Your assignment is to address the questions on pages 216-218. Use whatever software resources make the most sense (Tableau, Excel, R, etc.) given that you are working with more than 500 time series. (Remember that for the regression model in 8(d) you are only to forecast the first 5 series). Your response to the case will be answers to the text questions (upload Excel or Tableau files, along with a Word file or .pdf with responses to the written questions). PAGES 216-218Background: Tourism is one of the most rapidly growing global industries and tourism forecasting is becoming an increasingly important activity in planning and managing the industry. Problem Description: The 2010 tourism forecasting competition had two parts. in part I, competitors were tasked with producing forecasts for the next four years, given 518 series of annual tourism data.Available Data: The data consists of 518 annual series, each related to some (undisclosed) tourism activity. Tourism activities include inbound tourism numbers to one country tourism expenditure, etc. The series differ in lenght, ranging from 7 year series to 43 year series. they also differ in the order of magnitude of values. The data is available at www.kaggle.com/c/tourism/dataAssignment Goals: This case will give you experience with forecasting a large number of series. while the competition goal was a achieve the highest predictive accuracy, the goal of this case is to highlight different aspects of the forecasting process that pertain to forecasting a large number of series. for example easy to use visualization tools have a significant advantage for visualizing a large number of series. note that multiple series in this case are not likely to be forecasted together. however, it is common to forecast the demand for a large number of series such as products in a supermarket chain. Another aspect of this case is the use of different predictive measures, and handling practical issues such as zero counts and summarizing forecast accuracy across series.The assignment provides guidelines for walking you through the forecasting process. remember that the purpose is not winning the (already completed) contest, but rather learning how to approach forecasting of a large number of series. 1. Plot all the series (an advanced data visualization tool is recommended)-what type of components are visible? are the series similar or different? check for problems such as missing values and possible errors.2. Partition the series into training and validation so that the last 4 years are in the validation period for each series. what is the logic of such partitioning? what is the disadvantage?3. generate naive forecasts for all series for the validation period. for each series create forecasts with horizons of 1,2,3, and 4 years ahead.4. which measures are suitable if we plan to combine the results for the 518 series? consider MAE, average error, MAPE, and RMSE.5. for each series, compute MAPE of the naive forecasts once for the training period and once for the validation period.6. the performance measure used in the competition is mean absolute scaled error (MASE). explain the advantage of MASE and compute the training and validation for the naive forecasts. 7. Create a scatter plot of the MAPE pairs with training MAPE on the x-axis and the validation MAPE on the y-axis. Create a similar scatter plot for the MASE pairs. now examine both plots. what do we learn? how does performance differ between the training and validation periods? how does performance range across series?8. The competition winner, Lee Baker, used an ensemble of three methods:naïve forecasts multiplied by a constant trend (global/local trend: "globally tourism has grown " at a rate of 6% annually.")linear regressionexponentially-weighted linear regression(a) write the exact formula used for generating the first method, in the form F t+k =… (k1,2,3,4)(b) what is the rational behind multiplying the naïve forecasts by a constant? (c) what should be the dependent variable and the predictors in a linear regression model for this data? explain(d) fit the linear regression model to the first five series and compute forecast errors for the validation period.(e) before choosing a linear regression, the winner described the following process "I examined fitting a polynomial line to the data and using the line to predict future values. I tried using first through fifth order polynomials to find that the lowest MASE was obtained using a first order polynomial (simple regression line). this best fit line was used to predict future values. I also kept the [R^2] value of the fit for use in blending the results of the predictor."what are two flaws in this approach?(f) if we were to consider exponential smoothing, what particular types of exponential smoothing are reasonable candidates?(g) the winner concludes with possible improvements, one being "an investigation into how to come up with a blending [ensemble] method that doesn't use as much manual tweaking would also be of benefit". can you suggest methods or an approach that would lead to easier automation of the ensemble step? (h) the competition focused on minimizing the average MAPE of the next four values across al 518 series. how does this goal differ from goals encountered in practice when considering tourism demand? which steps in the forecasting process would likely be different in a real-life tourism forecasting scenario?

This is only a discussion question, I do not need anything long and drawn out."Tourism (20 points)" Please respond to the following: Analyze the various means of tourism (rail, automobile, and coach travel; airlines; cruise ships) to determine which sector you anticipate will grow the most in the next 20 years. Explain your rationale.

