Summary of Information
Lesson II
Getting Started with Stats
Why Might You Be Involved in Doing Statistical Research?
- Manager as research-based decision maker
- Subordinate employee as researcher
- Manager as research services buyer/evaluator
- Manager as evaluator of secondary data sources
- Research specialist
Research Defined
A systematic inquiry at providing information to solve managerial problems
- Beginning researchers should understand that research is a process of reasoning with facts
- I like to “let the data speak to me.”
- What did you find from your articles for tonight?
- There is much you can do, but what SHOULD you do?
What is the decision dilemma we face?
What can research tell us/accomplish?
How do we define “best”?
- What type of study do we need to do?
Reporting
Descriptive
Explanatory
Predictive
Research Defined continued…
- Basic vs. applied research
Basic – aims to discover new knowledge in a more general sense; scientists
Applied – an effort to solve an immediate problem; to make a particular management decision
- Primary vs. Secondary research
- Initial research vs. Problem solving
- Survey vs. Experimental research
Research Defined continued…
The Role of Statistics in Research
- “Statistical thinking is necessary…for effective decision making in various facets of business”
- The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions
- Can be to capture a population’s characteristics & make inferences from a sample’s characteristics
- Many firms have data they don’t mine for regular decision making insights.
Some Examples
- You work for Books R Us and have been asked to look at their sales data to see how to boost sales.
- What questions do we need to ask?
To what purpose will the research be put
How do I shape my research to provide valid, quantifiable results?
If I do an opinion survey, how many people do I need to ask?
How do I craft my questions?
How have similar studies been conducted in the past?
Questions to Ponder…
- What types of research does your organization use?
- How can research help your organization save money?
- Do you feel that the amount of research being conducted has increased or decreased over the past 10 years?
- How has the Internet changed the quality of quantity of research?
Creating a Research Plan and Design
Time Series Data for the Economy – in class exercise.
Research Designs
- The general research process contains three major stages:
Exploration of the situation
Collection of data
Analysis and interpretation of the results
- What questions do the data prompt you to ask?
- Research study idea: What events triggered changes in key indicators (i.e., oil prices)? How do variables interrelate (i.e., inflation, personal taxes & GDP?)
Research Designs
- The essentials of a research design
What data do we need?
Is the data available?
Do we need to “massage” it?
Is it longitudinal vs. cross sectional (which was the Econ data and the Gas price data)?
- Quantitative vs. qualitative data
Research Methods
Exploratory Studies
- Useful when we lack a clear idea of the problem
- Saves time and money
- Relies more heavily on qualitative techniques
- An exploratory study is finished when we have achieved the following:
Established the major dimensions of the task
Defined a set of investigative questions to guide a detailed research design
Developed several hypotheses about possible causes of the management dilemma
- May use a focus group.
Case Studies
- Much of what you’ve done so far at UOP has been of this nature.
- Can be qualitative or quantitative.
- Will tend to use primary data, but can also use secondary data
What kind of data is the Gas Station data?
What kind of data are the Econ stats?
You might need to interview people – what questions do you ask & how ask them?
Descriptive Studies
- More formalized study with clearly stated hypothesis or investigative questions
- Descriptions of phenomena associated with a subject population - subjects
- Discovery or associations among different variables – correlational study
- How is this different from a causational study?
Need to ask: Do the variables interact/effect each other?
In other words, can the “dependent variable” impact or effect the “independent variable”
Causal Studies
- How one variable affects, or is responsible for, changes in another variable
- Possible variable relationships
Symmetrical – no direct link, but fluctuate together
Reciprocal – when 2 variables mutually influence or reinforce each other
Asymmetrical – changes in one variable are responsible for changes in another
Questions to Ponder…
- When is it appropriate to use exploratory research?
- Descriptive research is usually used in the marketing and sales business functions. What other areas might descriptive research be used?
- When conducting causal research, how can researchers keep variables constant throughout the entire research period?
- What factors should be considered when conducting a longitudinal study?
- Can decision-making be accomplished by using only cross-sectional research?
Theory Building
Theory
- Theory – set of systematically interrelated concepts, definitions, and propositions that are advanced to explain and predict facts
- Our ability to make rational decisions is measured by the degree to which we combine fact and theory, each of which is necessary for the other to be of value
- For our purposes, it is helpful to do a literature search of studies that are similar in nature to see what “guiding principles” we can glean – we will not be looking to expand the academic literature by coming up with a new theory.
