Use information from this page to answer Question numbers 2 and 3. 2. Conduct goodness of fit or R and Adj. R analysis in the context of the above result. [10] 1. Prepare a multiple regression model in its population form for a time series data for Bangladesh in the context of the COVID-19 death rate. Make sure that you use the . expected sign of the coefficients and proper subscripts. [10] Using the following corruption perception index of three countries over seven years by Transparency International (TI) in the range of (0-100) where 100 is the least corruption. 3. Using the information from the print out conduct a T-test to test whether Afghanistan's corruption level has any significant impact in determining Bangladesh's corruption level at 5% level of significance. 4. Distinguish between a regression model and an ANOVA model with a practical example in the context of our favorite weight model. A data set is given as follows: Country AFG US BD 15 74 26 15 75 28 16 71 26 5. Using a one-way ANOVA show whether Country matter for corruption perception level or not at 5% level. Use the above data Year BD AFG US 2012 26 8 73 2013 27 73 2014 25 12 74 2015 25 11 76 2016 26 15 74 2017 28 15 75 2018 26 16 71 Assuming that the corruption level of Bangladesh depends on the corruption level of US and Afghanistan through the trading channel we run the following OLS regression. Regression Analysis: BD versus AFG, US Regression Equation BD - 285+0.041 AFC -0.039 US Coefficients Term Coaf. SE Coat T-Value P-Value VIE Constant 2015 24.6 1.16 0.311 AFC 0.041 0.159 0.26 0.809 101 US -0.039 0.331 -0.12 0.92 101 Model Summary Tas Rasa Rascadi) R-speed, 212%D0000% Analysis of Variance DF Adi SS Adi MS F-Value P-value Regression 2 0.74547 OTOY 273 0.04 01938 AFC 1 0.11240 0.11240 0.07 0.809 US 1 0.02337 0.02337 0.01 0.92 Error 4 671168 1.67792 Lack-at-Fit 3 6.21168 2.07056 4.14 0343 Pure Error 1 0.5000 0.50000 Tatal 6 6.85714 6. What is the difference between 'expected frequencies' (EJ) or "expected headcounts and observed frequencies' (ow in contingency table analysis? Explain. Find out whether country and corruption level are associated or not at 5% level by using a contingency table method. Follow all the steps. The numbers in the table represent observed headcounts in each category. [30] Source AFG 150 Country US BD 500 100 Most corrupt Moderately Corrupt Not corrupt Corrupt ion Level 0 320 500 300 200 150 200 1 2 Use information from this page to answer Question numbers 2 and 3. 2. Conduct goodness of fit or R and Adj. R analysis in the context of the above result. [10] 1. Prepare a multiple regression model in its population form for a time series data for Bangladesh in the context of the COVID-19 death rate. Make sure that you use the . expected sign of the coefficients and proper subscripts. [10] Using the following corruption perception index of three countries over seven years by Transparency International (TI) in the range of (0-100) where 100 is the least corruption. 3. Using the information from the print out conduct a T-test to test whether Afghanistan's corruption level has any significant impact in determining Bangladesh's corruption level at 5% level of significance. 4. Distinguish between a regression model and an ANOVA model with a practical example in the context of our favorite weight model. A data set is given as follows: Country AFG US BD 15 74 26 15 75 28 16 71 26 5. Using a one-way ANOVA show whether Country matter for corruption perception level or not at 5% level. Use the above data Year BD AFG US 2012 26 8 73 2013 27 73 2014 25 12 74 2015 25 11 76 2016 26 15 74 2017 28 15 75 2018 26 16 71 Assuming that the corruption level of Bangladesh depends on the corruption level of US and Afghanistan through the trading channel we run the following OLS regression. Regression Analysis: BD versus AFG, US Regression Equation BD - 285+0.041 AFC -0.039 US Coefficients Term Coaf. SE Coat T-Value P-Value VIE Constant 2015 24.6 1.16 0.311 AFC 0.041 0.159 0.26 0.809 101 US -0.039 0.331 -0.12 0.92 101 Model Summary Tas Rasa Rascadi) R-speed, 212%D0000% Analysis of Variance DF Adi SS Adi MS F-Value P-value Regression 2 0.74547 OTOY 273 0.04 01938 AFC 1 0.11240 0.11240 0.07 0.809 US 1 0.02337 0.02337 0.01 0.92 Error 4 671168 1.67792 Lack-at-Fit 3 6.21168 2.07056 4.14 0343 Pure Error 1 0.5000 0.50000 Tatal 6 6.85714 6. What is the difference between 'expected frequencies' (EJ) or "expected headcounts and observed frequencies' (ow in contingency table analysis? Explain. Find out whether country and corruption level are associated or not at 5% level by using a contingency table method. Follow all the steps. The numbers in the table represent observed headcounts in each category. [30] Source AFG 150 Country US BD 500 100 Most corrupt Moderately Corrupt Not corrupt Corrupt ion Level 0 320 500 300 200 150 200 1 2