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Regression Analysis
Simple Regression
y = mx + b
y = a + bx
y = a + bxwhere:
y dependent variable (value depends on x)
a y-intercept (value of y when x = 0)
b slope (rate of change in ratio of delta y divided by delta x)
x independent variable
Assumptions
Linearity
Independence of Error
Homoscedasticity
Normality
Linearity
The most fundamental assumption is that the model fits the situation [i.e.: the Y
variable is linearly related to the value of the X variable].
Independence of Error
The error (residual) is independent
for each value of X.
[Residual = observed - predicted]
Homoscedasticity
The variation around
the line of regression
constant
for all values of X.
Normality
The values of Y be normally distributed at
each value of X.
Diagnostic Checking Linearity
Independence
Examine scatter plot of residuals versus fitted [Yhat] for evidence of nonlinearity
Plot residuals in time order and look for patterns
Diagnostic Checking Homoscedasticity
Normality
Examine scatter plots of residuals versus fitted [Yhat] and residuals vs time order and look for changing scatter.
Examine histogram of residuals. Look for departures from normal curve.
Goal
Develop a statistical model that can predict the values of a dependent (response) variable based upon the values of the independent (explanatory) variable(s).
Goal
Simple Regression
A statistical model that utilizes one quantitativequantitative independent variable “X” to predict the quantitativequantitative dependent variable “Y.”
Mini-Case
Since a new housing complex is being developed in Carmichael, management is under pressure to open a new pie restaurant. Assuming that population and annual sales are related, a study was conducted to predict expected sales.
Mini-Case(Descartes Pie Restaurants)
RestaurantPopulation
(1000)Annual Sales
($1000)1 2 58
2 6 105
3 8 88
::: ::: :::
9 22 149
10 26 202
Mini-Case What preliminary conclusions
can management draw from the data?
What could management expect sales to be if population of the new complex is approximately 18,000 people?
Scatter Diagrams The values are
plotted on a two-dimensional graph called a “scatter diagram.”
Each value is plotted at its X and Y coordinates.
Scatter Plot of Pieshop
0 5 10 15 20 25 30
Population (1000’s)
0
40
80
120
160
200
240
Sales ($1000’s_sal
ScatterPlot of PIESHOP
Types of Models
No relationship between X and Y
Positive linear relationship
Negative linear relationship
Method of Least Squares The straight line that best fits the data.
Determine the straight line for which the differences between the actual values (Y) and the values that would be predicted from the fitted line of regression (Y-hat) are as small as possible.
Measures of Variation
Explained
Unexplained
Total
Explained Variation
Sum of Squares(Yhat - Ybar)
2
due to Regression
[SSR]
Unexplained Variation
Sum of Squares(Yobs - Yhat)2
Error
[SSE]
Total Variation
Sum of Squares(Yobs - Ybar)2
Total
[SST]
H0:
There is no linear relationship between the
dependent variable and the explanatory variable
Hypotheses
H0: = 0
H1: 0
or
H0: No relationship exists
H1: A relationship exists
Analysis of Variance for Regression
Sourceof
VariationSum ofSquares d.f. Mean Square
[Regression]Model SSR k - 1 SSR/dfn
[Residual]Error SSE n - k SSE/dfd
Total SST n - 1test:
p 0.05SST
Standard Error of the Estimate
sy.x
- the measure of variability around the line of regression
Relationship
When null hypothesis is rejected, a
relationship between Y and X variables exists.
Coefficient of Determination
R2 measures the proportion of variation that is explained
by the independent variable
in the regression model.
R2 = SSR / SST
Confidence interval estimates
»True mean
YX
»Individual
Y-hat
Pieshop Forecasting
0 5 10 15 20 25 30
Population (1000’s)
0
40
80
120
160
200
240
Sales ($1000’s)
PIESHOP Forecasts
Coefficient of Sanity
Diagnostic Checking
H0 retain or reject
{Reject if p-value 0.05}
R2 (larger is “better”)
sy.x (smaller is “better”)
Analysis of Variance for Regression for Pieshop
SourceSum ofSquares d.f. Mean Square
Model 14,200.0 1 14,200.0
Error 1,530.0 8 191.25
Total 15,730.0 9test:
p = 0.00003SST
Coefficient of Determination
R2 = SSR / SST
= 90.27 %
thus, 90.27 percent of the variation in annual sales is
explained by the population.
Standard Error of the Estimate
sy.x = 13.8293
with
SSE = 1,530.0
Regression Analysis[Simple Regression]
*** End of Presentation ***
Questions?