Chapter 18 – Pricing Setting in the Business World There are few Methods for setting pricing – costs methods vs demand methods Formulas considering costs and mark up will help you to do the Problem set assignment: 1. Markup for setting prices (Mark up $ = SP-CP); MARK UP % = (MU $/SP) X100) Formula for setting price with the markup method SP = Cost/(1- Markup %) Example - retailer buys A hat for $15 and wants a 40% markup, his selling price would be…. SP = 15/(1-.40)=.60 = $25.00 2. Understand Role of different costs – fixed, variable, total costs and average costs 3. What is the breakeven point? Formula for calculating the Break Even point. BE = Total Fixed Cost/Fixed cost contribution Fixed Cost Contribution=Price – variable cost 4. Average Cost = when there are many flavors/types of the same product, producer determines average cost and then add the mark up to set a common selling price. .ANOVA Analysis of Variance is a method of testing the equality of three or more population means by analyzing sample variance. One-Way ANOVA The one-way ANOVA is used to compare three or more population means when there is one factor of interest. Requirements  The populations have distributions that are approximately normal.  The populations have the same variance.  The samples are simple random samples of quantitative data.  The samples are independent of each other.  The different samples are from populations that are categorized in only one. way One-Way ANOVA is a hypothesis test. There are seven steps for a hypothesis test. Example A professor at a local University believes there is a relationship between head size and the major of the students in her biostatistics classes. She takes a random sample of 20 students from each of three classes and records their major and head circumference. The data are shown in the following table. Step 1: State the null hypothesis. Mean 1 equals mean 2 equals mean 3 equals mean 4. Step 2: State the Alternative hypothesis. At least one mean is different. Step 3: State the Level of Significance. The level of significance is 0.05. Step 4: State the test statistic. variance between samples variance within samples F  The test statistic follows the F distribution which has two degrees of freedom, one for the numerator and one for the denominator. The calculations for the test statistic are complicated, so a software program is generally used for the calculations. We will be using Microsoft Excel for this example. Step 5: Calculate The calculations are done in Microsoft Excel using the data analysis toolpak. Enter the data into the spread sheet as shown here. Click on data and the data analysis tookpak button is on the right. When you click on the button a dialogue box appears. Choose ANOVA One Factor. Then another dialogue box appears. Input range is where the data is in the table. Be sure to put a check in the box for labels in.

1. Chapter 18 – Pricing Setting in the Business World
There are few Methods for setting pricing – costs methods vs
demand methods
Formulas considering costs and mark up will help you to do the
Problem set assignment:
1. Markup for setting prices (Mark up $ = SP-CP); MARK UP %
= (MU $/SP) X100)
Formula for setting price with the markup method
SP = Cost/(1- Markup %)
Example - retailer buys A hat for $15 and wants a 40% markup,
his selling price would be….
SP = 15/(1-.40)=.60
= $25.00
2. Understand Role of different costs – fixed, variable, total
costs and average costs
3. What is the breakeven point? Formula for calculating the
Break Even point.
BE = Total Fixed Cost/Fixed cost contribution
Fixed Cost Contribution=Price – variable cost
4. Average Cost = when there are many flavors/types of the
same product, producer determines average cost and then add
the mark up to set a common selling price.
.ANOVA
Analysis of Variance is a method of testing the equality of three
or more population
means by analyzing sample variance.

2. One-Way ANOVA
The one-way ANOVA is used to compare three or more
population means when there is
one factor of interest.
Requirements
normal.
the same variance.
categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps
for a hypothesis test.
Example
A professor at a local University believes there is a relationship
between head size and
the major of the students in her biostatistics classes. She takes a
random sample of 20
students from each of three classes and records their major and
head circumference.

