DDBA 8307B: Quantitative Business Data Analysis Using SPSSAssignment: SPSS – Descriptive Statistics
For this Assignment, you will use SPSS (PASW) software and learn to properly manipulate data according to APA requirements. This is an important skill and will be a major factor in future Assignments in this course, as well as in your Doctoral Study.
To prepare for this Assignment, review the Week 2 Assignment Exemplar document provided in the week’s Resources, and download the Week 2 Assignment Template. Be sure to review the footnotes, as they provide helpful information. Also, review Lessons 20 and 21 in the Green and Salkind (2017) text, as well as the Copying and Pasting SPSS Output into Word document, located in this week’s Resources. Download and use the Week 2 Assignment Dataset file for this Assignment. Keep in mind that you cannot draw conclusions using descriptive statistics without further testing (inferential statistics). Inferential statistics (e.g., t-tests, ANOVA, correlation, regression, etc.) will be covered in later weeks.
By Day 7
Submit an application of descriptive statistics within a quantitative business research context that follows the Week 2 Assignment Template. Your application must include the following:
- An explanation of the implications of “Scales of Measurement” in quantitative research
- A properly stated research question
- A “Presentation of Findings” section, to include appropriate descriptive statistics for nominal (categorical/qualitative) and scale (ordinal, interval, and ratio) data using appropriately formatted APA table(s)
- One appropriate graph for a nominal variable (e.g., pie chart) and one appropriate graph for scale (quantitative) variable (e.g., histogram)
- An Appendix containing the SPSS output (see the Week 2 Assignment Exemplar)
- Correct APA formatting, including in-text citations and a separate References page where appropriate
Please Note: You will cut and paste the appropriate SPSS output into the Appendix. The SPSS output is not in APA format, so you will need to type the information from the SPSS output to the appropriate sections of the APA table. You must use the Week 2 Assignment Template to complete this Assignment. Also, refer to the Week 2 Assignment Rubric for specific grading elements and criteria. Your Instructor will use this rubric to assess your work.
Time Series and Forecasting
Final Project
For this project, you are to build a data set of at least two, but no more than four, variables that you believe might be related over time. It’s probably best to use macroeconomic data (national or state level). Good sources of US macro data include the FRED database at the St. Louis Federal Reserve Bank, and the data at the Bureau of Labor Statistics (BLS.gov).
Your data should be monthly, with a minimum of 15 years (180 observations). I would prefer at least 20 years, but I know of some data that only go back to 2000, so I’ll accept 15. If your data are released daily or weekly, you’ll have to use XL to get the daily (or weekly) averages for the month.
You are to provide two forecasts of the final year of your data: a VAR of all of your variables together, and ARIMA (p, d, q) forecasts of each variable separately. You may use either the entire data set to choose your models, or you may truncate it not to include the final year. Once you’ve decided on a model, run the model with the truncated data to generate your forecasts. In the VAR case, you’ll need to explain why you chose the number of lags you used, and if there was predictive causality between your variables (and in what direction: e.g., X had some for Y, but Y didn’t for X). In the ARIMA models, you’ll need to document why you chose the final structures, and show any unit root tests you might have run.
Once you have your forecasts, compare them to the actual. Which method resulted in forecasts with the smallest MSE? Briefly explain why you believe that method worked better.
When it is time to turn your project in, please submit the RATS program as well as your write up. It’s best to print any graphs generated by RATS as you go, for once you close the program, the graphs disappear.
Links to the data to be used:
As you can see the answers are there and they are correct, I need an explanation:
1) provide the process step by step for the graph on excel
2) do the calculation step by step how you get the answers on excel, please do not skip
3) highlight the answers on excel
4) provide the formula on excel
each number on the excel match with each question on word doc. please return it and label the solution with the numbers thank you
Problem 1. When John started his freshman year at GWU, his parents decided to create a basket of items that included his spending on major items so they can track his spending and compute inflation rates for the basket. John is now a junior (finishing up his 6th semester at GWU). His parents have the complete data until the end of 5th semester. To save you computation time, I included only three items from the basket: Item 1: Textbook Item 2: Eating at High End Restaurants Item 3: Electronic items/Cell phones/laptop/tablet
Use the information provided above, answer (compute) all the questions (CPIs) below: a) Laspeyres price index for Semester 4 (S4) – 1 point b) Paasche Price Index for Semester 4 (S4) – 1 point c) Fisher for Price Index for Semester 4 (S4) – 0.5 point d) Lowe’s Price Index for Semester 4 (S4) – 1 point — For this, use S3 basket as the base e) Establish a Run chart (Chapter 13) for textbook price only (identify the center line and upper/control limits using the data provided) — here, your answer can be based on the total spending on the textbook per semester or average spending on the textbook – you decide on the chart you prefer — All I want for you is to establish a norm (center line and upper/lower control limits based on 6-sigma rule) – 1 point Problem 2. Using the average price spent on Electronic items/Cell phones/laptop/tablet, provided above, as our time series data, a) Using the moving average of two period forecast the spending on this item for Semesters 6 and 8 (0.5 pt) b) Using simple exponential smoothing method and w=0.60, forecast the spending for this item in Semesters 6 and 8 (1 pt) c) Using double exponential smoothing method and w=0.60, υ=0.2, forecast the spending for this item in Semesters 6 and 8 (1.5 pts)
Problem 3. Using the average price spent on Electronic items/Cell phones/laptop/tablet, provided above, compute Autocorrelation of lag 1 and lag 2. (0.5 for each) Problem 4. Two forecasting methods have produced the following forecast for the past five months. Actual Forecast 1 Forecast 2. 10 11 9 8 10 7 10 8 11 6 6 7 9 8 10. a) Compute all three popular measures of forecast accuracy for the Forecast 1. (0.5 pt.) b) Compute all three popular measures of forecast accuracy for the Forecast 2. (0.5 pt.) c) Based on the results for MAD (mean absolute deviation) statistics which forecast method do you recommend? (.5 pt)
1.5For the two series, xt, inProblem 1.2(a) and (b):
(a) Compute and plot the mean functionsµx(t), fort=1, . . . ,200.
(b) Calculate the autocovariance functions, x (s, t), fors, t=1, . . . ,200.
in the attached file 1.2 problem
I need it with R program
What characteristics of data would lead you to select one type of statistical analysis over another? Be specific, give examples, and support your answer
Evidence supports that effective use of dietary principles (adequacy, balance, kcalorie control, nutrient density, moderation, and variety) will lead to healthier food choices, and yet people still make poor food choices. Is there a difference in responsibility between individuals and families regarding whether they follow recommended diet-planning principles? If so, what are these differences?
THIS IS A DISCUSSION. MUST REPLY TO 3 PEOPLE
What is ‘overfitting’ and why is it a problem in generating forecasting models?