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Research Question: "Is there a linear relationship between Total Expenditure on Health as \% of GDP 2003 (TExpen_2003) and Infant Mortality rate (per 1000 live births) in 2004 (IM_2004)?" To help you check the relevant assumptions of the simple linear regression, four graphs are provided below: \begin{tabular}{|r|r|r|r|} \hline & \multicolumn{1}{|c|}{A} & \multicolumn{1}{|c|}{B} & C \\ \hline 1 & IM_2004 & TErpen_2003 \\ \hline 2 & 122.35 & 2.4 & \\ \hline 3 & 108.78 & 2.7 & \\ \hline 4 & 106.69 & 4.4 & \\ \hline 5 & 98.79 & 4.8 & \\ \hline 6 & 110.52 & 3.7 & \\ \hline 7 & 98.66 & 6 & \\ \hline 8 & 108.18 & 4 & \\ \hline 9 & 107.37 & 4 & \\ \hline 10 & 53.87 & 7.9 & \\ \hline 1 & 98.18 & 5.9 & \\ \hline 12 & 101.84 & 4.8 & \\ \hline 13 & 38.69 & 9.4 & \\ \hline 14 & 98.78 & 5.5 & \\ \hline 15 & 91.36 & 4.6 & \\ \hline 16 & 92.86 & 5.4 & \\ \hline 7 & 114.74 & 4.4 & \\ \hline 18 & 81.98 & 6.5 & \\ \hline 19 & 70.13 & 7.3 & \\ \hline 20 & 100 & 4.1 & \\ \hline 21 & 90.41 & 5.6 & \\ \hline 22 & 70.8 & 7.3 & \\ \hline 23 & 93.19 & 5.6 & \\ \hline 24 & 54.78 & 9.3 & \\ \hline 25 & 104.67 & 4.8 & \\ \hline 26 & 117.53 & 3.9 & \\ \hline 27 & 70.08 & 7.5 & \\ \hline 28 & 111.61 & 3.6 & \\ \hline 29 & 70 & 7.1 & \\ \hline 30 & 92.07 & 5.4 & \\ \hline 31 & 123.58 & 3.5 & \\ \hline 32 & 90.29 & 5.2 & \\ \hline 33 & 67.17 & 8.1 & \\ \hline 34 & 98.14 & 5.1 & \\ \hline 35 & 85.17 & 6.2 & \\ \hline 36 & 86.36 & 5.4 & \\ \hline 37 & 105.74 & 4.4 & \\ \hline 38 & 83.77 & 6.5 & \\ \hline 39 & 104.39 & 4.4 & \\ \hline 0 & 104.98 & 4.3 & \\ \hline 11 & 70.72 & 7 & \\ \hline 42 & 96.85 & 5.3 & \\ \hline 3 & 76.49 & 5.8 & \\ \hline 44 & 100.38 & 4.7 & \\ \hline 5 & 119.76 & 2.8 & \\ \hline 6 & 88.22 & 5.1 & \\ \hline 7 & 34.22 & 10.9 & \\ \hline 8 & 85.41 & 5.6 & \\ \hline 49 & & \end{tabular} A. There is a significant negative linear relationship between the two variables. A. There is a significant negative linear relationship between the two variables. B. There is not enough evidence to indicate that there is a significant linear relationship between the two variables. C. There is a significant positive linear relationship between the two variables. B. T_Fert_2004 is the better predictor since the slope of the regression line was significantly different than zero and it has a higher r-square. x A. TExpen_2003 is the better predictor since the slope of the regression line was significantly different than zero and it has a higher R2. B. T_Fert_2004 is the better predictor since the slope of the regression line was significantly different than zero and it has a higher R2. C. Neither of them is good predictors since the slopes of the regression lines were not significantly different than zero. Assume that both of the lineor regressions were valid for predictions (the slope of the regression line was significantly different than zero) for the following questions: 12. (2 marks) Can you use the regression equation to predict Infant Mortality rate (per 1000 live births) in 2004 (IM_2004) for a country that had a total expenditure (T_Expen_2003) of 20\%? A. Yes, I can do that. The answer would be -68.29 A. Yes, I can do that. The answer would be -68.29 . B. No, I can't do that since the (T_Expen_2003) of 20% is outside the range of the observations for TExpen_2003. C. No, I can't do that, this is a silly question. 