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Dr. Ramzan Tahir
This course will concentrate on business domains marketing, operations, and
accounting and
the use of data and data analysis to support business decisions.
There will be a focus on how specific business problems in these areas can be
solved.
In addition to theoretical and conceptual learning being required, team-based
management skills will also be developed and utilized.
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The development of case-based analytical skills, requiring basic descriptive
statistical measures and MS Office will also be a focus of this course.
The expectation is that through the use of case studies these skills will be used along
with applicable business theory to analyze, evaluate, recommend, and present
courses of action.
Students will work both individually and in teams.
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Prepare students to successfully manage in dynamic, constantly changing work
environments.
Provide students with an overall integrative comprehension of the basic quantitative
methods required to successfully run a business.
Help students learn about how to improve their individual and team-based working skills.
Provide students with the skill set needed to effectively analyze and generate numerical
information needed to support business decision-making.
Aid students in improving their research and presentation skills.
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Students will:
Possess a solid understanding of quantitative methods applicable to management.
Be proficient in utilizing statistical tools for data-driven decision-making.
Demonstrate the ability to construct and optimize decision models.
Effectively apply forecasting and predictive modeling techniques in a business context.
Comprehend the role of decision support systems in enhancing managerial decision processes
have improved research and presentation skills.
know how to write a business memo, executive summary, and full case report.
be able to effectively use the MS Office suite for data/information analysis, report generation and
presentation.
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Business Statistics, Sharpe, De Veaux, et al., 3rd Cdn. ed., Pearson Prentice –Hall,
2018, (ISBN: 9780136726548); Chapters covered: 1 – 7
MyITLab for Office 2016: GO! Series [12.19.16]
S. Gaskin , A. Vargas , C. McLellan , K. Martin et al., Pearson Publishing, Textbook
ISBN-13: 9780134497921
MyITLab: Kevin Hauck (kevin.hauck@algomau.ca)
Tutorial Plan: Larry Masters (larry.masters@algomau.ca)
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A = 80 - 100 [excellent performance]
B = 70 - 79 [good performance]
C = 60 - 69 [satisfactory performance]
D = 50 - 59 [marginal performance]
F = 0 - 49 [inadequate performance]
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The Student Feedback of Teaching (SFT) is an important component of teaching
quality evaluation.
The administration of the SFTs for the 2023F term will begin on November 20, 2023
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STATISTICAL MEASURES
Statistics and probability
statistical measures of central tendency and dispersion
graphical and numerical measures
Random variables and probability distributions, binomial, Poisson, normal distributions
The central limit theorem
Point estimation, confidence levels, test of hypotheses
Correlation
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DESCRIPTION OF DATA
Statistics
Continuous:
Mean, Standard deviation, Minimum, Maximum, Median, Quartiles,
Inter-quartile range, range
Categorical:
Frequency, Percentage
Visualization/plots
Continuous:
Scatter plot, Histogram, Correlation plots, line charts
Categorical:
Barplots, Pie Chart
Boxplots
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Statistics is the collection, organization, analysis, interpretation, and
presentation of data
Statistical methods are essential tools in the modern approach to
quality
Without statistical tools, making conclusions about data becomes less certain
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Collection of facts
Information that can be processed
Types of variables Variation
Continuous and Categorical
Different ways of understanding the data
What you can do with Excel
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A variable is a quantity that may change within the context of a problem or
experiment. OR
A variable is a characteristic that can be measured and that can assume
different values. Height, age, income, province or country of birth, grades
obtained at school and type of housing are all examples of variables.
Constants
A constant is a number that is fixed and known
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Categorical variables
A categorical variable (also called qualitative variable) refers to a characteristic that
can’t be quantifiable. Categorical variables can be either nominal or ordinal.
Nominal variables
A nominal variable is one that describes a name, label or category without natural order.
Sex and type of dwelling are examples of nominal variables.
Ordinal variables
An ordinal variable is a variable whose values are defined by an order relation between
the different categories.
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Numeric variables
A numeric variable (also called quantitative variable) is a quantifiable characteristic
whose values are numbers (except numbers which are codes standing up for
categories). Numeric variables may be either continuous or discrete.
Continuous variables
A variable is said to be continuous if it can assume an infinite number of real values
within a given interval. For instance, consider the height of a student.
Discrete variables
As opposed to a continuous variable, a discrete variable can assume only a finite
number of real values within a given interval. An example of a discrete variable would
be the score given by a judge to a gymnast in competition
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The concept of variation: no two items are perfectly identical, e.g.
Cans of tomato soup vary slightly from can to can
Time required to assign a seat at an airline check-in counter varies passenger to
passenger
Statistics helps analyze data and offer conclusions, while taking existence of
variations into account
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Data can be summarized in different ways: tables, graphs,
numerically
A frequency distribution is a tabulation of data arranged
according to size
The frequency distribution spotlights where most of the data are
grouped and how much variation there is
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Data is information that has been translated into a form that is efficient for
movement or processing.
It is acceptable for data to be used as a singular subject or a plural subject.
Collection of facts
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Data can also be summarized by calculating the…
Measure of central tendency
Measure of dispersion
These two measures often provide a satisfactory summary of data
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The key measure of central tendency is the Mean, also known
as average. Other measures of central tendency are the
Median and Mode
Mode: Most frequently occurring data point, e.g.
1,2,2,3,3,3,4,4,5 the mode is 3
Median: Middle value when data is arranged according to
size, e.g. 1, 2, 3, 4, 5
Median useful for data which can be ranked but not easily measured,
e.g. color
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The two key measures of dispersion, or spread of data are the
Range and the Standard Deviation
Range – mostly used in Dispersed data
The difference between the maximum value and the minimum value
Standard deviation – more useful measure of dispersion
There is no formal definition, only a formula
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EXPLORE
Use visuals to take a decision
Find the story Just one visual
Try different visuals Simplify things
Use Complex Calculations
ACTIONABLE
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• Should we reduce non-initiative spending in favor of increasing funding to
advancing higher education?
• Used Color to
highlight
• Narrative to Direct the
reader
• Use order to Signify
Difference
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