General InstructionsFor each Assignment:1.Include an APA title block with your name, class title, date, and the assignment number.Include a table of contents and a reference section. Number your pages in the footer along with the date. Include a header starting on page 2 with the Course and assignment number.Write the problem number and the problem title as a level one heading (Example ‐ A.1.1: Chapter 2, Problem 2.1, Check the Completed Questionnaires) and then provide yourresponse.Use level two headings with short titles for multi part questions (Example ‐ A1.1.a, Short Title, A1.1.b, Short Title II, etc.)Use appropriate level headings for key elements of your discussion such as Research Questions, Hypotheses, Descriptive Statistics, Assumptions & Conditions, Interpretation, Results, and others. Your goal is to make your analysis easy to follow and logical.Ensure that all tables and graphs are legible and include a figure number.Carefully review your document prior to submission for formatting, flow, and readability. Keep in mind that running the statistical tests is only the first half of the challenge; you must be able to clearly communicate your findings to the reader!. Cut and paste your outputs directly into your document and refer to them in your interpretation.At least 3 referencesAssignment 8A8.1a: Chapter 11, Problem 11.1, One‐Way (or Single Factor) ANOVA. Write a short narrative of your process and an interpretation of your findings. Cut and paste the One‐Way Descriptives, Test of Homogeneity of Variances, and ANOVA tables directly into your document and refer to them in your interpretation.A8.1b: Chapter 11, Problem 11.2, Post Hoc Multiple Comparison Tests. Write a short narrative of your process and an interpretation of your findings. Cut and paste the ANOVA, Post Hoc Tests, and Homogeneous Subset tables for the Tukey HSD tests and the Games‐Howell tests directly into your document and refer to them in your interpretation.A8.1c: Chapter 11, Problem 11.1 & 11.2 One‐Way ANOVA with Post Hoc Tests. Write the results of Problem 11.1 and 11.2 to include tables.A8.2: Chapter 11, Problem 11.3, Nonparametric Kruskal‐Wallis Test. Write a short narrative of your process, an interpretation of your findings, and write your results. Cut and paste the Ranks and Test Statistics tables directly into your document and refer to them in your interpretation.A8.3: Chapter 11, Problem 11.4, Two‐Way (or Factorial) ANOVA. Write a short narrative of your process, an interpretation of your findings, and write your results to include tables. Cut and paste the Between Subjects Factors, Descriptive Statistics, Tests of Between Subjects Effects, and Profile Plots tables directly into your document and refer to them in your interpretation.A8.4, Application Problem ‐ ANOVA. Using the “college student data.sav” and “hsbdata.sav” files, do the following problems. Write a short narrative of your process, an interpretation of your findings, and write your results. Cut and paste your outputs directly into your document and refer to them in your interpretation.Write a research question and a null hypothesis exploring the potential difference in the students’ scholastic aptitude test (satm) mean scores based upon ethnicity (ethnic). Conduct a One‐Way ANOVA, Tukey HSD Post Hoc test, and the Games‐Howell Post Hoc test to test for the difference in meanscores. Include One‐Way Descriptives, Test of Homogeneity of Variances, ANOVA, Post Hoc Tests, and Homogeneous Subset tables and refer to them in your interpretation. Prior to running the analysis, discuss how the data meets the assumptions and conditions for the tests you are going to conduct. Support your assertion with the appropriate descriptive statistics.Write a research question and a null hypothesis exploring the potential difference in students’ heights based upon gender and marital status. Examine any potential interaction between gender and marital status. Conduct a Two‐Way ANOVA to test for the difference in mean heights. Include Between Subjects Factors, Descriptive Statistics, Tests of Between Subjects Effects, and Profile Plots tables and refer to them in your interpretation. Prior to running the analysis, discuss how the data meets the assumptions and conditions for the tests you are going to conduct. Support your assertion with the appropriate descriptive statistics.BOOKSSPSS GradPack (Standard) 25 6 Months LATESTAuthor:Liberty UniversityISBN-13:ISBN-10:Edition/Copyright:LATESTPublisher:Kivuto Solutions, Inc. (cc)Returns:Non-returnable once openedIBM SPSS for Introductory Statistics 5TH 13 EBBOKISBN-13: 9781848729827Authors: George A MorganNancy L LeechGene W GloecknerKaren C Barrett