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Hi guys, Just want to elaborate on the last paper of yours as promised. I guess the way to go is to introduce the steps you should take: Your analysis consists of 2 parts and the first part is to introduce the new information into the spreadsheet ( the 4 case scenarios). The second is to run log linear regression analysis. “The log transformation can be used to make highly skewed distributions less skewed. ... The comparison of the means of log-transformed data is actually a comparison of geometric means. This occurs because the anti-log of the arithmetic mean of log-transformed values is the geometric mean. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics”. 1. I suggest you insert a column called Inflation (say 2%). Please, remember, it will be incorporated indirectly through calculating real income (combine increases in real income and inflation assumption). E.g. =c3*(1+0.01). =G3/(1+0.02). =D3*(1+0.075) (extend to 2 decimal places) These represent the below raw: Annual average demand of energy bars per person Average income per person Tariff rate on imports of energy bars Number of stores where energy bars are offered 106 15500 5 15 Now, transform the new data into log linear form: You need to put the formula in for logs in your chosen cell, put = LN(106); =LN(15348); =LN(5.38); and =(LN15). Another way is to put in LN, open the bracket, click on the cell number and close the bracket) The below is what you will get with the case scenario of 1% income increase, 2% inflation increase, 7.5% tariff change, and number of stores 15. Annual average demand of energy bars per person Average income per person Tariff rate on imports of energy bars Number of stores where energy bars are offered Inflation Incrased income Real Income New Tariff No of stores Log Demand Log Real Income Log Tariffs Log Stores 106 15500 5 15 2% 15655 15348 5.38 15 4.6634391 9.638743006 1.681759 2.7080502 This latter step should be extended to other 4 case scenarios Simply copy and extend as it is normally done in excel Now you need to perform the regression analysis: Go to Data; data analysis, regression. Input Y variable (it is your log demand) and extend it to all your data (all demands in the table); Input the X range (all your dependent variables), put in the confidence level 95% and click OK Please, see the short youtubes to help you do it and analyse the results Entering data and performing regression https://www.youtube.com/watch?v=wBocR96UdyY Interpreting: https://www.youtube.com/watch?v=tlbdkgYz7FM https://www.youtube.com/watch?v=wBocR96UdyY https://www.youtube.com/watch?v=tlbdkgYz7FM ECON6000 Final Report Clarifications 1. If the government in Atollia wants reduce consumption of imported products, they may increase tariffs. In this question you are required to test what will happen if, say for example, 5% increase in income is associated with 2% inflation rate, etc. 2. You need to predict the impact of changes in average in income, tariffs, and inflation on the average demand. In this case, you need to run regressions once again. For example, Income 3%, 5% or 7%. If you multiply the income column with these percentages, you will get the values for all three scenarios. Tariffs 7.5%, 10%, 5% or free trade. Same as above Inflation is a Nominal Income (indicated as “average income”) minus inflation. That can also be incorporated. Example: income of 15000 – 3% inflation Conducting a multiple linear regression (in log linear form) which is Log Y = α + β1X1 +β2X2 +β3X3 + β4X4 + ϵt The log is in base e so you need to put in Excel the formula = LN (average demand). In case, you want to know more about how to do it in excel, do go to youtube with these keywords or see the links below. The slope/coefficient/parameter (β1, β2 etc.) should be interpreted as the impact (effect) of these variables (variable 1, variable 2 etc.) on the demand. Say, with + sign, increase; with – sign, decrease (i.e. increase in variable, say, in tariff, which is actually tax, would impact demand as per the sign of the estimate of β). Another hint: According to Laffer Curve, as taxes (tariffs in our case) increase from low levels, tax revenue collected by the government also increases. It also shows that tax rates increasing after a certain point would cause people not to work as hard or not at all, thereby reducing tax revenue and as a result their real income. And: According to Phillips Curve, Decreased unemployment in an economy correlates with higher rates of inflation (up to a certain extent). That does not necessarily translates into greater total real income in the economy but we could assume that more people will be employed affecting sales dependant on the number of stores (this is just an assumption). https://www.youtube.com/watch?v=wBocR96UdyY https://www.youtube.com/watch?v=O7TMCYuDbDc https://www.youtube.com/watch?v=tlbdkgYz7FM&t=23s https://www.youtube.com/watch?v=wBocR96UdyY https://www.youtube.com/watch?v=O7TMCYuDbDc https://www.youtube.com/watch?v=tlbdkgYz7FM&t=23s