# Price Predictions Project

Price Predictions Project

Overview and Rationale
This assignment is designed to give you hands-on experience in performing both
regression and time series forecasting. You will be given a particular real-life time series,
and are asked to perform regression for predictions and to perform a time series
forecasting. In addition, you are asked to perform a sensitivity analysis by using different
parameter values and calculating measures of error for each of those values.
Course Outcomes
This assignment is directly linked to the following key learning outcomes from the course
syllabus:
CO1: Use descriptive, Heuristic and prescriptive analysis to drive business strategies
and actions
CO3: Analyze the role of analytics in supporting decision making for various other
stakeholder groups within and outside of your organization
CO5: Utilize applied analytics and definitions of measures of success to provide a
strategic analytic roadmap for an organization
Assignment Summary
The Excel workbook Honeywell2020.xlsx contains the historical stock prices of the
Honeywell International Incorporated, an American multinational company that produces
a variety of commercial and consumer products, engineering services and aerospace
systems for a wide variety of customers, from private consumers to major corporations and
governments from 1/22/2019 to 1/17/2020 (courtesy of Yahoo Finance). This project
consists of three parts. Each part should be completed in a separate worksheet as
designated in the workbook.
Project Instructions:
1. Perform exponential smoothing forecasts on the Honeywell stock prices to forecast
the price for 1/21/2020. Use successive values of 0.15, 0.35, 0.55, and 0.75 for the
smoothing parameter α. Calculate the MSE of each forecast, Use the MSEs of your
forecasts to determine the value of α that has provided the most accurate forecast.
Describe qualitatively as to why such a value of α has yielded the most accurate
forecast.
exponential smoothing forecasts on the Honeywell stock prices to forecast the price
for 1/21/2020. Use successive values of 0.15, 0.25, 0.45, and 0.85 for the trend
parameter β. Use the MSEs of your forecasts to determine the value of β that has
provided the most accurate forecast. Describe qualitatively as to why such a value of
β has yielded the most accurate forecast.
3. Perform a simple regression analysis of Honeywell stock prices versus periods (i.e.,
1, 2, 3,…) to forecast the Honeywell stock value for 1/21/2020. Calculate the MSE of
this forecast and compare its value with those obtained from parts (1) and (2)
above. The regression analysis should consist of the following additional details:
a. Coefficients of correlation and determination, and the interpretations of their
values
b. A histogram of the regression residuals, and the interpretation of its shape
c. A Chi-squared normality test of the residuals, and the interpretation of its
outputs
d. A Normal probability plot of the residuals
e. A scatter plot of residuals versus time to study their independency, and the
interpretation of the shape of the scatter plot
f. A scatter plot of residuals versus the predicted stock values to study their
homoscedasticity, and the interpretation of the shape of the scatter plot
4. Perform a research to find out the actual Honeywell stock value on 1/21/2020, and
compare this true value with your forecasts in this project. Among the forecasting
methods that you have used in this project, what method has shown to be actually
the most accurate method in predicting the Honeywell stock price for 1/21/2020?
Format & Guidelines
The report should follow the following format:
(i) Introduction
(ii) Analysis
(iii) Conclusion
And be 1000 - 1200 words in length, not including the title page, and presented in the APA
format.
Rubric
Category Above Standard Meets Standards Approaching
Standards Below Standards

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