KENNETH C HOLMES
APPLIED MANAGERIAL DECISION MAKING
DR. HENRIETTA OKORO
APRIL 29, 2015
PHASE 4: DISCUSSION BOARD
COLD BEVERAGE SALES REGRESSION MODEL
The scenario:
WidgeCorp is considering expanding their snack food empire into the cold beverage
market, and I have been charged with preparing a regression model analysis to forecast the
monthly sales of cold beverages for the next year, and determine if this would be wise and
profitable move for them. This document discloses my findings.
Business forecasting:
I will start with the basics. Business forecasting is statistical analysis that estimates future
demand, and is a necessary process for the success of any business, industry and government.
The purpose of regression analysis is to predict the values, explain the impact, identify the
relationships and measure the strengths and correlations of both independent (explanatory)
variables (the variable used to explain what we are trying to solve) and dependent (response)
variables (the variables we want to explain), to provide insight about the success of the next
cycle, new venture, predict future results, values and performance (Saint-Leger, 2015).
Business forecasting uses several sources for its predictions. Historical performance data
from a firm’s previous performance, financial statements, sales invoices, national economic data,
industry statistics, or any variables relevant to the future success of your business based on
observations and experience. The minimum number of observations is usually 20 to 30. When it
comes to past performance it is not always the best reference, and you should never count on past
performance as a basis for future prediction. For example: a 25 percent increase in WidgeCorp’s
business from the previous year should not be used to forecast another 25 percent increase for the
next year. To establish an appropriate regression analysis for the next year, you must identify the
variables of the present time to determine an accurate prediction for the next year, because times
change, variables change, and the effects of those variable can affect business results differently
each period. The more regression analysis is used, and the longer the forecast time frame, the
better the predictions for future success. Using historical data also has its limitations, significant
correlation of explanatory and response variables do not guarantee cause and effect relationships,
there are times when a series of events was the common link, and you should never count on a
repeat of events (Saint-Leger, 2015).
Regression models:
Both Linear and nonlinear regression models find values for the intercepts x and y, and
the slope that define the line that comes closest to the data as possible, show the relationship
between two variables with a linear equation, graphs a line over a set of data points that most
closely fit the overall shape of the data, and shows the extent to which changes in an explanatory
variable (the x axis) affect changes in a response variable (the y axis). The difference is linear
graphs with a straight line, uses simple algebra, ensures no ambiguity, and the user simply inputs
the data and receives the results, while nonlinear graphs with a curved line, uses intensive
calculus and matrix algebra computations, requires initial estimated values for each variable, and
is slightly more complicated to use. Both linear and nonlinear can fit any model. Examples
include: WidgeCorp’s sales have increased steadily each month for the past year, monthly sales
is the explanatory variable and time is the response variable, resulting in a line showing the
upward trend in sales; WidgeCorp has changed the price of a snack food numerous times, price
is the explanatory variable, and quantity sold is the response variable (Hamel, 2015 and N.A.,
2015).
Simple linear regression has only one response and one explanatory variable to predict
the outcome, and is calculated by the linear equation Y=β0+β1x+ε, where y is the response
variable, β0 is the y intercept slope, β1 is the slope coefficient, x is the explanatory variable, and ε
is the random error term (Okoro, 2015). Multiple-linear regression uses one response and several
explanatory variables to predict the outcome, seeks to find a mathematical correlation, and then
shows the relationship as a straight line that best approximates all the data points. The equation
for multiple-linear regression is y=β0 +β1X1 +β2X2 +…..βkXk + ε, where y is the response
variable, β0 is the y intercept slope, β1 and β2 are partial slope coefficients, X1 and X2 are
explanatory variables, and ε is the random error term (Okoro, 2015). Multiple-linear regression is
often used to determine how many specific factors including price, interest rate, and specific
industries or sectors influence the price changes of a product. Multivariate linear regression is
where several correlated response variables are predicted, instead of one response variable (N.A.,
Linear Regression, 2015).
Regression model to forecast monthly sales:
For forecasting cold beverage sales for the next year the progression model could account
for the following variables: the price of the new beverages (explanatory variable), and cold
beverage sales of competitors (response variable), since WidgeCorp does not have any figures of
their own yet; the unique product line introduced to the market (explanatory variable), and sales
of the new beverage (response variable); and the exotic blends from around the world
(explanatory variable), and sales in heavily minority areas or overseas sales (response variable).
Product suggestions:
To be competitive in the cold beverage industry, WidgeCorp needs to create a line of
beverages that is different from their competitors, and will be enticing for the consumer to try. I
would recommend creative, unique and tasty flavors, at prices that will attract even the most
discerning palate. Flavors could include: exotic tropical fruit blends from around the world; iced
tea and lemonade, both traditional and fruit blends; fruit juices and fruit blends; and new spin
and traditional cola, rootbeer, birch beer and cream soda. When the line becomes popular, I
recommend offering the product in bottles, cans both individually and by the case, and 2 liter
bottles. Think about how popular they would be at home and beach parties, night clubs as
mixers, and vending machines everywhere.
The thirteen steps to statistical modeling:
(It is important to note that all regression models follow the same 13 steps, but the order and the
specifics will vary depending on the type of data and the model used (Karen, 2013)).
1) Write the research questions in hypothetical and practical terms, be specific, base the
question on a relationship you want to test, and never by vague.
2) Define and design the study based on a clear idea.
3) Choose the variables for answering research questions, determine the level of
measurement for each response and the explanatory value to determine the type of
information you get from the chosen model.
4) Write an analysis plan, based on best guess for the statistical method that will answer
the research questions, and remember it is not final, it is just a reasonable approximate.
5) Calculate sample size estimations, and do not run a post-hoc power analysis, it tells you
nothing.
6) Collect, code, clean, and enter the variables based on the analysis plan.
7) Rewrite the variables in the correct form, create indices, categorize, reverse code, put
the variables in their final form, and run principal components or factor analysis.
8) Run Univariate and Bivariate Statistics to determine if there are issues with the data.
9) Run an initial model to determine if you must change the model being used.
10) Redefine explanatory variables and check model fit, eliminate interactions and research
questions to refine the analysis, eliminate non-significant control variables, do
hierarchical modeling, check for over dispersion, and test the best specifications of
random effects.
11) Test assumptions to confirm, check, and refine variables.
12) Check and resolve data issues that can affect the model including: multicollinearity,
outliers, influential points, missing data, truncation and censoring.
13) Interpret the results to ensure the data, explanatory variables and results are correct, and
if they are not, go back to previous steps.
REFERENCES
Hamel, G. (2015). What Are Some Ways Linear Regression Can Be Applied in Business
Settings? Retrieved from smallbusiness.chron.com › … › Setting up a Business
Karen. (2013, April 15). The Thirteen Steps of Statistical Modeling in any Regression or
ANOVA. Retrieved from www.theanalysisfactor.com/13-steps-regression-anova
N.A. (2015). Comparing Linear Regression to Nonlinear Regression. Retrieved from
http://www.graphpad.com/guides/prism/6/curvefitting/index.htm?reg_the_differencese_between_linea.htm
N.A. (2015, April 7). Linear Regression. Retrieved from
en.wikipedia.org/wiki/Linear_regression
Okoro, H. (2015, April 28). Week 4 Live Chat 7. Retrieved from www.ctuonline.edu/live chat 7
Saint-Leger, R. (2015). Business Forecasting Using Historical Data and Regression Analysis.
Retrieved from smallbusiness.chron.com › … › Starting a Home-Based Business