3. The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
The test statistic follows the F distribution which has two
degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a
software program is
generally used for the calculations. We will be using Microsoft
Excel for this example.
Step 5: Calculate

4. The calculations are done in Microsoft Excel using the data
analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the
data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box
appears.
Input range is where the data is in the table. Be sure to put a
check in the box for labels
in the first row and then pick a cell for the results to be
displayed in.
We need to be able to interpret these results.
Step 6: Statistical Conclusion
The summary output from Excel gives the observed test statistic
as well as the critical
value.
The null hypothesis is rejected if the observed test statistic is
greater than the critical
value.
Since F equals 0.816124 and is less than 2.769431 which is the
F critical value, then we

5. fail to reject the null hypothesis.
Step 7: Experimental Conclusion
There is not sufficient evidence to indicate that there is a
statistically significant
difference between the mean head size of different majors in the
biostatistics classes of
this professor at a level of significance of 0.05.
Two-Way ANOVA
There is an interaction between two factors if the effect of one
of the factors changes for
different categories of the other factor.
Requirements
a distribution that
is normal.
the same
variance.

6. Example
A professor at a local University believes there is a relationship
between head size, the
major of the students, and the gender of students in her
biostatistics classes. She takes
a random sample from her three classes. The data is in the
following table.
Notice that the sample size for each set of categories is the
same. (i.e., female and
premed had 4 data values as does male and premed etc…)
A two-way ANOVA essentially does three different hypothesis
tests. The first test is for
interaction effect, then effect from each of the two factors. All
the test statistics are
calculated at once in Microsoft Excel (data analysis toolpak).
Start by entering the data in Excel as follows then select the
data analysis toolpak under
the data tab.

7. Then select ANOVA: Two factor with replication (since there is
more than one sample
per category duo)
Select the data that is to be used and the number of data values
in each category due.
That is 4 for this example. The default alpha is 0.05. Then
select the cell to have the
results output to.
The results give a lot of information but we are concerned with
the last table displayed.
The ANOVA table gives the F test statistic for each of the three
tests for consideration.
Start by looking at the interaction test statistic.
We could look at either the critical value or the p-value to
determine if there is an effect
due to interaction. The p-value is the probability of a more
extreme value than the
observed test statistic. If the p-value is less than the critical
value 0.05, then reject the
null hypothesis that there is no effect from the interaction of the
factors. Since

8. hypothesis. So there is
sufficient evidence to indicate an effect due to the interaction of
gender and major.
We can look at the effect of gender and major individually also.
We can again look at the p-value. If p-value is less than the
level of significance then we
reject the null hypothesis that there is no effect from the factor
of major.
the null hypothesis that there
is no effect due to the factor of major.
We can again look at the p-value. If p-value is less than the
level of significance then we
reject the null hypothesis that there is no effect from the factor
of gender.
hypothesis that there is no
effect due to the factor of gender.
Putting all the statistical conclusions together we can see that
there is no effect from the
interaction of gender and major, on the head circumference and
there is no effect on

9. head circumference due to major but there is an effect due to
gender on head
circumference at a statistically significant level of 0.05.
.ANOVA
Analysis of Variance is a method of testing the equality of three
or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more
population means when there is
one factor of interest.
Requirements
normal.

10. ch other.
categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps
for a hypothesis test.
Example
A professor at a local University believes there is a relationship
between head size and
the major of the students in her biostatistics classes. She takes a
random sample of 20
students from each of three classes and records their major and
head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.

11. Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
The test statistic follows the F distribution which has two
degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a
software program is
generally used for the calculations. We will be using Microsoft
Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data
analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the
data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.

12. Choose ANOVA One Factor. Then another dialogue box
appears.
Input range is where the data is in the table. Be sure to put a
check in the box for labels
in the first row and then pick a cell for the results to be
displayed in.
We need to be able to interpret these results.
Step 6: Statistical Conclusion
The summary output from Excel gives the observed test statistic
as well as the critical
value.
The null hypothesis is rejected if the observed test statistic is
greater than the critical
value.
Since F equals 0.816124 and is less than 2.769431 which is the
F critical value, then we
fail to reject the null hypothesis.
Step 7: Experimental Conclusion
There is not sufficient evidence to indicate that there is a
statistically significant
difference between the mean head size of different majors in the
biostatistics classes of

