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UC / Statistics / STAT 1031 / What is the use of response variable?

What is the use of response variable?

What is the use of response variable?

Description

School: University of Cincinnati
Department: Statistics
Course: Introduction to Statistics
Professor: Dawit tadesse
Term: Summer 2015
Tags:
Cost: 25
Name: Chapter 3-4
Description: This includes Interpreting data and relationships (regression).
Uploaded: 03/09/2016
3 Pages 173 Views 1 Unlocks
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Adalberto Dooley (Rating: )

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Example : Strong right skew distribution”We also discuss several other topics like What are two legislative branches collectively known as Congress?
We also discuss several other topics like What is a binomial prob dist?

        Mean: 0.015; Median: 0.009Don't forget about the age old question of What is the difference between preservation and conservation?

        Median is a better summary statistic

If you want to learn more check out What are the characteristics of an open system?

Chapter 3:

Bivariate Data: 2 variables

  • Examine the relationship between these two variables

Don't forget about the age old question of How do we measure the sample z-score?

Scatterplot: displays quantitative bivariate  data

Types of variables:

  • Response (dependent (y)) variable: measure outcome of a study
  • Explanatory (independent (x)) variable: explain or influence changes in a response variable

We also discuss several other topics like business statistics syllabus

Interpreting scatterplots

  • Describe pattern of the relationship
  • Form: linear, curved, clusters, no pattern
  • Direction: positive, negative, no direction
  • Strength: how closely the points fit the “form”; strong/weak
  • Outlines of the pattern

Correlation coefficient ®: measure of the direction and strength of a relationship

        

  • mean and std. dev. For explanatory variable
  • mean and std. dev. For response variable
  • Properties:
  • r does not change if we interchange x and y
  • r has no unit
  • |r|= measures the strength
  • sign of r gives us the direction

Lurking Variable: is not explanatory or response variable, but may influence the relationship between the two variables.

Establishing Causation

  • Association is strong / consistent
  • Higher doses are associated with stronger responses

Chapter 7: Samples and Observational Studies

Strength:

  1. r=-0.9 or r=0.9

They are the same

  1. r=-0.87 or r=0.85

-0.87 is stronger because 1-0.87= 0.87 >0.85

-r is not resistant to outliers

Chapter 4: Relationships (Regression)

Least-square regression line (LSRL): line such that the sum of the vertical distances between the data points and line is 0; the sum of the squared vertical distances is the smallest possible.

  • Notation:                 

                        

  • Slope: describes how much we expect y to change, on average, for every unit change in x.
  • Intercept: necessary mathematical description of the regression line
  • For every unit change in the explanatory variable, the response variable changes by the slope

        r:correlation coefficient;

        

  • Coefficient of determination (r2): fraction of variance (or percent) of variance explained by the x.
  • Influential individuals: changes the regression if removed: isolated point
  • Residuals: observed value - predicted value

  • Extrapdation: prediction outside range

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