chap 2-1 introduction to linear regression. chap 2-2 scatter plots and correlation a scatter plot...
TRANSCRIPT
Chap 2-1
Introduction to Linear Regression
Chap 2-2
Scatter Plots and Correlation
A scatter plot (or scatter diagram) is used to show the relationship between two variables
Correlation analysis is used to measure strength of the association (linear relationship) between two variables
Only concerned with strength of the relationship
No causal effect is implied
Chap 2-3
Scatter Plot Examples
y
x
y
x
y
y
x
x
Linear relationships Curvilinear relationships
Chap 2-4
Scatter Plot Examples
y
x
y
x
y
y
x
x
Strong relationships Weak relationships
(continued)
Chap 2-5
Scatter Plot Examples
y
x
y
x
No relationship
(continued)
Chap 2-6
Correlation Coefficient
The population correlation coefficient ρ (rho) measures the strength of the association between the variables
The sample correlation coefficient r is an estimate of ρ and is used to measure the strength of the linear relationship in the sample observations
(continued)
Chap 2-7
Features of ρand r
Unit free Range between -1 and 1 The closer to -1, the stronger the negative
linear relationship The closer to 1, the stronger the positive
linear relationship The closer to 0, the weaker the linear
relationship
Chap 2-8r = +.3 r = +1
Examples of Approximate r Values
y
x
y
x
y
x
y
x
y
x
r = -1 r = -.6 r = 0
Chap 2-9
Calculating the Correlation Coefficient
])yy(][)xx([
)yy)(xx(r
22
where:r = Sample correlation coefficientn = Sample sizex = Value of the independent variabley = Value of the dependent variable
])y()y(n][)x()x(n[
yxxynr
2222
Sample correlation coefficient:
or the algebraic equivalent:
Chap 2-10
Calculation Example
Tree Height
Trunk Diameter
y x xy y2 x2
35 8 280 1225 64
49 9 441 2401 81
27 7 189 729 49
33 6 198 1089 36
60 13 780 3600 169
21 7 147 441 49
45 11 495 2025 121
51 12 612 2601 144
=321 =73 =3142 =14111 =713
Chap 2-11
0
10
20
30
40
50
60
70
0 2 4 6 8 10 12 14
0.886
](321)][8(14111)(73)[8(713)
(73)(321)8(3142)
]y)()y][n(x)()x[n(
yxxynr
22
2222
Trunk Diameter, x
TreeHeight, y
Calculation Example(continued)
r = 0.886 → relatively strong positive linear association between x and y
Chap 2-12
Significance Test for Correlation
Hypotheses
H0: ρ = 0 (no correlation)
HA: ρ ≠ 0 (correlation exists)
Test statistic
(with n – 2 degrees of freedom)
2nr1
rt
2
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 2-13
Example: Produce Stores
Is there evidence of a linear relationship between tree height and trunk diameter at the .05 level of significance?
