Your friend Maz runs a small shop on Etsy selling homemade knit scarves and beanies. Maz is trying to figure out what they needs to sell before Christmas to earn an operating income of $500. You volunteer to help them figure it out because of all you’ve learned in ACTG 211. Maz provides you with the following information about their business:
| Scarf | Beanie | |
| Price per Unit | $11 | $14 |
| Variable Cost per unit | $6 | $6 |
| Sales history from last month | 30 units | 20 units |
| Fixed Cost | $380 |
What is the weighted average cost per unit? Round answer to nearest penny (x.xx).
How many units does Maz need to sell to breakeven? Round answer to a whole unit.
How many units does Maz need to sell to achieve their target operating income? Round answer to a whole unit.
In: Accounting
Write a program in C++ that will output the truth table for a simple wff. There will only be Ps and Qs, so you can hard code the P truth table and the Q truth table into arrays. The user will use A for ^ , O for V, N for ' , & I for -> .
Hint: Read the first character, load the appropriate truth table into the first working array. Read the next character. If it is an N, output the negation of the first working array. If it is a P or Q, oad the appropriate truth table into the second working, array, then read the last character and output the appropriate truth table.
Expression Would be input as
P^Q PQA
P' PN
Q->P QPI
Example runs (note the user input is bold)
Run 1
Please input the wff: PQA
The truth table is
T
F
F
F
Run 2
Please input the wff: P'
The truth table is
F
F
T
T
In: Computer Science
For Exercises explain how each experiment can be simulated by using random numbers.
Two players match pennies.
In: Statistics and Probability
Eclosion refers to the emergence of an adult insect from an egg. The following data gives eclosion rates when nymphs were exposed to heat for various durations.
| Duration (d) | 0 | 1 | 2 | 3 | 5 | 10 | 15 |
|---|---|---|---|---|---|---|---|
| Sample size | 126 | 42 | 47 | 44 | 46 | 42 | 10 |
| # Emerged: | 107 | 38 | 44 | 40 | 38 | 35 | 7 |
Carry out a chi-squared test to decide whether it is plausible
that eclosion rate does not depend on exposure duration.
a. State the appropriate hypotheses.
b. Calculate the test statistic value. (X^2)
In: Statistics and Probability
A researcher wants to investigate the usefulness of hypnotism for reducing levels of anxiety in individuals experiencing stress. He identifies 30 people at random from a group of 100 who have "high stress" jobs. The 30 people are divided into two separate groups. One group of 15 receive the hypnotism. The other group of 15 acts as the control group - they receive no hypnotism. The subjects in each group are then given an anxiety inventory. The results are in the table below, where a higher score means more anxiety.
| Hypnotism Group | No Hypnotism Group |
| 7 | 7 |
| 2 | 7 |
| 7 | 9 |
| 6 | 10 |
| 8 | 11 |
| 6 | 8 |
| 3 | 6 |
| 2 | 12 |
| 6 | 9 |
| 8 | 11 |
| 7 | 7 |
| 5 | 9 |
| 5 | 11 |
| 8 | 10 |
| 7 | 8 |
Please answer all parts to the question
a. Calculate the degrees of freedom.
b. Calculate the difference between the means (MHypnotism - MNo Hypnotism).
c. Calculate Σχ for the Hypnotism Group.
d. Compute Σχ² for the Hypnotism Group.
e. Calculate Σχ for the No Hypnotism Group.
f. Calculate Σχ² for the No Hypnotism Group.
g. Calculate the Standard Error for this test. Round to two decimal places.
S(χ1 – χ2) = ?
f.Calculate the t observed value for this test. Round to two decimal places.
t = ?
g.What is the probability (p) of getting the difference between sample means if the null hypothesis were true? Use Oak Software (NOTE: make sure you input the correct df, select t to p, select the correct area for a left-tailed test, input the correct t value, then press Calculate.
p = ?
h. Based on the results of your analysis, what is your decision regarding the Null hypothesis?
