# hw 3 - MAS362 Assignment#3 Due April 4 1 Use the data of 25...

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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 R-Sq= 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 R-Sq 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 (Import-new) and C13(GDP-new)). Check the residual assumptions and interpret the results. Better, but still not good (1.d) Use the following commands to create the log-transformed Imports and GDP for the data omitting USA and Netherlands. LET C22=LOGE(C12) LET C23=LOGE(C13) Name c22 ‘log-import’ Name c23 ‘log-GDP’ (1.f) Run the regression with C22 (log-import) as response and C23 (log-GDP) 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: R-Sq (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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expenditure (c57), State and local expenditures (C60), Unemployment rate (C71) and Total poverty rate (C151) as input variables. Save Residuals and Durbin-Watson (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 lag-crime rate. Lag c41 c241 Name C241 ‘lag-crime’ (2.d) Run a regression model with violent crime rate (C41) as the response variable and Lag-crime (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 Durbin-Watson (DW) statistics. (i.e. add the lag-crime 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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hw 3 - MAS362 Assignment#3 Due April 4 1 Use the data of 25...

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