Reasoning
Deductive reasoning – form of inference; the conclusion must necessarily follow for the reasons given; imply the conclusion and represent a proof
- For a deduction to be correct, it must be both true and valid:
Premises (reasons) given for the conclusion must agree with the real world (true)
The conclusion must necessarily follow from the premises (valid)
- Use Research to avoid Thoughtless Thinking!
Inductive vs. Deductive Thinking
Induction vs. Deduction Cont.
Deduction: If A = B, and B = C then A = C
The conclusion is contained in the premise.
If we deny the premise, we deny the conclusion.
Induction: A conclusion drawn from one or more facts.
The conclusion explains the facts.
The facts support the conclusion
What Causes Societal Dysfunction?
“Data Correlations show that in almost all regards, the highly secular democracies consistently enjoy low rates of societal dysfunction, while pro-religious and anti-evolution America performs poorly.” http://moses.creighton.edu/JRS/2005/2005-11.html
“Since 1962-63 … the judicial rejection of natural law and the embracing of relativism, the United States has become number one in the world in violent crime, divorce, and illegal drug use.” (Barton, David: The Myth of Separation, p. 217, © 1992)
What causes societal dysfunction in the U.S.? What is your theory?
Hypothesis Testing
- Hypothesis - A tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation
- Role of the hypothesis
Guide the study
Identifies relevant factors
Leads to data collection
Frames work in which to organize conclusions
Refining the Research Problem
Operational Definitions
- Requires the use of concepts, constructs, and definitions; building blocks of theory
- Concept – a generally accepted collection of meanings or characteristics associated with certain events, objects, conditions, situations, and behaviors
- The success of research hinges on:
How clearly we conceptualize and how well others understand the concepts we use
The challenge is to develop concepts that others will clearly understand
Concepts and Constructs
Constructs
- Is an image or idea specifically invented for a given research and/or theory building purpose
- Operational definitions – stated in terms of specific testing or measurement criteria
- Variables – used as a synonym for construct; a symbol to which we assign numerals and values
Benchmarking
- A search for best practices that leads to superior performance; measurement
- Applied to many areas
goods and services
business processes
performance measures
- Key steps in benchmarking
planning, analysis, integration, action, and maturity
- Types of benchmarking
internal, competitive, functional, and generic
Let’s Develop a Bus. Research Problem: Sell More Phone/Data Lines
- Construct
Market share of lines
Install timeframe
Sales activity level
Pricing
Customer service quality
- Benchmarks
Market breakeven %
% of orders installed in X time frame
10 new appoint/wk.
10% below SBC Price
2 hr. response time
Learning Objectives
- What is the Difference between Descriptive and Inferential Statistics?
- What is a Binomial Distribution and how it relates to Stats
- What is the Variance and Standard Deviation
- An Introduction to the Concept of a Z Distribution Table
Statistics
- The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions
- Descriptive stats – methods for organizing, summarizing, and presenting data in an informative way
- Inferential stats – methods used to determine something about a population, based on sample
Descriptive Statistics
- With Descriptive Statistics you are simply describing “what is” or “what the data show.”
- For example, “60% of people survey said they prefer Coke”. Or “Both Social Security and Defense Spending make up 21% of the federal budget.”
Example of Gas Price Data
| Gas Station Name | Day | Date | Time of Day | Location | Gas Price |
| Gas America | Monday | 3/8/2010 | Morning | 21st and Franklin Rd. | $2.58 |
| BP Gas Station | Tuesday | 3/9/2010 | Afternoon | 21st and Post Rd. | $2.75 |
| BP Gas Station | Sunday | 3/7/2010 | Morning | 21st and Post Rd. | $2.59 |
| Admiral Gas Station | Tuesday | 3/9/2010 | Afternoon | E. 21st Street. | $2.59 |
| Marathon Gas Station | Monday | 3/8/2010 | Evening | 21st and Mithoeffer Rd. | $2.69 |
| Shell Gas Station | Tuesday | 3/9/2010 | Afternoon | 21st and Post Rd. | $2.75 |
| Circle K | Tuesday | 3/9/2010 | Afternoon | 96th & Meridean | $2.75 |
| Meijer Gas Station | Saturday | 3/6/2010 | Morning | Rockville & Raceway | $2.62 |
| Speedway Gas Station | Tuesday | 3/9/2010 | Afternoon | Rockville & Girlschool | $2.69 |
| Speedway Gas Station | Tuesday | 3/9/2010 | Morning | St. Rd. 32 | $2.67 |
Each row is a “case”. Variables are across the top. Which one is a quantitative measure? Which is qualitative? See p. 4 and 5
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Inferential Statistics
- With Inferential Statistics you are asking the data to “speak to you” to infer from the sample data certain characteristics about the population we are studying.