13. this professor at a level of significance of 0.05.
Two-Way ANOVA
There is an interaction between two factors if the effect of one
of the factors changes for
different categories of the other factor.
Requirements
alues come from a population with
a distribution that
is normal.
the same
variance.
e values are categorized two ways.
Example
A professor at a local University believes there is a relationship

14. between head size, the
major of the students, and the gender of students in her
biostatistics classes. She takes
a random sample from her three classes. The data is in the
following table.
Notice that the sample size for each set of categories is the
same. (i.e., female and
premed had 4 data values as does male and premed etc…)
A two-way ANOVA essentially does three different hypothesis
tests. The first test is for
interaction effect, then effect from each of the two factors. All
the test statistics are
calculated at once in Microsoft Excel (data analysis toolpak).
Start by entering the data in Excel as follows then select the
data analysis toolpak under
the data tab.
Then select ANOVA: Two factor with replication (since there is
more than one sample
per category duo)
Select the data that is to be used and the number of data values
in each category due.
That is 4 for this example. The default alpha is 0.05. Then
select the cell to have the

15. results output to.
The results give a lot of information but we are concerned with
the last table displayed.
The ANOVA table gives the F test statistic for each of the three
tests for consideration.
Start by looking at the interaction test statistic.
We could look at either the critical value or the p-value to
determine if there is an effect
due to interaction. The p-value is the probability of a more
extreme value than the
observed test statistic. If the p-value is less than the critical
value 0.05, then reject the
null hypothesis that there is no effect from the interaction of the
factors. Since
0.087227 0.0
hypothesis. So there is
sufficient evidence to indicate an effect due to the interaction of
gender and major.
We can look at the effect of gender and major individually also.
We can again look at the p-value. If p-value is less than the
level of significance then we

16. reject the null hypothesis that there is no effect from the factor
of major.
the null hypothesis that there
is no effect due to the factor of major.
We can again look at the p-value. If p-value is less than the
level of significance then we
reject the null hypothesis that there is no effect from the factor
of gender.
t the null
hypothesis that there is no
effect due to the factor of gender.
Putting all the statistical conclusions together we can see that
there is no effect from the
interaction of gender and major, on the head circumference and
there is no effect on
head circumference due to major but there is an effect due to
gender on head
circumference at a statistically significant level of 0.05.

17. .ANOVA
Analysis of Variance is a method of testing the equality of three
or more population
means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more
population means when there is
one factor of interest.
Requirements
normal.
opulations have the same variance.
categorized in only one.
way

18. One-Way ANOVA is a hypothesis test. There are seven steps
for a hypothesis test.
Example
A professor at a local University believes there is a relationship
between head size and
the major of the students in her biostatistics classes. She takes a
random sample of 20
students from each of three classes and records their major and
head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples

19. The test statistic follows the F distribution which has two
degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a
software program is
generally used for the calculations. We will be using Microsoft
Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data
analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the
data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box
appears.
Input range is where the data is in the table. Be sure to put a
check in the box for labels
in the first row and then pick a cell for the results to be
displayed in.
We need to be able to interpret these results.

20. Step 6: Statistical Conclusion
The summary output from Excel gives the observed test statistic
as well as the critical
value.
The null hypothesis is rejected if the observed test statistic is
greater than the critical
value.
Since F equals 0.816124 and is less than 2.769431 which is the
F critical value, then we
fail to reject the null hypothesis.
Step 7: Experimental Conclusion
There is not sufficient evidence to indicate that there is a
statistically significant
difference between the mean head size of different majors in the
biostatistics classes of
this professor at a level of significance of 0.05.
Two-Way ANOVA
There is an interaction between two factors if the effect of one
of the factors changes for

21. different categories of the other factor.
Requirements
a distribution that
is normal.
tion having
the same
variance.
Example
A professor at a local University believes there is a relationship
between head size, the
major of the students, and the gender of students in her
biostatistics classes. She takes
a random sample from her three classes. The data is in the
following table.
Notice that the sample size for each set of categories is the
same. (i.e., female and

