The document provides an analysis of the relationship between house prices and their sizes using simple linear regression, including a dataset of house prices and sizes. It details the regression output from Excel, explaining statistics such as the coefficient of determination (r-squared), which indicates that 58.08% of the variation in house prices is explained by house size. The findings also include a hypothesis test confirming that square footage has a statistically significant effect on house prices.

1. 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
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2. 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
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3. 0
50
100
150
200
250
300
350
400
450
0 500 1000 1500 2000 2500 3000
Square Feet
House
Price
($1000s)
Graphical Presentation
• House price model: scatter plot
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4. Regression Using Excel
• Tools / Data Analysis / Regression
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5. Excel Output
Regression Statistics
Multiple R 0.76211
R Square 0.58082
Adjusted R Square 0.52842
Standard Error 41.33032
Observations 10
ANOVA
df SS MS F Significance F
Regression 1 18934.9348 18934.9348 11.0848 0.01039
Residual 8 13665.5652 1708.1957
Total 9 32600.5000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
The regression equation is:
house price 98.24833 0.10977 (square feet)
 
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ˆ 98.24833 0.10977
y X
 

6. 0
50
100
150
200
250
300
350
400
450
0 500 1000 1500 2000 2500 3000
Square Feet
House
Price
($1000s)
Graphical Presentation
• House price model: scatter plot and regression
line
feet)
(square
0.10977
98.24833
price
house 

Slope
= 0.10977
Intercept
= 98.248
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7. Interpretation of the Intercept, b0
• b0 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 b0 = 98.24833 has
no meaningful interpretation
feet)
(square
0.10977
98.24833
price
house 

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8. Interpretation of the Slope Coefficient, b1
• b1 measures the estimated change in the
average value of Y as a result of a one-unit
change in X
• Here, b1 = .10977 tells us that the average value of a
house increases by .10977($1000) = $109.77, on average,
for each additional one square feet of size
feet)
(square
0.10977
98.24833
price
house 

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9. Measures of Variation
• Total variation is made up of two parts:
SSE
SSR
SST 

Total Sum of
Squares
Regression Sum
of Squares
Error Sum of
Squares
 
 2
i )
y
(y
SST  
 2
i
i )
y
(y
SSE ˆ
 
 2
i )
y
y
(
SSR ˆ
where:
= Average value of the dependent variable
yi = Observed values of the dependent variable
i = Predicted value of y for the given xi value
ŷ
y
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10. • SST = total sum of squares
• Measures the variation of the yi values around their mean, y
• SSR = regression sum of squares
• Explained variation attributable to the linear relationship
between x and y
• SSE = error sum of squares
• Variation attributable to factors other than the linear relationship
between x and y
(continued)
Measures of Variation
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11. • 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
1
R
0 2


note:
squares
of
sum
total
squares
of
sum
regression
SST
SSR
R2


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12. 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
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13. Examples of Approximate
r2 Values
Y
X
Y
X
0 < r2 < 1
Weaker linear relationships
between X and Y:
Some but not all of the
variation in Y is explained
by variation in X
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14. 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
X
r2 = 0
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15. Excel Output
Regression Statistics
Multiple R 0.76211
R Square 0.58082
Adjusted R Square 0.52842
Standard Error 41.33032
Observations 10
ANOVA
df SS MS F Significance F
Regression 1 18934.9348 18934.9348 11.0848 0.01039
Residual 8 13665.5652 1708.1957
Total 9 32600.5000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
58.08% of the variation in
house prices is explained by
variation in square feet
0.58082
32600.5000
18934.9348
SST
SSR
R2



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16. Correlation and
• The coefficient of determination, R2, for a simple
regression is equal to the simple correlation
squared
2 2
xy
r
R 
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2
R

17. Estimation of Model Error Variance
• An estimator for the variance of the population model error is
• Division by n – 2 instead of n – 1 is because the simple regression model uses two estimated
parameters, b0 and b1, instead of one
is called the standard error of the estimate
2
n
SSE
2
n
e
s
σ
n
1
i
2
i
2
e
2







ˆ
2
e
e s
s 
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18. Excel Output
Regression Statistics
Multiple R 0.76211
R Square 0.58082
Adjusted R Square 0.52842
Standard Error 41.33032
Observations 10
ANOVA
df SS MS F Significance F
Regression 1 18934.9348 18934.9348 11.0848 0.01039
Residual 8 13665.5652 1708.1957
Total 9 32600.5000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
41.33032
se 
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19. Comparing Standard Errors
Y
Y
X X
e
s
small e
s
large
se is a measure of the variation of observed y
values from the regression line
The magnitude of se should always be judged relative to the size
of the y values in the sample data
i.e., se = $41.33K is moderately small relative to house prices in the $200 - $300K range
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20. Inferences About the Regression Model
• The variance of the regression slope coefficient (b1) is estimated by
2
x
2
e
2
i
2
e
2
1)s
(n
s
)
x
(x
s
s
1
b





where:
= Estimate of the standard error of the least squares slope
= Standard error of the estimate
1
b
s
2
n
SSE
se