- For example, the formula to the right measures the variation or dispersion of data above and below the population mean.
2 = S (x- µ)2
N
µ
Variance of a Population
How Long does it Take to Catch a Fish?
µ
Let’s take 4 samples on different parts of the lake to estimate “The Truth”.
30 seconds
30 min.
1 hour
2 hours
The Impact of Statistics
- Positive
Data translation into useful information
Answers questions of uncertainty
Constructive hedging
- Negative
Misleading
Abusive
Preemptive bias
The Impact of Statistics continued…
- Personal
“You might not want to use statistics, but statistics are being used on you”
Buyer behavior
Product availability
Wage determination
Actuarial tables’ influence on insurance products
Queuing theory
Variables
Types of Variables
- Categorical Places individual into one of several groups or categories
Attribute – gender, age, ethnicity, education level
- Quantitative: Takes Numerical values, allowing adding and calculating averages
Continuous – can take on fractional values; infinite number of values b/n units on the scale; always an approximate; height
Types of Variables
- Variable – trait, attribute that can take on different values at different times
- Constant variable – doesn’t change
- Qualitative – based on qualities that can be classified but not measured; difference of types of kinds; gender
- Quantitative – measurable differences in amount
- Discrete – no possible values b/n adjacent units on the scale (whole #’s); dichotomous; marital status
Flip a coin 10 times.: Either heads or tails (1 or 0)
Results in a Binomial Distribution
Analogy: Product either perfect or imperfect.
We can run statistical tests to predict the probability of H or T, Good or Bad
Types of Variables continued…
- Control – most important in research study; most difficult implications of study
- Independent – research systematically manipulates; experimental treatment; measure to observe effect of dependant variable
- Dependant – researcher measures to observe the effects of independent variable
- Intervening or modifying – originates within subject; psychological or emotional reaction; can cause errors in study; fear, anxiety, anger, etc. Need to control by getting to know people, developing rapport
- Extraneous or confounding – Appears to be related, but in fact is not related. Can lead to false conclusions.
Make a List of Variables for Gas Price Data…
- Make a list of the variables for your team project.
- What type of variable are they?
- How will you collect the data?
- What do you know about the data source? – Would someone dispute this data, recommend other data to consider, or a different research design to analyze it?
What Type of Data?
Levels of Measurement
- Measurement – procedure for assigning a value (numbers) to an observation (variable) according to certain rules
- Nominal – categories are mutually exclusive
Male vs. Female, Yes vs. No
- Ordinal – categories have a logical order
Likert Scale, 1 – 5, Strongly Agree to Strongly Disagree (is Bob a good prof.?)
- Interval – equal distance between categories
Time, temp.
- Ratio – True Zero point exits
Height, weight, distance
Ratio Analysis
- Handout of moving average of GDP.
- Analyze the Books R Us data – 3 week moving average. (This is on “Books R US Raw Data” excel file.
- How do we put this into a research question that allows us to collect & analyze data?
- How will we measure the data we collect?
Role of Statistics
The Language of Statistics
- Distribution
- Bar Graph
- Histogram
- Pareto Chart
- Time Plot
- Cross-sectional data
- Quartile
- Box Plot
- Variance
- Coefficient of Variation
- Resistant Measure
- Degrees of freedom
Definitions in Statistics
- Population
- Sample
- Standard deviation
- Normal Distribution
- Statistic versus parameter
- Random
- Mean
- Median
- Mode
- Right Skew
- Left Skew
- Stemplot
Symbols
- (Uppercase Sigma) = Summation
- (Mu) = Population mean
- (Lowercase Sigma) = Standard deviation
- (Pi) = Probability of success in a binomial trial
- (Epsilon) = Maximum allowable error
- 2 (Chi Square) = Nonparametric hypothesis test
- ! = Factorial
- H0 = Null hypothesis
- H1 = Alternate hypothesis
Measures of Central Tendency
- Central Tendency – the tendency of a set of data to center around certain numerical values
- Mean – computed by summing all the observations in the sample and dividing the sum by the number of observations; considers the magnitude of each observation
- Median – is the observation that divides the distribution into equal parts; most typical observation in a distribution
- Mode – the observation that occurs most frequently; if all the values are different, there is no mode
Means, Medians, and Modes
Age
- 25
- 28
- 30
- 30
- 33
- 34
Mean =30
Median = 30
Mode = 30
Mean
Median
Mode
Frequency
SX
N
µ =
25+28+30+30+33+34
6
= 30
Measures of Central Tendency
- The idea is that the sum of the differences between any given observed value & the mean = 0
- S(X – ) = 0 What’s an “X”?