13. (2 marks) Can you use the regression equation to predict the TExpen_2003 for a country which had an infant mortality rate (IM_2004) of 5 ? A. Yes, I can do that. The answer would be 13.36 . B. No, I can't do that since the TExpen_2003 is the predictor, not outcome variable. 1. ( 3 marks) The validity of the statistical inference depends on how well the data approximates the assumptions of the linear model (choose the figure that goes with each statement): - To check the linear relationship between Total Expenditure on Health as \% of GDP 2003 (TExpen_2003) and Infant Mortality rate (per 1000 live births) in 2004 (IM_2004), we use - To check the normality of residuals, we use 2. (1 mark) If there were outliers in the data, what would be the best action to obtain a linear regression equation to model the relationship between the variables? B. Exclude the outliers (i.e. both for predictor and/or outcome) and re-fit the regression model. ownload the file for and use Excel to carry out suitable statistical analysis to answer the above research question. 3. (1 mark) The slope of the least squares regression line is? 4. (2 marks) The absolute value of the test statistic for testing the slope of the regression line is: X (3dp) with degrees of freedom equal to: (integer). 5. (1 mark) The p-value for testing the slope of the regression line is less than 0.05 . births) in 2004 (IM_2004)?". A. There is a significant negative linear relationship between the two variables There is a significant negative linear relationship between the two variables. There is not enough evidence to indicate that there is a significant linear relationship between the two variables. There is a significant positive linear relationship between the two variables. 7. (1 mark) The correlation between the two variables (TExpen_2003 and IM_2004) is: (3dp - remember to include a negative sign if appropriate) Research Question: "Is there a linear relationship between Total Expenditure on Health as \% of GDP 2003 (TExpen_2003) and Infant Mortality rate (per 1000 live births) in 2004 (IM_2004)?" To help you check the relevant assumptions of the simple linear regression, four graphs are provided below: \begin{tabular}{|r|r|r|r|} \hline & \multicolumn{1}{|c|}{A} & \multicolumn{1}{|c|}{B} & C \\ \hline 1 & IM_2004 & TErpen_2003 \\ \hline 2 & 122.35 & 2.4 & \\ \hline 3 & 108.78 & 2.7 & \\ \hline 4 & 106.69 & 4.4 & \\ \hline 5 & 98.79 & 4.8 & \\ \hline 6 & 110.52 & 3.7 & \\ \hline 7 & 98.66 & 6 & \\ \hline 8 & 108.18 & 4 & \\ \hline 9 & 107.37 & 4 & \\ \hline 10 & 53.87 & 7.9 & \\ \hline 1 & 98.18 & 5.9 & \\ \hline 12 & 101.84 & 4.8 & \\ \hline 13 & 38.69 & 9.4 & \\ \hline 14 & 98.78 & 5.5 & \\ \hline 15 & 91.36 & 4.6 & \\ \hline 16 & 92.86 & 5.4 & \\ \hline 7 & 114.74 & 4.4 & \\ \hline 18 & 81.98 & 6.5 & \\ \hline 19 & 70.13 & 7.3 & \\ \hline 20 & 100 & 4.1 & \\ \hline 21 & 90.41 & 5.6 & \\ \hline 22 & 70.8 & 7.3 & \\ \hline 23 & 93.19 & 5.6 & \\ \hline 24 & 54.78 & 9.3 & \\ \hline 25 & 104.67 & 4.8 & \\ \hline 26 & 117.53 & 3.9 & \\ \hline 27 & 70.08 & 7.5 & \\ \hline 28 & 111.61 & 3.6 & \\ \hline 29 & 70 & 7.1 & \\ \hline 30 & 92.07 & 5.4 & \\ \hline 31 & 123.58 & 3.5 & \\ \hline 32 & 90.29 & 5.2 & \\ \hline 33 & 67.17 & 8.1 & \\ \hline 34 & 98.14 & 5.1 & \\ \hline 35 & 85.17 & 6.2 & \\ \hline 36 & 86.36 & 5.4 & \\ \hline 37 & 105.74 & 4.4 & \\ \hline 38 & 83.77 & 6.5 & \\ \hline 39 & 104.39 & 4.4 & \\ \hline 0 & 104.98 & 4.3 & \\ \hline 11 & 70.72 & 7 & \\ \hline 42 & 96.85 & 5.3 & \\ \hline 3 & 76.49 & 5.8 & \\ \hline 44 & 100.38 & 4.7 & \\ \hline 5 & 119.76 & 2.8 & \\ \hline 6 & 88.22 & 5.1 & \\ \hline 7 & 