chapter 6 & 7 linear regression & correlation. goal : understand and interpret the terms...
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![Page 1: CHAPTER 6 & 7 LINEAR REGRESSION & CORRELATION. GOAL : Understand and interpret the terms dependent variable and independent variable. Draw a scatter diagram](https://reader036.vdocument.in/reader036/viewer/2022062421/56649e5e5503460f94b56fd6/html5/thumbnails/1.jpg)
CHAPTER 6 & 7LINEAR REGRESSION & CORRELATION
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GOAL :
• Understand and interpret the terms dependent variable and independent variable.
• Draw a scatter diagram.
• Calculate and interpret the coefficient of correlation, the coefficient of determination, and the standard error of estimate.
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Linear Regression & correlation
Correlation
Least square model
Assumption of the least square model
Standard error of the estimate
Strength of the relationship
Coefficient of correlation
Regression Analysis
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Correlation Analysis
The study of the relationship between variablesor
A group of techniques to measure the strength of the association between two variables
Note that here we consider two variables One is the independent variable The second is the dependent variable,
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Variables
The Independent Variable provides the basis for estimation. It is the predictor variable. It is scaled on X axis.
The Dependent Variable is the variable being predicted or estimated. It is scaled on Y-axis.
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Scatter Diagram
A chart that portrays the relationship between the two variables.
Dependent variable – vertical (or Y) axis Independent variable – horizontal (or X)
axis
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EXAMPLE 1
Chaminda Liyanage , the president of IMSSA, is concerned about the cost to students of textbooks. He believes there is a relationship between the number of pages in the text and the selling price of the book. To provide insight into the problem he selects a sample of eight textbooks currently on sale in the bookstore.
Draw a scatter diagram.
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EXAMPLE 1
Book Page Price ($)
Operation Re search 500 84
Basic Algebra 700 75
Economics 800 99
Management Science 600 72
Business Management 400 69
Industrial law 500 81
Human Resource 600 63
Information Technology 800 93
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Example 1
0
20
40
60
80
100
120
0 200 400 600 800 1000
Pri
ce
# of pages
Scatter diagram showing no. of pages and price
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Correlation Analysis
Correlation Analysis is a group of
statistical techniques used to measure
the strength of the association between
two variables.
The Coefficient of Correlation (r) is a
measure of the strength of the
relationship between two variables.
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The Coefficient of Correlation (r) A measure of the strength of the linear
relationship between two variables. It can range from -1.00 to 1.00. Values of -1.00 or 1.00 indicate perfect and
strong correlation. Negative values indicate an inverse
relationship Positive values indicate a direct relationship. Values close to 0.0 indicate weak correlation
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The strength and direction of the correlation
-1.00 -0.50 0 0.50 1.00
Negative correlation Positive correlation
Perfect Negative
correlationModerate Negative
correlation
Strong Negative
correlation
Weak Negative
correlation
No correlation
Moderate Positive
correlation
Weak Positive
correlation
Strong Positive
correlation
Perfect Positive
correlation
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0 1 2 3 4 5 6 7 8 9 10
10 9 8 7 6 5 4 3 2 1 0
X
Y
Perfect Negative Correlation
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Perfect Positive Correlation
0 1 2 3 4 5 6 7 8 9 10
10 9 8 7 6 5 4 3 2 1 0
X
Y
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Strong Positive Correlation
0 1 2 3 4 5 6 7 8 9 10
10 9 8 7 6 5 4 3 2 1 0
X
Y
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Zero Correlation
0 1 2 3 4 5 6 7 8 9 10
10 9 8 7 6 5 4 3 2 1 0
X
Y
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EXAMPLE 1
Compute the correlation coefficient, interpret the strength.
Determine the coefficient of determination and interpret.
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Coefficient of correlation (r)
])()(][)()([
))(()(2222 YYnXXn
YXXYnr
Formula for coefficient of correlation or (correlation coefficient )
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Coefficient of Determination (r2) The proportion of the total variation in the
dependent variable (Y) that is explained or accounted for, by the variation in the independent variable (X).
It is the square of the coefficient of correlation.
It ranges from 0 to 1. It does not give any information on the
direction of the relationship between the variables.