- What do we do if they all add up to 0?
Calculate Deviation Scores
25 28 30 33 34
-2 +3
-5 +4
| x | x - µ | |
| 25 | (25-30) | -5 |
| 28 | (28-30) | -2 |
| 30 | (30-30) | 0 |
| 30 | (30-30) | 0 |
| 33 | (33-30) | 3 |
| 34 | (34-30) | 4 |
| 180/6=30 | 0 |
Population Variance & Population Standard Deviation
- Population variance – can be used to compare dispersion in 2 or more sets of observations
On average, 1 standard deviation in student’s ages is 3.0 years from the mean of 30 years.
- Population Standard deviation – square root of the variance (the more alike they are, more reliable they are)
- The value s = 3.0 indicates, that on average, observations fall 3.0 units + or - from the mean
9 = 3
= S (x- µ)2
N
Measures of Dispersion
2 = S (x- µ)2
N
= 54 = 9 variance
6
9 = the measure of variability that indicated how much all of the scores in a distribution typically deviate or vary from the mean
- Population variance also know as ‘mean deviation’ (mean of squared deviation from the mean)
| x | x - µ | (x- µ)2 |
| 25 | -5 | 25 |
| 28 | -2 | 4 |
| 30 | 0 | 0 |
| 30 | 0 | 0 |
| 33 | 3 | 9 |
| 34 | 4 | 16 |
| 180 | 0 | 54 |
Sample Variance
Properties of the “s”
Gives a measure of dispersion relative to the mean
Sensitive to each score in the distribution
If a score is moved closer to the mean, then the standard deviation will decrease, if the score shifts away from mean, the standard deviation increases
3. Tends to underestimate the population variance, so provide an appropriate correction by subtracting 1 from total observations (n-1)
Sample Variance Example
s2 = S (X - X )2
n – 1 Sample
variance formula
54
(5-1)
Variance: 13.5 yrs
Standard Deviation: 13.5 = 3.67 yrs
| x | x - x | (x-x)2 | x2 |
| 25 | -5 | 25 | 625 |
| 28 | -2 | 4 | 784 |
| 30 | 0 | 0 | 900 |
| 33 | 3 | 9 | 1089 |
| 34 | 4 | 16 | 1156 |
| 150 | 0 | 54 | 4554 |
Let’s Analyze Class Ages
| Mean and Standard Deviation | ||||
| For 5 Learning Teams | ||||
| Team1 | Team2 | Team3 | Team4 | Team5 |
| 24 | 35 | 31 | 35 | 44 |
| 31 | 34 | 24 | 44 | 27 |
| 30 | 24 | 27 | 31 | 25 |
| 47 | 22 | 24 | 42 | 27 |
| 29 | 24 | 21 | 21 | |
| 25 |
Calculate means and Standard Deviations.