H0: ρ = 0 (No correlation)
H1: ρ ≠ 0 (correlation exists)
=.05 , df = 8 - 2 = 6
4.68
28.8861
.886
2nr1
rt
22
Chap 2-14
4.68
28.8861
.886
2nr1
rt
22
Example: Test Solution
Conclusion:There is evidence of a linear relationship at the 5% level of significance
Decision:Reject H0
Reject H0Reject H0
/2=.025
-tα/2
Do not reject H0
0 tα/2
/2=.025
-2.4469 2.44694.68
d.f. = 8-2 = 6
Chap 2-15
Introduction to Regression Analysis
Regression analysis is used to: Predict the value of a dependent variable based on
the value of at least one independent variable Explain the impact of changes in an independent
variable on the dependent variable
Dependent variable: the variable we wish to explain
Independent variable: the variable used to explain the dependent variable
Chap 2-16
Simple Linear Regression Model
Only one independent variable, x
Relationship between x and y is described by a linear function
Changes in y are assumed to be caused by changes in x
Chap 2-17
Types of Regression Models
Positive Linear Relationship
Negative Linear Relationship
Relationship NOT Linear
No Relationship
Chap 2-18
εxββy 10 Linear component
Population Linear Regression
The population regression model:
Population y intercept
Population SlopeCoefficient
Random Error term, or residualDependent
Variable
Independent Variable
Random Error component
Chap 2-19
Linear Regression Assumptions
Error values (ε) are statistically independent Error values are normally distributed for any
given value of x The probability distribution of the errors is
normal The probability distribution of the errors has
constant variance The underlying relationship between the x
variable and the y variable is linear
Chap 2-20
Population Linear Regression(continued)
Random Error for this x value
y
x
Observed Value of y for xi
Predicted Value of y for xi
εxββy 10
xi
Slope = β1
Intercept = β0
εi
Chap 2-21
i 0 1ˆ ˆy x
The sample regression line provides an estimate of the population regression line
Estimated Regression Model
Estimate of the regression
intercept
Estimate of the regression slope
Estimated (or predicted) y value
Independent variable
The individual random error terms ei have a mean of zero
Chap 2-22
Least Squares Criterion
and are obtained by finding the values of and that minimize the sum of the squared residuals
2 2
20 1
ˆe (y y)
ˆ ˆ(y ( x))
01
10
Chap 2-23
The Least Squares Equation
The formulas for and are:
algebraic equivalent:
1 22
ˆ( )
xy
xx
x yxy Sn
x Sx
n
1 2
( )( )ˆ( )
xy
xx
Sx x y y
x x S
0 1ˆ ˆy x
and
01
Chap 2-24
is the estimated average value of y when the value of x is zero
is the estimated change in the average value of y as a result of a one-unit change in x
Interpretation of the Slope and the Intercept
0
1
Chap 2-25
Finding the Least Squares Equation
The coefficients and will usually be found using computer software, such as Minitab
Other regression measures will also be computed as part of computer-based regression analysis
01
Chap 2-26
Simple Linear Regression Example
A real estate agent wishes to examine the relationship between the selling price of a home and its size (measured in square feet)
A random sample of 10 houses is selected Dependent variable (y) = house price in $1000s Independent variable (x) = square feet
Chap 2-27
Sample Data for House Price Model
House Price in $1000s(y)
Square Feet (x)
245 1400
312 1600
279 1700
308 1875
199 1100
219 1550
405 2350
324 2450
319 1425
255 1700
Chap 2-28
Regression Using MINATAB
Stat / Regression/ RegressionRegression Analysis: House price
versus Square Feet
The regression equation is House price = 98.2 + 0.110 Square Feet
Predictor Coef SE Coef T P
Constant 98.25 58.03 1.69 0.129
Square Feet 0.110 0.03297 3.33 0.010
S = 41.3303 R-Sq = 58.1% R-Sq(adj) = 52.8%
MINITAB OUTPUT
Analysis of Variance
Source DF SS MS F P
Regression 1 18935 18935 11.08 0.010
Residual Error 8 13666 1708
Total 9 32600
Chap 2-29
Chap 2-30
Graphical Presentation
Stat /Regression/ Fitted Line Plot
Chap 2-31
Interpretation of the Intercept,
is the estimated average value of Y when the value of X is zero (if x = 0 is in the range of observed x values) Here, no houses had 0 square feet, so = 98.25
just indicates that, for houses within the range of sizes observed, $98,250 is the portion of the house price not explained by square feet
0
House price = 98.25 + 0.110 Square Feet
0
0
Chap 2-32
Interpretation of the Slope Coefficient,
