MAS362
Assignment #3
Due: April 4
1.
Use the data of 25 countries in Question 7 of Chapter 6 (page 263), “IMPORT6.MTW” to
answer the following questions.
(1.a) Construct the scatter plots of Imports versus GDP and describe the pattern of relationship.
Other than the 1 outlier, as GDP increases so do imports.
(1.b) Run the regression using Imports as the response variable and GDP as the input variable,
save the residuals. Interpret the regression results. Do any of the assumptions of the
regression model appear to be violated? Test the normality assumption of residuals at 5%
level of significance.
The whole model is distorted because of that one outlier.
Imports = 95.7 + 0.0906 GDP
S= 358.931
RSq= 15.8%
Every time GDP goes up by 1, imports increase by .0906. This is not a strong predictor
because S is very high and RSq is very low. Also P<.05 meaning this data is not adequate.
(1.c) Omit the data of USA (row 25) and Netherlands (row 20) and run the regression again
(save the new variables in C12 (Importnew) and C13(GDPnew)). Check the residual
assumptions and interpret the results.
Better, but still not good
(1.d) Use the following commands to create the logtransformed Imports and GDP for the data
omitting USA and Netherlands.
LET C22=LOGE(C12)
LET C23=LOGE(C13)
Name c22 ‘logimport’
Name
c23 ‘logGDP’
(1.f) Run the regression with C22 (logimport) as response and C23 (logGDP) as input.
Interpret the results and check the normality assumption of residuals.
For every 1% increase of GDP, imports increase by .84%.
(1.g) Which of the results in (1.b), (1.c) and (1.g) best describe the data? Explain.
2 criteria:
RSq (s), residual that looks better. The Last one (1.g)
2.
Open the MINITAB project file ‘proj1a.MPJ’. Variable names are listed on page 6.
(2.a) Run a regression model with violent crime rate (C41) as the response variable and
GDP
(C51), Consumption (C52), private investment (C55), Fixed investment (c56), Government
1
MAS362
Assignment #3
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State and local expenditures (C60), Unemployment rate (C71) and Total
poverty rate (C151) as input variables. Save Residuals and DurbinWatson (DW) statistics.
(2.b) Check the normality assumption of the residuals. Interpret the regression estimates and the
DW statistics.
(2.c) Use the following commands to create the lagcrime rate.
Lag c41 c241
Name C241 ‘lagcrime’
(2.d) Run a regression model with violent crime rate (C41) as the response variable and
Lagcrime
(C241), Consumption (C52), private investment (C55), Fixed investment (c56), Government
expenditure (c57),
State and local expenditures (C60), Unemployment rate (C71), GDP (C51),
and Total poverty rate (C151) as input variables. Save Residuals and DurbinWatson (DW)
statistics. (i.e. add the lagcrime in the model, make sure that GDP (C51) and total poverty rate
(C151) are the last two input variables.)
(2.e) Check the normality assumption of the residuals. Interpret the regression estimates and the
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 Spring '10
 Mrs.Nicholadies
 Linear Regression, Regression Analysis, Errors and residuals in statistics, violent crime rate, Total Poverty Rate

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