Create a bar chart
Create a histo gram
Create a Pareto Chart
*
Bar Chart
Chart1
| Team1 |
| Team2 |
| Team3 |
| Team4 |
| Team5 |
Class ages
| Mean and Standard Deviation | ||||||||||||||
| For 5 Learning Teams | Class Average | |||||||||||||
| Team1 | Team2 | Team3 | Team4 | Team5 | ||||||||||
| 24 | 35 | 31 | 35 | 44 | ||||||||||
| 31 | 34 | 24 | 44 | 27 | ||||||||||
| 30 | 24 | 27 | 31 | 25 | ||||||||||
| 47 | 22 | 24 | 42 | 27 | ||||||||||
| 29 | 24 | 21 | 21 | |||||||||||
| 25 | ||||||||||||||
| Mean | 32.2 | 28.75 | 26 | 33 | 28.8 | 29.92 | ||||||||
| Sample Stand Deviation | 8.7005746937 | 6.7019897543 | 3.0822070015 | 9.1433035605 | 8.8430763878 | 7.4238534468 | ||||||||
| Coefficient of Var | 0.270204183 | 0.2331126871 | 0.1185464231 | 0.2770698049 | 0.3070512635 | 0.2481234441 | ||||||||
| Note that you go to "Insert" then "Function" then "Average" | ||||||||||||||
| to get the formula for the mean. | ||||||||||||||
| For the team standard deviations, do the same thing and then use the STDEV function (standard deviation for a sample) | ||||||||||||||
| For the class total standard deviation, use the STDEVP function, which is for a population. | ||||||||||||||
| Now, try doing the Skewness calculation on your own in the yellow highlighted area. | ||||||||||||||
| Team1 | Team2 | Team3 | Team4 | Team5 | Team 4 | Team 1 | Team 5 | Team 2 | Team 3 | |||||
| Mean | 32.2 | 28.75 | 26 | 33 | 28.8 | 33 | 32.2 | 28.8 | 28.75 | 26 |
Class ages
Histogram
Chart1
| 32.2 | 28.75 | 26 | 33 | 28.8 |
Class ages
| Mean and Standard Deviation | ||||||||||||||
| For 5 Learning Teams | Class Average | |||||||||||||
| Team1 | Team2 | Team3 | Team4 | Team5 | ||||||||||
| 24 | 35 | 31 | 35 | 44 | ||||||||||
| 31 | 34 | 24 | 44 | 27 | ||||||||||
| 30 | 24 | 27 | 31 | 25 | ||||||||||
| 47 | 22 | 24 | 42 | 27 | ||||||||||
| 29 | 24 | 21 | 21 | |||||||||||
| 25 | ||||||||||||||
| Mean | 32.2 | 28.75 | 26 | 33 | 28.8 | 29.92 | ||||||||
| Sample Stand Deviation | 8.7005746937 | 6.7019897543 | 3.0822070015 | 9.1433035605 | 8.8430763878 | 7.4238534468 | ||||||||
| Coefficient of Var | 0.270204183 | 0.2331126871 | 0.1185464231 | 0.2770698049 | 0.3070512635 | 0.2481234441 | ||||||||
| Note that you go to "Insert" then "Function" then "Average" | ||||||||||||||
| to get the formula for the mean. | ||||||||||||||
| For the team standard deviations, do the same thing and then use the STDEV function (standard deviation for a sample) | ||||||||||||||
| For the class total standard deviation, use the STDEVP function, which is for a population. | ||||||||||||||
| Now, try doing the Skewness calculation on your own in the yellow highlighted area. | ||||||||||||||
| Team1 | Team2 | Team3 | Team4 | Team5 | Team 4 | Team 1 | Team 5 | Team 2 | Team 3 | |||||
| Mean | 32.2 | 28.75 | 26 | 33 | 28.8 | 33 | 32.2 | 28.8 | 28.75 | 26 |
Class ages
Pareto Chart
Chart1
| Team 4 |
| Team 1 |
| Team 5 |
| Team 2 |
| Team 3 |
Class ages
| Mean and Standard Deviation | ||||||||||||||
| For 5 Learning Teams | Class Average | |||||||||||||
| Team1 | Team2 | Team3 | Team4 | Team5 | ||||||||||
| 24 | 35 | 31 | 35 | 44 | ||||||||||
| 31 | 34 | 24 | 44 | 27 | ||||||||||
| 30 | 24 | 27 | 31 | 25 | ||||||||||
| 47 | 22 | 24 | 42 | 27 | ||||||||||
| 29 | 24 | 21 | 21 | |||||||||||
| 25 | ||||||||||||||
| Mean | 32.2 | 28.75 | 26 | 33 | 28.8 | 29.92 | ||||||||
| Sample Stand Deviation | 8.7005746937 | 6.7019897543 | 3.0822070015 | 9.1433035605 | 8.8430763878 | 7.4238534468 | ||||||||
| Coefficient of Var | 0.270204183 | 0.2331126871 | 0.1185464231 | 0.2770698049 | 0.3070512635 | 0.2481234441 | ||||||||
| Note that you go to "Insert" then "Function" then "Average" | ||||||||||||||
| to get the formula for the mean. | ||||||||||||||
| For the team standard deviations, do the same thing and then use the STDEV function (standard deviation for a sample) | ||||||||||||||
| For the class total standard deviation, use the STDEVP function, which is for a population. | ||||||||||||||
| Now, try doing the Skewness calculation on your own in the yellow highlighted area. | ||||||||||||||
| Team1 | Team2 | Team3 | Team4 | Team5 | Team 4 | Team 1 | Team 5 | Team 2 | Team 3 | |||||
| Mean | 32.2 | 28.75 | 26 | 33 | 28.8 | 33 | 32.2 | 28.8 | 28.75 | 26 |
Class ages
Coefficient of Variation (CV)
- The ratio of the standard deviation to the absolute value of the mean, expressed as a percentage
- Useful when: data are different units or the data are in the same units, but the means are far apart
- CV = s
X
(100) converts the decimal to a %
Normal Probability Distribution
Probability of 4 Heads in a Row
- Mutually exclusive – Must assume outcomes (H or T) are mutually exclusive.
