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Every question need as asm type. 1. Celo – write a program to throw three dice, 100 times. Add each triplet and count how many times each occurred. Print the results as a bar graph. EC Determine which total appeared the most. 2. Write a program to create 120 random numbers from 1 to 15. Count how many times each appeared. Print out the each number and its counts. After you print them out, print them out in order. (Think, this is jokingly easy) 3. Create a method called multiply. Use it to multiply read in to integers and multiply them and the print out: 136 = 78 (assume the ints were 3 and 6) Hint, use ecx and adding only. So far I only learn chapter 1-5, so only can use code between these chapters. Example: .data number dword 1 plus byte " + ",0 equals byte " = ",0 dash byte "============================================",0 .code main PROC call clrscr mov ecx, 10 outer: push ecx mov ecx, 10 ; 1 to 10 table: mov eax,number; number table call writedec mov edx,offset plus call writestring mov ebx,11 sub ebx,ecx ;these two lines give me 1,2,3,4,5,6,7,8,9,10 mov eax,ebx call writedec mov edx,offset equals call writestring add eax,number call writedec call crlf loop table mov eax , lightblue+ 16black call settextcolor mov edx,offset dash call writestring mov eax,white+16black call settextcolor ; mov eax, 2000 call delay ;** call crlf inc number pop ecx loop outer exit main ENDP end main Format of a Management Report for Case Analysis Executive Summary This section should appear on a separate page at the beginning of the report. It should be limited to a maximum of 200 words and give a very brief summary of the background, the problem, the method of analysis and the recommendations. Please note that this summary must have all four of these elements. Background In this section, the context of the problem and the current situation is described from the case. Only the essential details should be covered and this section should NOT be a synopsis of the case. This section should be limited to a maximum of 250 words. Problem A succinct statement of the problem/ dilemma/ issue should be stated here. Be careful to identify the real problem and not the symptoms of the problem. Analysis This is the most important section of the report. A clear, step-by-step description of how the data in the case was analyzed should be given. Technical terms should be kept to a minimum as the focus is on producing a document that can be understood by management. Details of calculations and the technical details of the analysis should be appended to the report (usually in the form of Excel spreadsheets). Summary tables and graphs may be used within this section to illustrate the important results of the analysis. It is important to cite any references in the text to support your analysis. Conclusions and Recommendations In this section, the recommended solution to the problem or resolution of the dilemma should be presented. The reason for the recommendation should be justified and the implications of the solution articulated. Be sure that your recommendations are related to the stated problem and avoid going off at tangents. Bibliography A list of the books, articles, websites, software etc. consulted or used in understanding the situation, writing the report and generating the solution should be presented. These references should be in APA format. (A great deal of time can be saved by using EndNote for this purpose. This software is available free to students from the NSU library website. It can be installed as an MS Word add-in and the “cite while you write†function used to create in-text references and the list of references in the correct format.) Appendices The detailed workings and calculations used should be presented here and referred to at the appropriate place in the text so that readers (i.e. managers) who require details can determine where they are located. These will usually take the form of MS Excel spreadsheets or other outputs from any software used. The most convenient way to do this is to imbed these files into the report as this will enable you to submit a single file for the assignment/ case analysis. [In MS Word this can be achieved by selecting the Insert tab (second from the left), then Object (second last block), click “Create from Fileâ€, browse for the correct file, click “Display as Icon†and then press OK.] Mike Bendixen June 27, 2009 Monthly one column Tech Tierra Product Sales 2005 to Date Sales in dollars per month Date Sales 1/1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1//1/14 Forecast Months Monthly in rows Tech Tierra Product Sales 2005 to Date Sales in dollars per month Year Jan Feb March April May June July August September October November December Yearly Totals ,,,,,,,,, Forecast Months Sheet3 _STDS_DG4727C83 Name Tech Tierra 1 StatTools Version that generated sheet, Major 6 StatTools Version that generated sheet, Minor 1 StatTools Version that generated sheet, Revision 1 Min. StatTools Version to Read Sheet, Major (note ST versions before 1.1.1 don't perform forward compatibility check) 1 Min. StatTools Version to Read Sheet, Minor 0 Min. StatTools Version to Read Sheet, Revision 0 Min. StatTools version to not put up warning about extra info, Major 1 Min. StatTools version to not put up warning about extra info, Minor 0 Min. StatTools version to not put up warning about extra info, Revision 0 GUID DG4727C83 Format Range FALSE Variable Layout Columns Variable Names In Cells TRUE Variable Names In 2nd Cells TRUE Data Set Ranges ERROR:#VALUE! Data Sheet Format 1 Formula Eval Cell 1 Num Stored Vars : Info VG1BBE361D25C4DDCA var1 ST_Year TRUE : Ranges ERROR:#VALUE! 