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21. Excel Output
Regression Statistics
Multiple R 0.76211
R Square 0.58082
Adjusted R Square 0.52842
Standard Error 41.33032
Observations 10
ANOVA
df SS MS F Significance F
Regression 1 18934.9348 18934.9348 11.0848 0.01039
Residual 8 13665.5652 1708.1957
Total 9 32600.5000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
0.03297
s 1
b 
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22. 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)
• Under H0, the test statistic
1
1 1
~ ( 2)
b
b
t t n
s


 
where:
b1 = regression slope
coefficient
β1 = hypothesized slope
sb1 = standard
error of the slope
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23. 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
(sq.ft.)
0.1098
98.25
price
house 

Estimated Regression Equation:
The slope of this model is 0.1098
Does square footage of the house
affect its sales price?
Inference about the Slope: t Test
(continued)
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24. Inferences about the Slope: t Test Example
H0: β1 = 0
H1: β1  0
From Excel output:
Coefficients Standard Error t Stat P-value
Intercept 98.24833 58.03348 1.69296 0.12892
Square Feet 0.10977 0.03297 3.32938 0.01039
1
b
s
t
b1
3.32938
0.03297
0
0.10977
s
β
b
t
1
b
1
1





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25. Inferences about the Slope: t Test Example
H0: β1 = 0
H1: β1  0
Test Statistic: t = 3.329
There is sufficient evidence
that square footage affects
house price
From Excel output:
Reject H0
Coefficients Standard Error t Stat P-value
Intercept 98.24833 58.03348 1.69296 0.12892
Square Feet 0.10977 0.03297 3.32938 0.01039
1
b
s t
b1
Decision:
Conclusion:
Reject H0
Reject H0
a/2=.025
-tn-2,α/2
Do not reject H0
0
a/2=.025
-2.3060 2.3060 3.329
d.f. = 10-2 = 8
t8,.025 = 2.3060
(continued)
tn-2,α/2
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26. Inferences about the Slope: t Test Example
H0: β1 = 0
H1: β1  0
P-value = 0.01039
There is sufficient evidence
that square footage affects
house price
From Excel output:
Reject H0
Coefficients Standard Error t Stat P-value
Intercept 98.24833 58.03348 1.69296 0.12892
Square Feet 0.10977 0.03297 3.32938 0.01039
P-value
Decision: P-value < α so
Conclusion:
(continued)
This is a two-tail test, so
the p-value is
P(t > 3.329)+P(t < -3.329)
= 0.01039
(for 8 d.f.)
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27. Confidence Interval Estimate for the Slope
Confidence Interval Estimate of the Slope:
Excel Printout for House Prices:
At 95% level of confidence, the confidence interval for
the slope is (0.0337, 0.1858)
1
1 b
α/2
2,
n
1
1
b
α/2
2,
n
1 s
t
b
β
s
t
b 
 



Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
d.f. = n - 2
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28. Since the units of the house price variable is
$1000s, we are 95% confident that the average
impact on sales price is between $33.70 and
$185.80 per square foot of house size
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
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
Confidence Interval Estimate for the Slope
(continued)
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29. F-Test for Significance
• F Test statistic:
where
MSE
MSR
F 
1
k
n
SSE
MSE
k
SSR
MSR




where F follows an F distribution with k numerator and (n – k - 1)
denominator degrees of freedom
(k = the number of independent variables in the regression model)
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30. Excel Output
Regression Statistics
Multiple R 0.76211
R Square 0.58082
Adjusted R Square 0.52842
Standard Error 41.33032
Observations 10
ANOVA
df SS MS F Significance F
Regression 1 18934.9348 18934.9348 11.0848 0.01039
Residual 8 13665.5652 1708.1957
Total 9 32600.5000
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386
Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580
11.0848
1708.1957
18934.9348
MSE
MSR
F 


With 1 and 8 degrees
of freedom
P-value for
the F-Test
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31. H0: β1 = 0
H1: β1 ≠ 0
a = .05
df1= 1 df2 = 8
Test Statistic:
Decision:
Conclusion:
Reject H0 at a = 0.05
There is sufficient evidence that
house size affects selling price
0
a = .05
F.05 = 5.32
Reject H0
Do not
reject H0
11.08
MSE
MSR
F 

Critical
Value:
Fa = 5.32
F-Test for Significance
(continued)
F
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32. 317.85
0)
0.1098(200
98.25
(sq.ft.)
0.1098
98.25
price
house





Predict the price for a house
with 2000 square feet:
The predicted price for a house with 2000
square feet is 317.85($1,000s) = $317,850
Predictions Using Regression Analysis
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33. Graphical Analysis
• The linear regression model is based on minimizing
the sum of squared errors
• If outliers exist, their potentially large squared errors
may have a strong influence on the fitted regression
line
• Be sure to examine your data graphically for outliers
and extreme points
• Decide, based on your model and logic, whether the
extreme points should remain or be removed
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34. Summary
• Introduced the linear regression model
• Reviewed correlation and the assumptions of linear
regression
• Discussed estimating the simple linear regression
coefficients
• Described measures of variation
• Described inference about the slope
• Addressed estimation of mean values and prediction
of individual values
SimpleLinearRegEx1-34