Read the information on the research described below and answer the questions that follow. A recent study titled, "The effect of emigration on unemployment rates: the case of EU emigrant countries", kuflic and Vuckovic (2019: 1833) reported the following findings: The obtained results show that emigration increases the unemployment rate in emigrant countries confirming that, besides generally expected positive effects in terms of a fall in unemployment, emigration could also have an adverse effect on emigrant countries' labour markets. Such results point to structural issues in the labour market caused by emigration. In another study titled, "Labour market efficiency and emigration in Slovakia and EU neighbouring countries", which analysed labour market conditions and emigration trends in Slovakia and EU neighbouring countries, Prvara (2020:1865) reported the following: The analysis of emigration trends has shown that among EU neighbouring countries, the Slovak migrant population tends to migrate mostly to the Czech Republic. Our regression analysis has demonstrated that the most significant determinants affecting emigration from Slovakia to the Czech Republic are the low unemployment rates in Czech Republic, the labour market regulations introduced in 2013 and the EU enlargement in 2004. According to the latest World Economic Forum assessment on labour market efficiency, the Czech Republic ranks better than Slovakia on most of the labour efficiency indicators, in particular, on cooperation in labour-employer relations, flexibility of wage determination, hiring and firing practices, effect of taxation on incentives to work, pay and productivity, reliance on professional management, country capacity to retain talent and country capacity to attract talent. Furthermore, when compared to other EU neighbouring countries, the Czech Republic, is more attractive for Slovak citizens due to their quick language adaptability. Following Prvara (2020) and kuflic and Vuckovic (2019), you want to unpack the dynamics of the relationship between South Africa's talent emigration (proxied by the emigration of South African university graduates) and South Africa's unemployment rate. You obtained secondary data on the emigration of South African university graduates and on South Africa's unemployment situation for the 1991 - 2022 period (see Table 4 below). Table 4: Secondary data on South African University Graduates' Emigration Rate and South Africa's unemployment Rate for the 1991 - 2022 period. Year Graduate emigration Rate (%) Unemployment Rate (%) 2022 1,979% 29,81% 2021 2,118% 28,77% 2020 2,258% 24,34% 2019 2,397% 25,54% 2018 2,536% 24,22% 2017 2,719% 23,99% 2016 2,902% 24,02% 2015 3,085% 22,87% 2014 3,268% 22,61% 2013 3,451% 22,04% 2012 3,457% 21,79% 2011 3,463% 21,42% 2010 3,468% 23,18% 2009 3,474% 20,51% 2008 3,480% 19,51% 2007 3,464% 19,54% 2006 3,448% 19,64% 2005 3,431% 19,74% 2004 3,415% 19,87% 2003 3,399% 20,02% 2002 3,312% 20,09% 2001 3,224% 20,22% 2000 3,137% 20,27% 1999 3,049% 20,42% 1998 2,962% 20,57% 1997 3,242% 20,60% 1996 3,521% 20,63% 1995 3,801% 20,75% 1994 4,080% 20,83% 1993 4,360% 20,97% 1992 3,352% 21,16% 1991 2,344% 21,19% IBM SPSS Statistics version 27 was used to analyse the data and the following output in Figure 4.1 to Figure 4.5 was generated: Figure 4.1 Scatter plot Figure 4.2 Descriptive Statistics Graduate emigration Rate (%) Unemployment Rate (%) Mean 3.1749% Mean 21.9103% Standard Error .0957% Standard Error .4463% Median 3.332% Median 20.90% Mode #N/A Mode #N/A Standard Deviation .5413% Standard Deviation 2.5247% Sample Variance .0029% Sample Variance .0637% Kurtosis .3058 Kurtosis 3.0272 Skewness -.4679 Skewness 1.7424 Range 2.381% Range 10.3% Minimum 1.979% Minimum 19.51% Maximum 4.36% Maximum 29.81% Sum 101.596% Sum 701.13% Count 32 Count 32 Figure 4.3 Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate 1 .735a .540 .525 .0174 a. Predictors: (Constant), Graduate_Emigration_Rate b. Dependent Variable: Unemployment_Rate Figure 4.4 ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression .01068 1 .01068 35.2613 .000 b Residual .00908 30 .000303 Total .01976 31 a. Dependent Variable: Unemployment_Rate b. Predictors: (Constant), Graduate_Emigration_Rate Figure 4.5 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients B t Sig. Std. Error Beta 1 (Constant) .3279 .0186 17.645 .000 Graduate_Emigration_Rate -3.4281 .5773 .735 -5.9381 .000 a. Dependent Variable: Unemployment_Rate REQUIRED: 4.1.Specify and describe the implied research design of the study described above. (5 marks) 4.2.Specify the independent and dependent variables underpinning the study and their levels of measurement. (4 marks) 4.3.Formulate the null and alternative hypotheses for the study. (2 marks) 4.4.Elaborate on any FOUR (4) assumptions of simple linear regression analysis. (4 marks) 4.5.Based on the output of the statistical analysis conducted, provide a comprehensive interpretation of the empirical model underpinning the study. Your interpretation should include the standard reporting format. (5 marks) 4.6.Establishing an association between an independent variable and a dependent variable is often a first step in the quest to establish a causal effect. Does your empirical model allow you to attribute causality between 'Graduate Emigration Rate' and 'Unemployment Rate'? Explain. (3 marks) 4.7.Denoting the variables 'Graduate Emigration Rate' by X and 'Unemployment Rate' by Y, state an appropriate linear regression equation to describe your empirical model. (2 marks)