I. Write you conclusion in terms of the effect of hypnotism on anxiety.
In: Statistics and Probability
Career Training According to the study The American Freshman: National Norms 2015, 76.1% of college freshmen said that “to get training for a specific career” was a very important reason for their going to college. Consider a group of seven freshman selected at random. In Exercises 23–26, find the probabilities that the number of people in the group who felt that the reason was very important is as stated. 23. All seven 24. Exactly three of the seven 25. At least six of the seven 26. No more than two of the seven
In: Statistics and Probability
Problem Two
Using the information in data set one, which I have included in the table below, recalculate total cost, fixed cost, variable cost, marginal cost, average total cost, average variable cost and average fixed costs if the price of the variable input (which is labor in this example) is not $50 but $55. I have created Table 2 for you to put your answers in. Assume that fixed costs remain at $220. When the price of a variable input changes which other costs will increase? Compare the costs you calculate for table two to the costs calculated in table one to find your answers.
Table Two for Answers to Problem Two
Units of Labor | Total Product (output) | FC | VC | TC | MC | ATC | AVC | AFC |
|---|---|---|---|---|---|---|---|---|
0 | 0 | |||||||
1 | 3 | |||||||
2 | 7 | |||||||
3 | 12 | |||||||
4 | 16 | |||||||
5 | 19 | |||||||
6 | 21 |
In: Economics
You’ve just been promoted to a management position at Worcester Corset Candies Company. Congrats! Immediately, you get a call that Finance noticed a cost variance in your department. It appears that you are consuming too few ingredients for your scheduled output. A box of Special Mixed Corset Candies is supposed to be 140 ounces, and your annual bonus is paid on how close you manage your department’s per/unit costs. To begin analyzing the process, you ask your fill machine operator to record the weights of 9 candy cartons each hour for the next 11 hours. (HINT: Save yourself a lot of time and do this in Excel). Are all points on the R-chart are within the control limits? Are all points on the X-bar chart are within the control limits? So, manager, is your candy making process considered in control?
Data provided
| Batch | Weight 1 | Weight 2 | Weight 3 | Weight 4 | Weight 5 | Weight 6 | Weight 7 | Weight 8 | Weight 9 |
| 1 | 147 | 136 | 138 | 138 | 131 | 130 | 134 | 148 | 144 |
| 2 | 148 | 137 | 149 | 134 | 134 | 143 | 144 | 150 | 140 |
| 3 | 138 | 141 | 130 | 140 | 135 | 132 | 138 | 134 | 133 |
| 4 | 141 | 143 | 149 | 133 | 143 | 149 | 132 | 135 | 130 |
| 5 | 135 | 141 | 140 | 139 | 135 | 150 | 136 | 130 | 135 |
| 6 | 133 | 146 | 142 | 136 | 150 | 140 | 144 | 147 | 150 |
| 7 | 138 | 137 | 145 | 138 | 134 | 149 | 130 | 131 | 147 |
| 8 | 144 | 137 | 133 | 149 | 131 | 141 | 143 | 149 | 146 |
| 9 | 145 | 134 | 149 | 140 | 142 | 130 | 150 | 142 | 148 |
| 10 | 135 | 146 | 130 | 141 | 135 | 137 | 141 | 140 | 150 |
| 11 | 149 | 142 | 149 | 143 | 142 | 137 | 134 | 139 | 140 |
In: Operations Management
Does undergraduate success predict graduate success? While most
people complete their bachelor's degree during the daytime while
taking multiple classes and not working full-time, those getting an
MBA are typically taking one or two courses at a time, in the
evening or on weekends, and while working and even supporting a
family. Yet one would expect those who perform better in their
bachelor's degree will perform better in their master's. Using a
significance level of .05, test whether there is a correlation
between the BS GPA and the MBA GPA. Also, answer the
following:
a) What is the correlation coefficient & how strong is
it?
b) What is the best fit regression equation that can predict the
MBA GPA from the BS GPA?
c) What percent of the variability in the MBA GPA can be explained
by the regression model?
