Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.
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Transcript of Chapter 11 Correlation and Simple Linear Regression Statistics for Business (Econ) 1.
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Chapter 11Correlation and
Simple Linear Regression
Statistics for Business(Econ)
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Introduction
• In this chapter we employ Regression Analysisto examine the relationship among quantitative variables.
• The technique is used to predict the value of one variable (the dependent variable - y) based on the value of other variables (independent variables x1, x2,…xk.)
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Correlation is a statistical technique that is used to measure and describe a relationship between two variables. The correlation between two variables reflects the degree to which the variables are related.For example:
A researcher interested in the relationship between nutrition and IQ could observe the dietary patterns for a group of children and then measure their IQ scores.A business analyst may wonder if there is any relationship between profit margin and return on capital for a group of public companies.
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A set of n= 6 pairs of scores (X and Y values) is shown in a table and in a scatterplot. The scatterplot allows you to see the relationship between X and Y.
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Positive correlation
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Negative correlation
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Non-linear relationship
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A strong positive relationship, approximately +0.90;
A relatively weak negative correlation, approximately -0.40
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A perfect negative correlation, -1.00
No linear trend, 0.00.
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A demonstration of how one extreme data point (an outrider) can influence the value of a correlation.
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A demonstration of how one extreme data point (an outrider) can influence the value of a correlation.
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Pearson correlation The most common measure of correlation is the Pearson Product Moment Correlation (called Pearson's correlation for short).
=
=
1
1
n
yyxxs
n
iii
xy
yx
xy
ss
sr
correlation coefficient
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The value r2 is called the coefficient of determination because it measures the proportion of variability in one variable that can be determined from the relationship with the other variable. A correlation of r =0.80 (or -0.80), for example, means that r2 =0.64 (or 64%) of the variability in the Y scores can be predicted from the relationship with X.
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Least square fit
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• The linear model
y = dependent variablex = independent variable0 = y-intercept
1 = slope of the line
= error variable
xy 10 xy 10
x
y
0 Run
Rise = Rise/Run
0 and 1 are unknown,therefore, are estimated from the data.
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To calculate the estimates of the coefficientsthat minimize the differences between the data points and the line, use the formulas:
xbyb
s
)Y,Xcov(b
10
2x
1
xbyb
s
)Y,Xcov(b
10
2x
1
The regression equation that estimatesthe equation of the first order linear modelis:
xbby 10ˆ xbby 10ˆ
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• Example 12.1 Relationship between odometer reading and a used car’s selling price.
– A car dealer wants to find the relationship between the odometer reading and the selling price of used cars.
– A random sample of 100 cars is selected, and the data recorded.
– Find the regression line.
Car Odometer Price1 37388 53182 44758 50613 45833 50084 30862 57955 31705 57846 34010 5359
. . .
. . .
. . .
Independent variable x
Dependent variable y
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• Solution– Solving by hand
• To calculate b0 and b1 we need to calculate several statistics first;
;41.411,5y
;45.009,36x
256,356,11n
)yy)(xx()Y,Xcov(
688,528,431n
)xx(s
ii
2i2
x
where n = 100.
533,6)45.009,36)(0312.(41.5411xbyb
0312.688,528,43256,356,1
s
)Y,Xcov(b
10
2x
1
x0312.533,6xbby 10
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4500
5000
5500
6000
19000 29000 39000 49000
OdometerPrice
– Using the computer (see file Xm17-01.xls)
SUMMARY OUTPUT
Regression StatisticsMultiple R 0.806308R Square 0.650132Adjusted R Square0.646562Standard Error151.5688Observations 100
ANOVAdf SS MS F Significance F
Regression 1 4183528 4183528 182.1056 4.4435E-24Residual 98 2251362 22973.09Total 99 6434890
CoefficientsStandard Error t Stat P-valueIntercept 6533.383 84.51232 77.30687 1.22E-89Odometer -0.03116 0.002309 -13.4947 4.44E-24
x0312.533,6y
Tools > Data analysis > Regression > [Shade the y range and the x range] > OK
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This is the slope of the line.For each additional mile on the odometer,the price decreases by an average of $0.0312
4500
5000
5500
6000
19000 29000 39000 49000
Odometer
Price
x0312.533,6y
The intercept is b0 = 6533.
6533
0 No data
Do not interpret the intercept as the “Price of cars that have not been driven”