34.22 & 10.9 & \\ \hline 8 & 85.41 & 5.6 & \\ \hline 49 & & \end{tabular} A. There is a significant negative linear relationship between the two variables. A. There is a significant negative linear relationship between the two variables. B. There is not enough evidence to indicate that there is a significant linear relationship between the two variables. C. There is a significant positive linear relationship between the two variables. B. T_Fert_2004 is the better predictor since the slope of the regression line was significantly different than zero and it has a higher r-square. x A. TExpen_2003 is the better predictor since the slope of the regression line was significantly different than zero and it has a higher R2. B. T_Fert_2004 is the better predictor since the slope of the regression line was significantly different than zero and it has a higher R2. C. Neither of them is good predictors since the slopes of the regression lines were not significantly different than zero. Assume that both of the lineor regressions were valid for predictions (the slope of the regression line was significantly different than zero) for the following questions: 12. (2 marks) Can you use the regression equation to predict Infant Mortality rate (per 1000 live births) in 2004 (IM_2004) for a country that had a total expenditure (T_Expen_2003) of 20\%? A. Yes, I can do that. The answer would be -68.29 A. Yes, I can do that. The answer would be -68.29 . B. No, I can't do that since the (T_Expen_2003) of 20% is outside the range of the observations for TExpen_2003. C. No, I can't do that, this is a silly question. 13. (2 marks) Can you use the regression equation to predict the TExpen_2003 for a country which had an infant mortality rate (IM_2004) of 5 ? A. Yes, I can do that. The answer would be 13.36 . B. No, I can't do that since the TExpen_2003 is the predictor, not outcome variable. 1. ( 3 marks) The validity of the statistical inference depends on how well the data approximates the assumptions of the linear model (choose the figure that goes with each statement): - To check the linear relationship between Total Expenditure on Health as \% of GDP 2003 (TExpen_2003) and Infant Mortality rate (per 1000 live births) in 2004 (IM_2004), we use - To check the normality of residuals, we use 2. (1 mark) If there were outliers in the data, what would be the best action to obtain a linear regression equation to model the relationship between the variables? B. Exclude the outliers (i.e. both for predictor and/or outcome) and re-fit the regression model. ownload the file for and use Excel to carry out suitable statistical analysis to answer the above research question. 3. (1 mark) The slope of the least squares regression line is? 4. (2 marks) The absolute value of the test statistic for testing the slope of the regression line is: X (3dp) with degrees of freedom equal to: (integer). 5. (1 mark) The p-value for testing the slope of the regression line is less than 0.05 . births) in 2004 (IM_2004)?". A. There is a significant negative linear relationship between the two variables There is a significant negative linear relationship between the two variables. There is not enough evidence to indicate that there is a significant linear relationship between the two variables. There is a significant positive linear relationship between the two variables. 7. (1 mark) The correlation between the two variables (TExpen_2003 and IM_2004) is: (3dp - remember to include a negative sign if appropriate)