVA
Data Science - Part IV - Regression Analysis & ANOVA
A&S 350 Vr
- 1. Who is…. Zach Mosier?
- 2. I grew up here I have lived here:
- 3. I attend: And I study:
- 4. Member of:
- 5. I’ve worked:
- 6. Hard Working Creative Insightful Consistence Adaptability
- 7. I would like to go:
- 8. I would like to work:
- 9. And Ultimately.. School Sociology Hospital Counseling Sociology Sociology
- 10. I am Zach Mosier Zach Mosier zjmo222@g.uky.edu Cell:(270)-348-6109