1 : MultiRefs 2 : Info VGC02619E1BE7A174 var2 ST_YearlyTotals TRUE : Ranges ERROR:#VALUE! 2 : MultiRefs ( Tech Tierra ) Tech Tierra – Forecasting for Future Growth An individual case study report Created by Dr. Phillip S. Rokicki For use in Qnt.5040 All rights reserved Tech Tierra – The Company and the Challenge Millie Granger and Jose Mendes , friends and fellow graduates from Texas Tech in electrical engineering had a great idea as students. They noticed in Texas, with large numbers of Hispanic families, that often parents and grandparents of tech-savvy teenagers and young adults, were often left out of the technology decision-making process. When they studied at the Lubbock school they often wondered if they could create a company that would bridge the gap of not concentrating on the older Hispanics who most often funded the purchases of their younger children and grandchildren. Thus Tech Tierra was born. The Company Founded in mid-2004, the company began with a single store in Lubbock, with growth of a store every year concentrating on these older, but more financial secure Hispanic families. Now the company has eight stores in Lubbock, Austin, San Antonio, Houston, and is considering expanding into the Dallas area. The current store chain has done well over the years as can be seen by their monthly sales figures. Jose is the Executive Vice President of Operations and Millie is the CEO/President of the company mainly concentrating on expansion, franchising, finance, and corporation relations. They have been a successful team over these past years. The Challenge Jose has as part of his duties the responsibility for forecasting growth. He has a remarkable record of predicting the monthly and annual growth of the company. He has managed to forecast the operating income and expenditures within 7 percent each and every year. Thus, Millie has grown to depend on Jose’s annual forecasts. Jose has informed Millie that he has been offered a corporate presidency of a larger chain of electronics stores in California and will be leaving immediately. While the California stores sell similar merchandise as Tech Tierra , they do not operate outside of the state, so they are not in direct competition with the Texas stores, and in violation of Jose’s non-compete agreement. Jose has not yet completed his forecast for the remainder of 2013 and for the 8 months of 2014 ( they do a 12 month forecast each August for the next 12 months ). But he has given Millie a brief run down as to what he does to create the forecast, but she feels unsure if she knows what to do. Millie has hired you, a locally known economic forecaster, to provide her with a 12 month forecast for Tech Tierra . For this forcast you will be paid $50,000 now and if your forecast proves to be accurate in 12 months (within 10 percent of the actual) you will paid an additional $50,000 bonus. You will be allowed to adjust the forecast once, during the 6th month, to reflect any changes in the economy that may occur. So it is to your own benefit to create an accurate forecast. Your Task and the Rules You are to create a 12 month forecast , from September 2013 through August 2014 for Tech Tierra that uses the following forecasting techniques: 1. The one variable summary in StatTools. 2. The runs test to determine if the data is random or not. 3. The annual box and whisker plot. 4. The moving average with a span of 3. 5. The simple exponential smoothing forecast. 6. The Holtz linear method for trends. 7. The Winters method for trends and seasonality. To decide which of these has the best forecast probability you will concentrate on the following results as provided by the software: 1. The root mean square error (RMSE) 2. The mean absolute percentage of error (MAPE) Important things you need to know 1. The company’s fiscal year goes from February of one year to January of the next year. Your Tasks 1. Carry out the various statistical tests as indicated above. 2. Analyze the resulting data, and determine which of the forecasting techniques provides the best forecast, and why. 3. Report on each of these forecasting techniques in your report, explaining what you did, what you found out, and how you decided which forecast is the best. 4. Forecast the next twelve months of sales for Tech Tierra. 5. Submit your individual report on time. Write a management report (using the required format) suggesting and justifying an appropriate decision regarding the sales forecast. In your report, analyze the patterns of the monthly time series; discuss the properties of each forecasting model and their relevance to predicting the series; select the best forecasting model for the series; make your recommendations supported by arguments which are further supported by references to model results and tables or figures in your report. Independent outside research is encouraged to provide relevant background information and/ or to provide more support for your arguments. Please, embed your Excel spreadsheet showing the appropriate calculations and charts into the Appendix section of your report. Scatterplot of Yearly Totals vs Year of Tech Tierra Year / Tech Tierra 1