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Example 1 continued
6140
636606518900400015038
63690042003978
])()(][)()([
))(()(
22
2222
.
)(),(),(,,(
))(,(),(
YYnXXn
YXXYnr
Book Page-X Price ($)-Y X*X Y*Y XYOperation Research 500 84 250000 7056 42000Basic Algebra 700 75 490000 5625 52500Economics 800 99 640000 9801 79200Management Science 600 72 360000 5184 43200Business Management 400 69 160000 4761 27600Industrial law 500 81 250000 6561 40500Human Resource 600 63 360000 3969 37800Information Technology 800 93 640000 8649 74400total 4900 636 3150000 51606 397200
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SUMMARY OUTPUT
Regression StatisticsMultiple Regression 0.613878889R Square 0.376847291Adjusted R Square 0.272988506Standard Error 10.41290408Observations 8
Example 1 continued
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EXAMPLE 1 continued
The correlation between the number of pages and the selling price of the book is r =0.614.
This indicates a moderate association between the variable.
Coefficient of determination r2 = 0.376 37.6% of the variation in the price of the book is
accounted by variation on the page number
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Exercise
A production supervisor wishes to find the relationship between the number of workers on a job and the number of units produced for a shift. Listed below is the result for a sample of 8 days.
Workers Units 9 12 3 14 5 9 7 14 12 17
6 1313 17
4 9a. Identify the dependent and independent variableb. Determine the coefficient of correlation c. Determine the coefficient of determination
d. Interpret your findings in a. and b.
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Regression Analysis
In regression analysis we use the independent variable (X) to estimate the dependent variable (Y).
The relationship between the variables is linear.
Both independent and dependent variable must be interval or ratio scale.
The least squares criterion is used to determine the regression equation.
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Example
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Least Squares Principle
Gives the best fitting line Minimizes the sum of the squares of the
vertical distance between the actual “y” values and the predicted “y” values
Regression line is determined by using a mathematical method
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Example
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Regression Equation
Y’ = a + bX, where: Y’ is the average predicted value of Y (or estimated
value of y) for a selected value of X. a is the constant or Y-intercept.
It is the estimated Y’ value when X=0 b is the slope of the line, Shows the amount of change in Y’ for a change of one
unit in X Positive value of b indicates a direct relationship between
two variables Negative value of b indicates an inverse relationship
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Regression Equation
a is computed using;
b is computed using;
aYn
bXn
bn XY X Y
n X X
( ) ( )( )
( ) ( )
2 2
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22 XXn
YXXYnb
Develop a regression equation for the information given in EXAMPLE 1 that can be used to estimate the selling price based on the number of pages
05143.)900,4()000,150,3(8
)636)(900,4()200,397(82
b 0.48
8
900,405143.0
8
636a
aYn
bXn
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The regression equation is:Y’ = 48.0 + .05143X
The equation crosses the Y-axis at $48. A book with no pages would cost $48.
The slope of the line is 0.05143. Each addition page costs about 0.05 cents
The sign of the b value and the sign of r will always be the same.
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EXAMPLE 1
14.89)800(05143.00.48
05143.00.48
XY
We can use the regression equation to estimate values of Y.
The estimated selling price of an 800 page book is $89.14, found by
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Assumptions Underlying Linear Regression
For each value of X, there is a group of Y values, and these Y values are normally distributed.
The means of these normal distributions of Y values all lie on the straight line of regression.
The standard deviations of these normal distributions are equal.
The Y values are statistically independent. This means that in the selection of a sample, the Y values chosen for a particular X value do not depend on the Y values for any other X values
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The Standard Error of Estimate
2
)()(
2
)(
2
2
.
n
XYbYaY
n
YYs xy
The standard error of estimate measures the scatter, or dispersion or the variation, of the observed values around the line of regression It is in the same units as the dependent variable It is based on the squared deviations from the regression
line Small values indicate that the points cluster closely about
the regression line
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412.1028
)200,397(05143.0)636(48606,51
2
2
.
n
XYbYaYs xy
Find the standard error of estimate for the problem involving the number of pages in a book and the selling price in Example 1
We can use the regression equation to estimate values of Y.
The estimated selling price of an 800 page book is $89.14, found by
68% of the prices of books with page 800 falls between $89.14±10.41