22. premed had 4 data values as does male and premed etc…)
A two-way ANOVA essentially does three different hypothesis
tests. The first test is for
interaction effect, then effect from each of the two factors. All
the test statistics are
calculated at once in Microsoft Excel (data analysis toolpak).
Start by entering the data in Excel as follows then select the
data analysis toolpak under
the data tab.
Then select ANOVA: Two factor with replication (since there is
more than one sample
per category duo)
Select the data that is to be used and the number of data values
in each category due.
That is 4 for this example. The default alpha is 0.05. Then
select the cell to have the
results output to.
The results give a lot of information but we are concerned with
the last table displayed.
The ANOVA table gives the F test statistic for each of the three
tests for consideration.

23. Start by looking at the interaction test statistic.
We could look at either the critical value or the p-value to
determine if there is an effect
due to interaction. The p-value is the probability of a more
extreme value than the
observed test statistic. If the p-value is less than the critical
value 0.05, then reject the
null hypothesis that there is no effect from the interaction of the
factors. Since
hypothesis. So there is
sufficient evidence to indicate an effect due to the interaction of
gender and major.
We can look at the effect of gender and major individually also.
We can again look at the p-value. If p-value is less than the
level of significance then we
reject the null hypothesis that there is no effect from the factor
of major.
the null hypothesis that there
is no effect due to the factor of major.
We can again look at the p-value. If p-value is less than the
level of significance then we

24. reject the null hypothesis that there is no effect from the factor
of gender.
hypothesis that there is no
effect due to the factor of gender.
Putting all the statistical conclusions together we can see that
there is no effect from the
interaction of gender and major, on the head circumference and
there is no effect on
head circumference due to major but there is an effect due to
gender on head
circumference at a statistically significant level of 0.05.
.ANOVA
Analysis of Variance is a method of testing the equality of three
or more population

25. means by analyzing sample variance.
One-Way ANOVA
The one-way ANOVA is used to compare three or more
population means when there is
one factor of interest.
Requirements
normal.
ndependent of each other.
categorized in only one.
way
One-Way ANOVA is a hypothesis test. There are seven steps
for a hypothesis test.
Example
A professor at a local University believes there is a relationship
between head size and
the major of the students in her biostatistics classes. She takes a
random sample of 20

26. students from each of three classes and records their major and
head circumference.
The data are shown in the following table.
Step 1: State the null hypothesis.
Mean 1 equals mean 2 equals mean 3 equals mean 4.
Step 2: State the Alternative hypothesis.
At least one mean is different.
Step 3: State the Level of Significance.
The level of significance is 0.05.
Step 4: State the test statistic.
variance between samples
variance within samples
The test statistic follows the F distribution which has two
degrees of freedom, one for
the numerator and one for the denominator.
The calculations for the test statistic are complicated, so a
software program is
generally used for the calculations. We will be using Microsoft

27. Excel for this example.
Step 5: Calculate
The calculations are done in Microsoft Excel using the data
analysis toolpak. Enter the
data into the spread sheet as shown here. Click on data and the
data analysis tookpak
button is on the right.
When you click on the button a dialogue box appears.
Choose ANOVA One Factor. Then another dialogue box
appears.
Input range is where the data is in the table. Be sure to put a
check in the box for labels
in the first row and then pick a cell for the results to be
displayed in.
We need to be able to interpret these results.
Step 6: Statistical Conclusion
The summary output from Excel gives the observed test statistic
as well as the critical
value.
The null hypothesis is rejected if the observed test statistic is
greater than the critical
value.

