Statistics for Management and Economics-Sixth Edition Gerald Keller

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Statistics for Management and Economics-Sixth
EditionGerald Keller Brian Warrack

• Chapter 17 Simple Linear Regression and
  Correlation

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Figure 17. 1 Scatter diagram with regression line
for Example 17.1
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Figure 17.2 Calculation of residuals in Example
17.1
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Applet 18.1 Fitting the Regression Line
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Excel Summary Output Regression Statistics
from Example 17.2
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Minitab Regression Analysis Price versus
Odometer from Example 17.2
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Figure 17.3 Distribution of y given x
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Figure 17.4 Bivariate normal distribution
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Figure 17.5 Scatter diagram of entire population
with ß1 0
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Excel (See page 612 from Example 17.2) and this
also includes Example 17.4
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Minitab (See Page 612 from Example 17.2) This
also includes Example 17.4
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Figure 17.6 Partitioning the deviation for i5
in Example 17.1
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Applet 19.1 Analysis of Regression Deviations
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Excel- Summary Output Regression Statistics from
Example 17.6
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Minitab-Regression Analysis Nortel versus TSE
from Example 17.6
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Excel Prediction Interval from Example 17.7
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Minitab Predicted Values for New Observations
from Example 17.7
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Figure 17.77 Interval estimates and prediction
intervals.
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Excel Foreign Index Funds Solution
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Minitab Correlations US Index, Japanese Index
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Excel Spearman Rank Correlation from Example
17.8
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Minitab Correlations Rank apt, Rank Per from
Example 17.8
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Excel Residual Output from 17.9 Regression
Diagnostics 1
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Minitab From 17.9 Regression Diagnostics -1
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Excel histogram of residuals for Example 17.2
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Figure 17.8 Plot of residuals depicting
heteroscedasticity
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Figure 17.9 Plot of residuals depicting
homoscedasticity
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Excel plot of predicted values versus residuals
for Example 17.2
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Figure 17.10 Plot of residuals versus time
indicating autocorrelation (alternating)
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Figure 17.11 Plot of residuals versus time
indicating autocorrelation (increasing)
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Figure 17.12 Plot of residuals versus time
indicating independence
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Figure 17.13 Scatter diagram with one outlier
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Figure 17.15 Scatter diagram without the
influential observation