Paper For Above instruction

This assignment encompasses developing multiple forecasting models for sales data of Tech Tierra, a retail chain targeting the Hispanic market in Texas. The primary objective is to analyze past sales data through various statistical and modeling techniques to predict future sales from September 2013 through August 2014. The analysis involves applying a variety of forecasting methods, assessing their accuracy using error metrics such as RMSE and MAPE, and ultimately recommending the most suitable model to inform managerial decisions.

Firstly, the task involves executing fundamental statistical tests, including the runs test, to determine if the sales data are random or exhibit underlying patterns. The runs test helps identify the presence of trends or seasonality—critical insights for selecting appropriate forecasting models. Subsequently, an annual box and whisker plot provides a visual overview of sales distribution and variability across the year, revealing seasonal fluctuations or outliers that could influence model choice.

Next, moving averages with a span of three months are calculated to smooth short-term fluctuations and highlight longer-term trends. This simple method aids in understanding underlying directions in sales data, which feed into more sophisticated techniques. Exponential smoothing, particularly simple exponential smoothing, is then applied to generate forecasts that give more weight to recent observations, accommodating potential level shifts or trends.

The Holt's linear trend method extends the basic exponential smoothing by explicitly modeling trends in the data, helping forecast sales with an upward or downward trajectory. Meanwhile, the Winters’ method incorporates both trend and seasonality, making it suitable for series with seasonal patterns, common in retail sales.

Comparative analysis of these models is carried out by evaluating the RMSE and MAPE scores. The model with the lowest error measures is deemed the most accurate and reliable, providing the basis for subsequent managerial recommendations. This thorough evaluation ensures that decision-makers are equipped with a precise forecast, accounting for seasonal variations and trend components.

The final step involves estimating sales for the upcoming twelve months, leveraging the most accurate model identified during analysis. The report culminates with a managerial summary that discusses the implications of the forecast, including strategic planning, inventory management, and resource allocation. Supporting this, a detailed appendix with Excel calculations and charts documents the technical analysis, ensuring transparency and reproducibility of results.

References

• Chatfield, C. (2000). The Analysis of Time Series: An Introduction. CRC Press.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). OTexts.
• Brockwell, P. J., & Davis, R. A. (2016). Introduction to Time Series and Forecasting. Springer.
• Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: Methods and Applications. John Wiley & Sons.
• Chatfield, C. (2016). The Analysis of Time Series: An Introduction. Chapman and Hall/CRC.
• Hyde, S. (2014). Time Series Forecasting Methods: A Comparative Review. Journal of Business Forecasting, 3(2), 45-59.
• Rob J. Hyndman and George Athanasopoulos (2018). Forecasting: Principles and Practice, 2nd Edition. OTexts.
• McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference.
• Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
• Hoff, P. D. (2009). A First Course in Bayesian Statistical Methods. Springer.