quantitative business analysis for decision making simple linear regression
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Quantitative Business Analysis for Decision Making
Simple Linear Simple Linear RegressionRegression
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Lecture OutlinesLecture Outlines
Scatter Plots Correlation Analysis Simple Linear Regression Model Estimation and Significance Testing Coefficient of Determination Confidence and Prediction Intervals Analysis of Residuals
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Regression Analysis ?Regression Analysis ?
Regression analysis is used for modeling the mean of “response” variable Y as afunction of “predictor” variables X1, X2,..,
Xk.
When K = 1, it is called simple regression
analysis.
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Random SampleRandom Sample
Y: Response Variable, X: Predictor Variable
For each unit in a random sample of n, the pair
(X, Y) is observed resulting a random sample:
(x1, y1), (x2, y2),... (xn, yn)
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Scatter PlotScatter Plot
Scatter Plot is a graphical displays of the sample (x1, y1), (x2, y2),... (xn, yn) by n points in 2-dimension.
It will suggest if there is a relationship between X and Y
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A Scatter Plot Showing Linear A Scatter Plot Showing Linear TrendTrend
16 21 26
15
20
25
Nielsen
Peo
pleM
A Scatter Plot Showing Linear Trend
of Peoples Ratings and Nielsen Ratings
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A Scatter Plot Showing No Linear A Scatter Plot Showing No Linear TrendTrend
-1 0 1
-1
0
1
Today
Yes
terd
a
A Scatter Plot Showing No Linear Trend
of Today's With Yesterday's DJIA
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Modeling linear Trend Modeling linear Trend
A perfect linear relationship between Y A perfect linear relationship between Y
and X and X exists if . Coefficient is the slope--quantifying the amount of change in y corresponding to one unit change in x. There are no perfect linear relationships
in practical world.
X of XY
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Simple Linear Regression Simple Linear Regression ModelModel
Model: Model:
is linear function (nonrandom) is random error. It is assumed to be normally distributed mean 0 and
standard deviation . So are parameters of the model
XY
and ,
X
Xy
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EstimationEstimation
Simple linear regression analysis estimates the mean of
Y (linear trend) by
and
Xy bxay ˆ
xbya
2)(
))((
xx
yyxxb
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Standard deviation
Standard deviation (s) of the sample of n points in the scatter plot around the estimated regression line is:
bxay ˆ
2
ˆ 2
n
yys
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Testing the Slope of Linear Testing the Slope of Linear TrendTrend
For Testing
compute t-statistic and its p value:
0a00 :H vs.:H
bs
b 0-statistic-t
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Coefficient of Determination: Coefficient of Determination: RR22
A quantification of the significance of estimated model is denoted by R2.
R2 > 85% = significant model R2 < 85% = model is perceived as
inadequate Low R2 will suggest a need for additional
predictors for modeling the mean of Y
bxay ˆ
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Correlation Coefficient: rCorrelation Coefficient: r
The correlation coefficient r is the square root of R2. It is a number between -1 and 1.
– Closer r is to -1 or 1, the stronger is the linear trend
– Its sign is positive for increasing trend (slope b is positive)
– Its sign is negative for decreasing trend (slope b is negative)
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Confidence and Prediction Confidence and Prediction IntervalsIntervals
To estimate by a confidence interval, or to predict response Y
corresponding to its predictor value x = x0 – 1. Compute:
– 2. compute:
xy
0ˆ bxay
yesy ˆ..ˆ
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What is ?i.e. Standard Error of
yes ˆ..
For estimating ,y
2
20
)(
)(1)ˆ.(.
xx
xx
nsyes
For Predicting Y,
2
20
)(
)(11)ˆ.(.
xx
xx
nsyes
y
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Analysis of ResidualsAnalysis of ResidualsResiduals are defined:
Residual analysis is used to check the normality and homogeneity of variance assumptions of random errors .
Histogram or box plot of residuals will help to ascertain if errors are normally distributed.
2,....n 1,i ,ˆ iii yye
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Analysis of Residuals Analysis of Residuals (con’t)(con’t)
Plot of residual against observed predictor values xi will help ascertain
homogeneity assumption. – random appearance = homogeneity of
variance assumption is valid.– non-random appearance
=homogeneity assumption is not valid and variance is dependent on predictor values.
ie
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