measures the estimated change in the average value of Y as a result of a one-unit change in X Here, = .110 tells us that the average value of a
house increases by 0.110 ($1000) = $110, on average, for each additional one square foot of size
1
1
House price = 98.2 + 0.110 Square Feet
1
Chap 2-33
Least Squares Regression Properties
The sum of the residuals from the least squares regression line is 0 ( )
The sum of the squared residuals is a minimum (minimized )
The simple regression line always passes through the mean of the y variable and the mean of the x variable
The least squares coefficients are unbiased estimates of β0 and β1
0)ˆ( yy
2)ˆ( yy
Chap 2-34
Explained and Unexplained Variation
Total variation is made up of two parts:
Re RSS SS SST s Total sum of
SquaresSum of Squares
RegressionSum of Squares Error (Residual)
2( )TSS y y 2Re ˆ( )sSS y y 2ˆ( )RSS y y
where:
= Average value of the dependent variable
y = Observed values of the dependent variable
= Estimated value of y for the given x valuey
y
Chap 2-35
= total sum of squares
Measures the variation of the yi values around their mean y
= error (residual) sum of squares
Variation attributable to factors other than the relationship between x and y
= regression sum of squares
Explained variation attributable to the relationship between x and y
(continued)
Explained and Unexplained Variation
SST
TSS
Re sSS
RSS
Chap 2-36
(continued)
Xi
y
x
yi
= (yi - y)2
= (yi - yi )2
= (yi - y)2
_
_
_
Explained and Unexplained Variation
y
y
y_y
TSS
Re sSS
RSS
Chap 2-37
The coefficient of determination is the portion of the total variation in the dependent variable that is explained by variation in the independent variable
The coefficient of determination is also called R-squared and is denoted as R2
Coefficient of Determination, R2
2 R
T
SSR
SS 1R0 2 where
Chap 2-38
Coefficient of determination
Coefficient of Determination, R2
2 of squares explained by regression
sum of squaresR
T
SS sumR
SS total
(continued)
Note: In the single independent variable case, the coefficient of determination is
where:R2 = Coefficient of determination
r = Simple correlation coefficient
22 rR
Chap 2-39
R2 = +1
Examples of Approximate R2 Values
y
x
y
x
R2 = 1
R2 = 1
Perfect linear relationship between x and y:
100% of the variation in y is explained by variation in x
Chap 2-40
Examples of Approximate R2 Values
y
x
y
x
0 < R2 < 1
Weaker linear relationship between x and y:
Some but not all of the variation in y is explained by variation in x
Chap 2-41
Examples of Approximate R2 Values
R2 = 0
No linear relationship between x and y:
The value of Y does not depend on x. (None of the variation in y is explained by variation in x)
y
xR2 = 0
Chap 2-42
Standard Error Estimation
House price = 98.2 + 0.110 Square Feet
Predictor Coef SE Coef T P
Constant 98.25 58.03 1.69 0.129
Square Feet 0.110 0.03297 3.33 0.010
S = 41.3303 R-Sq = 58.1% R-Sq(adj) = 52.8%
Source DF SS MS F P
Regression 1 18935 18935 11.08 0.010
Residual Error 8 13666 1708
Total 9 32600
Chap 2-43
Standard Error of Estimate
The standard deviation of the variation of observations around the regression line is estimated by
ReRe 1
ss
SSMS
n k
Where
= Sum of squares error (residual) n = Sample size
k = number of independent variables in the model
Re sSS
Chap 2-44
The Standard Deviation of the Regression Slope
The standard error of the regression slope coefficient ( ) is estimated by
1 2 22
ˆSe( )(x x) ( x)
xn
Res ResMS MS
where:
= Estimate of the standard error of the least squares slope
= Sample standard error of the estimate
1( )Se
ReRe
SS
n 2s
sMS
1
Chap 2-45
Comparing Standard Errors
y
y y
x
x
x
y
x
1small ( )Se
1large ( )Se
small ResMS
Relarge MS s
Variation of observed y values from the regression line
Variation in the slope of regression lines from different possible samples
Chap 2-46
Inference about the Slope: t Test
t test for a population slope Is there a linear relationship between x and y?
Null and alternative hypotheses H0: β1 = 0 (no linear relationship) H1: β1 0 (linear relationship does exist)
Test statistic
1 1
1
ˆ βt
ˆ( )Se
2nd.f.
where:
= Sample regression slope coefficient
β1 = Hypothesized slope
= Estimator of the standard error of the slope
1
1( )Se
Chap 2-47
House Price in $1000s
(y)
Square Feet (x)
245 1400
312 1600
279 1700
308 1875
199 1100
219 1550
405 2350
324 2450
319 1425
255 1700
Estimated Regression Equation:
The slope of this model is 0.110
Does square footage of the house affect its sales price?
