Chapter 2 Flashcards
The term REGRESSION was introduced by
Francis Galton
Galton’s Law was confirmed by WHO: The average height of sons of a group of tall fathers < their fathers’ height. And the average height of sons of a group of short fathers > their fathers’ height.
Karl Pearson
By the words of Galton, this was “Regression to WHAT?
mediocrity
WHAT is concerned with the study of the dependence of one variable (The Dependent Variable), on one or more other variable(s) (The Explanatory Variable), with a view to estimating and/or predicting the (population) mean or average value of the former in term of the known or fixed (in repeated sampling) values of the latter.
Regression Analysis
WHIS IS Y? WHICH IS X?
Son’s Height
Father’s Height
Y = Son’s Height;
X = Father’s Height
WHIS IS Y? WHICH IS X?
Personal Disposable Income
Personal Consumption Expenditure
Y = Personal Consumption Expenditure
X = Personal Disposable Income
WHIS IS Y? WHICH IS X?
Money/Income
Inflation Rate
Y = Money/Income;
X = Inflation Rate
WHIS IS Y? WHICH IS Xs?
Crop yield;
temperature, rainfall, sunshine, fertilizer
Y = Crop yield;
Xs = temperature, rainfall, sunshine, fertilizer
In regression analysis we are concerned with WHAT among variables (not Functional or Deterministic)
STATISTICAL DEPENDENCE
there is bound to be some WHAT in the dependent-variable crop yield that cannot be fully explained no matter how many explanatory variables we consider.
“intrinsic” or random variability
T OR F
Regression does not necessarily imply causation.
TRUE
T OR F
A statistical relationship can logically imply causation.
FALSE; A statistical relationship cannot logically imply causation.
A WHAT relationship, however strong and however suggestive, can never establish causal connection: our ideas of causation must come from outside statistics, ultimately from some theory or other
statistical
the primary objective is to measure the strength or degree of linear association between two variables (both are assumed to be random)
Correlation Analysis
we try to estimate or predict the average value of one variable (dependent, and assumed to be stochastic) on the basis of the fixed values of other variables (independent, and non-stochastic)
Regression Analysis