28. Since F equals 0.816124 and is less than 2.769431 which is the
F critical value, then we
fail to reject the null hypothesis.
Step 7: Experimental Conclusion
There is not sufficient evidence to indicate that there is a
statistically significant
difference between the mean head size of different majors in the
biostatistics classes of
this professor at a level of significance of 0.05.
Two-Way ANOVA
There is an interaction between two factors if the effect of one
of the factors changes for
different categories of the other factor.
Requirements
ll, the sample values come from a population with
a distribution that
is normal.
the same

29. variance.
r.
Example
A professor at a local University believes there is a relationship
between head size, the
major of the students, and the gender of students in her
biostatistics classes. She takes
a random sample from her three classes. The data is in the
following table.
Notice that the sample size for each set of categories is the
same. (i.e., female and
premed had 4 data values as does male and premed etc…)
A two-way ANOVA essentially does three different hypothesis
tests. The first test is for
interaction effect, then effect from each of the two factors. All
the test statistics are
calculated at once in Microsoft Excel (data analysis toolpak).
Start by entering the data in Excel as follows then select the

30. data analysis toolpak under
the data tab.
Then select ANOVA: Two factor with replication (since there is
more than one sample
per category duo)
Select the data that is to be used and the number of data values
in each category due.
That is 4 for this example. The default alpha is 0.05. Then
select the cell to have the
results output to.
The results give a lot of information but we are concerned with
the last table displayed.
The ANOVA table gives the F test statistic for each of the three
tests for consideration.
Start by looking at the interaction test statistic.
We could look at either the critical value or the p-value to
determine if there is an effect
due to interaction. The p-value is the probability of a more
extreme value than the
observed test statistic. If the p-value is less than the critical
value 0.05, then reject the

31. null hypothesis that there is no effect from the interaction of the
factors. Since
hypothesis. So there is
sufficient evidence to indicate an effect due to the interaction of
gender and major.
We can look at the effect of gender and major individually also.
We can again look at the p-value. If p-value is less than the
level of significance then we
reject the null hypothesis that there is no effect from the factor
of major.
the null hypothesis that there
is no effect due to the factor of major.
We can again look at the p-value. If p-value is less than the
level of significance then we
reject the null hypothesis that there is no effect from the factor
of gender.
, then we reject the null
hypothesis that there is no
effect due to the factor of gender.
Putting all the statistical conclusions together we can see that
there is no effect from the

32. interaction of gender and major, on the head circumference and
there is no effect on
head circumference due to major but there is an effect due to
gender on head
circumference at a statistically significant level of 0.05.
Cost Based Mark Up Pricing
Scenario:
· A manufacturer of a new shampoo is planning to use several
different channels of distribution, as listed below.
· Each intermediary in each of the alternative channels uses a
cost-plus approach to pricing. That is, each firm takes a markup
on its selling price to cover operating expenses plus profit.
· The manufacturer sells the shampoo to the next member of
each channel for $2.00 per 12-ounce plastic bottle.
Use the data on the table to answer the following questions.
Operating Expenses as a % of salesProfit Margin as a % of sales
Mark Up %

33. Retail:
Small drugstores 36% 1%
?
Supermarkets 25% 2%
?
Mass-merchandisers 27%
2% ?
Wholesale:
Merchant wholesalers 10% 2%
?1. What is the retail selling price if the
manufacturer sells the shampoo directly to small drugstores?
A. $2.56
B. $3.17 C. $3.49
D. $2.98
E. $3.61
2. What is the retail selling price if the manufacturer sells the
shampoo directly to mass-merchandisers?
A. $2.59
B. $3.65
C. $3.05
D. $2.99
E. $2.823.What is the merchant wholesalers’ price to the
supermarkets?