Inference about the Slope: t Test
(continued)
House price = 98.25 + 0.110 Square Feet
Chap 2-48
Inferences about the Slope: t Test Example
H0: β1 = 0
HA: β1 0
Test Statistic: t = 3.33
There is sufficient evidence that square footage affects house price
From MINITAB output:
Reject H0
Decision:
Conclusion:
Reject H0Reject H0
/2=.025
-tα/2
Do not reject H0
0 tα/2
/2=.025
-2.3060 2.3060 3.329
d.f. = 10-2 = 8
Predictor Coef SE Coef T P
Constant 98.25 58.03 1.69 0.129
Square Feet 0.110 0.03297 3.33 0.010
Chap 2-49
Regression Analysis for Description
Confidence Interval Estimate of the Slope:
At 95% level of confidence, the confidence interval for the slope is (0.0337, 0.1858)
1 /2 1ˆ ˆ( )t Se d.f. = n - 2
This 95% confidence interval does not include 0.
Conclusion: There is a significant relationship between house price and square feet at the .05 level of significance
Chap 2-50
Confidence Interval for the Average y, Given x
Confidence interval estimate for the mean of y given a particular
Size of interval varies according to distance away from mean, x
20
/2 Re 2
(x x)1ˆ MS [ ]
n (x x)sy t
0x
Chap 2-51
Confidence Interval for an Individual y, Given x
Confidence interval estimate for an Individual value of y given a particular
20
/2 Re 2
(x x)1ˆ MS [1 ]
n (x x)sy t
This extra term adds to the interval width to reflect the added uncertainty for an individual case
0x
Chap 2-52
Interval Estimates for Different Values of x
y
x
Prediction Interval for an individual y, given
xp
y = + x
x
Confidence Interval for the mean of y, given
10
0x
0x
Chap 2-53
House Price in $1000s
(y)
Square Feet (x)
245 1400
312 1600
279 1700
308 1875
199 1100
219 1550
405 2350
324 2450
319 1425
255 1700
Estimated Regression Equation:
Example: House Prices
Predict the price for a house with 2000 square feet
House price = 98.25 + 0.110 Square Feet
Chap 2-54
house price 98.25 0.110 (sq.ft.)
98.25 0.110(2000)
318.25
Example: House Prices
Predict the price for a house with 2000 square feet:
The predicted price for a house with 2000 square feet is 318.25($1,000s) = $318,250
(continued)
Chap 2-55
Estimation of Mean Values: Example
Find the 95% confidence interval for the average price of 2,000 square-foot houses
Predicted Price Yi = 318.25 ($1,000s)
Confidence Interval Estimate for E(y)|
20
α/2 2
(x x)1y t [ ] 318.25 37.12
n (x x)ResMS
The confidence interval endpoints are 281.13 -- 355.37, or from $281,13 -- $355,37
0x
Chap 2-56
Estimation of Individual Values: Example
Find the 95% confidence interval for an individual house with 2,000 square feet
Predicted Price Yi = 317.85 ($1,000s)
Prediction Interval Estimate for y|
20
α/2 Re 2
(x x)1y t MS [1 ] 318.25 102.28
n (x x)s
The prediction interval endpoints are 215.97 -- 420.53, or from $215,97 -- $420,53
0x
Chap 2-57
Finding Confidence and Prediction Intervals MINITAB
Stat\Regression\Regression-option
Predicted Values for New Observations
New Obs Fit SE Fit 95% CI 95% PI
1 317.8 16.1 (280.7, 354.9) (215.5, 420.1)
Values of Predictors for New Observations
Square Feet
New Obs
1 2000
Chap 2-58
Finding Confidence and Prediction Intervals MINITAB
Stat\Regression\fitted line plot