- Collectively exhaustive – at least one of the events must occur when an experiment is conducted
- If meet these two tests, then the sum of the probabilities equals 1.
Three Main Properties of a Normal Distribution
- Bell-shaped curve – extending infinitely in both directions; symmetrical about the mean µ
- All normal distributions have a particular internal distribution for the area under the curve; the relative area between any 2 designated points is always the same
The area under the curve between 2 points can be interpreted as the relative frequency (P) of the values included between those points
- Theoretical distribution defined by 2 parameters: the mean µ and the standard deviation s
Characteristics of a
Normal Distribution
Area Under the Normal Curve
- All normal curves are symmetrical and have an area of 1.0
- Use a ‘standardized unit’ to compare with various normal curves; one that has a mean of 0 and a standard deviation of 1
- By standardizing a normal distribution, we can report the distance between the mean in units or the standard deviation.
- It’s called a Z Statistic
Z Score – Standardized Score
- Z score –the distance between a selected value, X, and the mean, m
Z Score
- X is the value of any observation or measurement
- µ is the mean of the distribution
- s is the standard deviation of the distribution
- By determining the z value, we can find the area or the probability under any normal curve.
- This is the probability that an observation is between 0 and the standard deviation (z score)
- The % under the curve, the likelihood of the observation, by using the Empirical Rule (68-95-99.7 Rule)
The Lemonade Stand Game
Profits
=
Total Revenue – Total Costs
http://www.coolmath-games.com/lemonade/
Lemonade Stand Game
- We had to buy cups, lemons, sugar, and ice
- We had to forecast sales based on the weather.
- Did you notice that you could get 12 cups to a pitcher?
- More if you used lots of ice
Some Variables from the
Lemonade Stand Game
- Cups Sold per day
- Potential Customers Per Day
- Total Revenue Per Day
- Total Costs Per Day
- Net Revenue Per Day
- Running Profit/Loss
Pricing for Lemonade Stand Game
| # of Cups | Price |
| 25 | $ .80 |
| 50 | $1.57 |
| 100 | $3.13 |
| # of Lemons | Price |
| 10 | $ .69 |
| 30 | $2.02 |
| 75 | $4.28 |
| Cups of Sugar | Price |
| 8 | $ .71 |
| 20 | $1.78 |
| 48 | $3.48 |
| # Ice Cubes | Price |
| 100 | $ .78 |
| 250 | $2.18 |
| 500 | $3.80 |
At the start of the game, you’re given $20. You have to decide what price to charge your customers per cup of lemonade. The default price to start is $.25, but you can change that. You then have to plan on the number of POTENIAL customers you might have, and purchase supplies to meet your forecasted demand. Of course, the actual number of customers is probably going to be less. Therefore, the question is: Will you make enough money to cover the cost of your supplies and make a profit, or will you lose money and have to take out a loan to purchase more supplies for the next day?
Assignment for Next Meeting
- Look at the pricing for lemons, ice, cups, and sugar.
Remember that a pitcher will serve 12 cups if there is no ice, and about 20 cups if there are 3 ice cubes per cup.
- Can we afford our choice of lemons per pitcher, sugar per pitcher, and ice per cup? Can we make money at the price per cup that we decide to charge?
- It will help if you do the following and SHOW YOUR WORK.
1. Calculate the number of cups/pitcher you can serve. ROUND UP to the nearest whole number.
2. Calculate the number of lemons, cups of sugar, and cups you’ll need to buy.
3. Look at the quantity pricing for each item and add up the cost to purchase your supplies.
4. Add up your costs. Are they less than $10?
5. Calculate your ACTUAL number of customers (the percent of potential customers) and multiply by your price per cup. Did you make money?