34. A. $3.04
B. $3.51
C. $2.27
D. $2.56
E. $2.95
4. What is the supermarkets’ retail price?
A. $3.11
B. $2.54
C. $2.27
D. $3.36
E. $2.97
MKT 3301 Fall 2022Problem Set Chapter 18: Price Setting
Name__________________________________
Date______________ Total Points: 40
The best approach is to type out your answer and then upload as
a word document. If you do not show calculations, you will be
awarded only partial credit for correct answers.
Problems
1. An appliance retailer purchased a small vacuum cleaner for
$38.00. He plans to add a 45% markup and resell. What will be
the retail selling price for the vacuum cleaner? (2 pts)
2. If a retailer sells an oriental vase for $210.99. What was his

35. markup if he bought the vase for $122.00 from the wholesaler
(Value 3 pts)
· Markup in actual dollars ________
· Markup (as a % of cost price) ________
· Markup (as a % of selling price) ________
According to our textbook, typically, wholesalers and retailers
use markup which is expressed as a % of ____________ for
convenience.
Select the correct choice:
A cost price B. selling price
3. Walgreens offers Centrum Vitamins for 14.99 per Bottle (for
150 tablets). Occasionally, Walgreens promotes Centrum with a
“buy two, get the third bottle for free” type of promotion.
When a consumer participates in this promotion, what is the
actual price per bottle for Centrum during the promotion?
__________________ (2 pts)
4. Kotler Bathroom product manufacturer just produced a low
flush toilet which saves over 50% on water use per flush of the
toilet. Kotler will make it available with merchant wholesalers
for $97.00. The company expects its wholesalers and retailers
to follow its recommended markup chain percentages where
wholesalers take a 25% markup and retailers take a 40%
markup. What does Kotler expect the final consumers to pay
for this product at retail stores? ___________________ (8 pts)
4. John Fleming, marketing manager for the Athletic Sporting
Goods Company (ASGC) is thinking about how the changes
taking place among retailers in his channel might impact his
strategy. The ASGC is a producer of different lines of sports
products. John is looking for alternative ways to make money.
John Fleming is considering a new strategy to increase sales of
tennis balls and new tennis racquets.
a. The basic idea for

36. ASGC is to sell tennis balls in large quantities to
nonprofit groups who resell the balls to raise money.
For example, a service organization at a local college bought
2,500 tennis balls printed with the college logo. The company
charged $.50 each for the tennis balls-plus a $800 one-time
charge for the stamp to print the logo. The service group plans
to resell the tennis balls for $2.50 each and contribute the
profits to a shelter for the homeless.
Questions based on the above. (5 pts)
5. What is the service organization's average cost per printed
Tennis Ball?
__________________________
6. What is the total profit the service group hopes to make and
contribute to the Shelter if it sells all 2500 tennis balls at $2.50
per ball?
__________________
b. ASGC is also considering adding tennis racquets to the
product lines it produces.
This would require a $500,000 modification to its factory as
well as the purchase of new equipment that costs $1,600,000.
The variable cost to produce a tennis racquet would be $55, but
John thinks that ASGC
could sell the racquet at a wholesale price of $82.
John thinks that if ASGC sells the racquet at a lower price,
many other retailers might decide to carry it. However, the vice
president of ASGC thinks that the tennis racquet is a superior
product and that ASGC
should sell it for $99.99 to upscale country clubs only.
The higher price would give a prestige image.

37. Questions based on the above (10 pts)
7. If
ASGC produces tennis racquets, how many racquets
must it sell at $82.00 and $99.99 to break even?
· Breakeven units at 82.00
_______________________________. (3pts)
· Breakeven units at 99.99
_______________________________. (3pts)
· Which price do you recommend and why?
__________________________ (2pts)
· If
ASGC wants to make at least $40,000 profit off the
racquets, at a selling price of $82.00 what would the breakeven
quantity be__________________________ (2pts)
9. Yoplait has new line of Greek yogurt in a 9 oz size. The
Marketing Managers plan to test market the product in one
small market for three weeks. They calculated the cost and
projected sales for each of the different flavors:
Plain cost is .55 each with the projected sales of 15,000 units,
Strawberry cost per unit is .70 with the projected sales of
18,000 units, Blueberry cost are .78 per unit with the projected
sales of 12,000 units.
Each yogurt product will retail at the same price.
Using the average cost and a 40% markup, what will
Yoplait price the 9 oz. Greek Yogurt at the retail level?
Ans. ______________ 10 pts.

38. 1