The purpose of this assignment is to perform association rules analysis to determine whether or not the information generated can be used to address a specific business problem.For this assignment, you will use the "Bakery" data set from the Topic Materials. Most data categories are self-explanatory. Clarifying notes are as follows.ReceiptID: Unique receipt ID (note that all items with the same receipt ID were purchased together)Item: Name of the food item that was purchasedA bakery is interested in increasing sales by optimally displaying and arranging its various baked goods and drinks. The bakery would like to know which items are typically purchased together so it can place the correlated items closer together in its display cases. The bakery has randomly selected 1,000 receipts and compiled a list of items that were purchased together for each receipt.As an analyst, you have been asked to examine the data, summarize your findings, and recommend how the bakery can use these findings to strategically arrange its bakery products to increase sales. Use associative rules analysis to explore the various dependencies among purchased items. Use the Apriori method to do the analysis with a minimum antecedent support of 4%, minimum confidence of 70%, and maximum number of antecedents of 5. Analyze and interpret several of the resulting rules. Present your finding in the form of a 250-word executive summary that includes relevant data, charts, and tables, and addresses the following.Question 1: How would you explain the concepts of support, confidence, and lift to an executive who is unfamiliar with association rules analysis? Include the Apriori method output chart when submitting the answer.Question 2: What would you say about the support of the rules derived from the Apriori analysis?Question 3: If both an apple tart and an apple Danish were purchased, how likely is it that an apple croissant is also purchased? Explain your answer.Question 4: Does it make sense for the bakery to place all of its apple-flavored items together in the display or is it better to spread them out in the display? Explain why.Question 5: If the bakery wanted to increase the sales of cherry soda, what cross-promotional offers could it employ? Explain why.General Requirements:Submit the executive summary as Word document.APA format is not required, but solid academic writing is expected.

All are attached below, max word count is 9 pagesProblem Description:The role of temperature in economic development has been an important question. It is often documented that hot countries (or countries closer to equator) tend to be poor. In recent decades, with the warming at a global scale, scientists and policy makers are increasingly concerned about the economic consequence of warmer and drier weather. In this assignment, you are given historical data on temperature, precipitation, and economic outcomes for a sample of 106 countries.You are required to analyse the impact of temperature, precipitation on income levels (measured by per capita GDP in natural logarithm), and more important, on income growth (measured by annual growth of per capita GDP). Each question is described below (2 + 3 + 4 + 6 + 8 + 4 + 1+ 4 = 32 marks; professional report = 8 marks):Locate the data file (IndividualBusStats.xls) on CANVAS.Has the global been becoming warmer and drier over 1950-2000? Draw evidence on the descriptive statistics of relevant variables in the dataset. Calculate the sample covariance and correlation for the two relationships in question 2 above using Data Analysis Tool Pack or Excel statistical functions. In addition, you are required to calculate the sample covariance and correlation using a second method (using basic Excel formulae without Data Analysis Tool Pack). The calculations by the second method should be carefully laid out in Excel and should NOT use any hard-wired Excel statistical functions e.g. COVARIANCE.S, CORREL, et al. You can use the Excel sort command, the sum command, and any other non-statistical excel commands. Carefully interpret your results. Use simple regression to explore the relationship between (i) annual growth of per capita GDP over 1990-2000 (Y) and mean temperature over 1990-2000 (X); (ii) annual growth of per capita GDP over 1990-2000 (Y) and mean precipitation over 1990-2000 (X), respectively. You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equations, then carry out any relevant two-tailed hypothesis tests using the critical value approach at the 5% significance level. Carefully interpret your hypothesis test results. Now use multiple regression to explore the relationship of annual growth of per capita GDP over 1990-2000 (Y) with, mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). You may use Data Analysis Tool Pack for this. Based on the excel regression output, first write down the estimated regression equation, then interpret the estimated coefficients on the mean temperature over 1990-2000 (X1), and mean precipitation over 1990-2000 (X2). Carry out any relevant two-tailed hypothesis tests using the p-value approach at the 5% significance level, and an overall significance test using the p-value approach. Carefully interpret your hypothesis test results. Write down a paragraph or two to illustrate: a) why the hypothesis test is required in the above regression analysis (either simple or multiple); b) the underlying mechanism/intuition of a two-tailed hypothesis test in the context of regression analysis. Aid your illustration with diagrams that you think appropriate. Carefully interpret the adjusted R-squared in the multiple regression analysis. If you could request additional data to study the factors that influence the annual growth of per capita GDP, what extra variables would you request? Illustrate two such variables. Carefully explain why you choose these two variables (by drawing evidence from the literature such as journal articles, newspapers, et al), types of your proposed variables (e.g. numerical or categorical), and how each of your proposed variables will be measured in the regression model. 2.Do hot and dry countries tend to be poor (with lower per capita GDP)? Use appropriate graphs to interpret the relationship between relevant variables. You only need to present two relationships. Carefully interpret and explain.