'Explanatory variable x' presentation slideshows

Inference About Regression Coefficients

Inference About Regression Coefficients. BENDRIX.XLS. This is a continuation of the Bendrix manufacturing example from the previous chapter. As before, the response variable is Overhead and the explanatory variables are MachHrs and ProRuns. The data are contained in this file.

944 views • 78 slides

SC968: Panel Data Methods for Sociologists

SC968: Panel Data Methods for Sociologists. Introduction to survival/event history models. Types of outcome. Continuous OLS Linear regression Binary Binary regression Logistic or probit regression Time to event data Survival or event history analysis.

815 views • 65 slides

Association

756 views • 57 slides

EXAMINING RELATIONSHIPS

EXAMINING RELATIONSHIPS . Least Squares Regression. Least Squares Regression. A regression line is a straight line that describes how a response variable y changes as an explanatory variable x changes. The least squares regression line (LSRL) is a mathematical model for the data.

637 views • 53 slides

Chapter 15 Inference for Regression

Chapter 15 Inference for Regression. AP Statistics Hamilton HW: 15.1, 15.2, 15.5, 15.8, 15.10, 15.11, 15.14, 15.26. The Regression Model.

836 views • 45 slides

Exploratory Data Analysis: Two Variables

Exploratory Data Analysis: Two Variables . FPP 7-9. Exploratory data analysis: two variables. There are three combinations of variables we must consider. We do so in the following order 1 qualitative/categorical, 1 quantitative variables Side-by-side box plots, counts, etc.

1.05k views • 31 slides

Chapter 4 Section 3 Establishing Causation

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Class 3: Thursday, Sept. 16

Class 3: Thursday, Sept. 16. Reliability and Validity of Measurements Introduction to Regression Analysis Simple Linear Regression (2.3). Correlation and Reliability of Measurements.

394 views • 24 slides

Least-Squares Regression

Least-Squares Regression. Section 3.3. Recall from 3.2:. Correlation measures the strength and direction of a linear relationship between two variables. How do we summarize the overall pattern of a linear relationship? Draw a line!. Least-Squares Regression.

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Regression Analysis: Part 2

Regression Analysis: Part 2. Inference Dummies / Interactions Multicollinearity / Heteroscedasticity Residual Analysis / Outliers. Shoe Size Example. Consider the prediction of Shoe Size discussed before

669 views • 19 slides

Warm Up: TI83/84

Warm Up: TI83/84. Tech. Toolbox p. 165 (Cleardraw tip) Do on your calculator: 3.11 Warm Up: 3.15, 3.20. 3.2: Least Squares Regression. (LSRL = Least Squares Regression Line) We are required to have an explanatory and response variable.

330 views • 17 slides

Chapter Three Day Six

Chapter Three Day Six. Coefficient of Determination R 2. Homework. P. 227 43,44,47 P. 2239 49,50,53. P. 227 43.

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Chapter 9 Notes

Chapter 9 Notes. In scatterplots we can have points that are outliers or influential points or both.

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Chapter 4 Summary

Chapter 4 Summary. Scatter diagrams of data pairs (x , y) are useful in helping us determine visually if there is any relation between x and y values and, if so, how strong the relation might be. We call x the explanatory variable and y the response variable. Summary cont.

154 views • 3 slides