d) What would you expect a student's MBA GPA to be if he/she had a
3.50 BS GPA?
Data File
| ID | Gender | Major | Employ | Age | MBA_GPA | BS GPA | Hrs_Studying | Works FT |
| 1 | 0 | No Major | Unemployed | 39 | 2.82 | 3 | 10 | 0 |
| 2 | 1 | No Major | Full Time | 55 | 4 | 4 | 15 | 0 |
| 3 | 0 | No Major | Part Time | 43 | 3.45 | 3.5 | 3 | 0 |
| 4 | 0 | No Major | Full Time | 56 | 2.61 | 4 | 4 | 0 |
| 5 | 1 | No Major | Full Time | 38 | 3.5 | 3.3 | 5 | 0 |
| 6 | 0 | No Major | Unemployed | 54 | 4 | 3.05 | 5 | 1 |
| 7 | 0 | No Major | Full Time | 30 | 3 | 4 | 6 | 0 |
| 8 | 0 | No Major | Full Time | 37 | 2.5 | 3.6 | 6 | 0 |
| 9 | 0 | No Major | Part Time | 38 | 2.84 | 3.05 | 6 | 0 |
| 10 | 0 | No Major | Full Time | 42 | 3.72 | 3.7 | 6 | 0 |
| 11 | 0 | No Major | Part Time | 52 | 3.21 | 3.5 | 6 | 0 |
| 12 | 0 | No Major | Full Time | 35 | 3.44 | 3.55 | 6 | 0 |
| 13 | 0 | No Major | Full Time | 37 | 3.65 | 2.78 | 6 | 0 |
| 14 | 0 | No Major | Full Time | 53 | 3.02 | 3.3 | 6 | 0 |
| 15 | 0 | No Major | Part Time | 51 | 3.03 | 3.25 | 6 | 0 |
| 16 | 1 | No Major | Full Time | 40 | 3.8 | 4 | 6 | 0 |
| 17 | 0 | Finance | Full Time | 33 | 4 | 3.5 | 6 | 1 |
| 18 | 0 | No Major | Part Time | 53 | 3.26 | 3.5 | 7 | 0 |
| 19 | 0 | No Major | Full Time | 43 | 3.53 | 3.75 | 6 | 0 |
| 20 | 0 | Finance | Unemployed | 35 | 3.75 | 3.9 | 7 | 0 |
| 21 | 0 | No Major | Full Time | 57 | 3.15 | 3.2 | 6 | 0 |
| 22 | 1 | No Major | Part Time | 32 | 3.66 | 3.75 | 8 | 0 |
| 23 | 1 | No Major | Full Time | 59 | 3.36 | 3.45 | 8 | 0 |
| 24 | 1 | No Major | Full Time | 48 | 3.79 | 2.55 | 8 | 0 |
| 25 | 1 | No Major | Part Time | 34 | 2.85 | 3.05 | 8 | 0 |
| 26 | 1 | No Major | Full Time | 53 | 3.74 | 3.9 | 8 | 0 |
| 27 | 1 | No Major | Part Time | 35 | 3.23 | 4 | 2 | 0 |
| 28 | 1 | No Major | Unemployed | 38 | 3.52 | 3.7 | 2 | 0 |
| 29 | 1 | No Major | Part Time | 37 | 3.32 | 3.45 | 2 | 0 |
| 30 | 0 | Finance | Full Time | 46 | 2.89 | 3.1 | 2 | 0 |
| 31 | 0 | No Major | Full Time | 44 | 2.83 | 3.05 | 1 | 0 |
| 32 | 0 | No Major | Unemployed | 31 | 2.93 | 3.1 | 1 | 0 |
| 33 | 0 | No Major | Full Time | 51 | 3.71 | 3.8 | 1 | 0 |
| 34 | 0 | Finance | Full Time | 47 | 3.47 | 2.6 | 4 | 0 |
| 35 | 0 | No Major | Part Time | 56 | 3.52 | 3.8 | 4 | 0 |
| 36 | 1 | Finance | Part Time | 42 | 2.83 | 4 | 4 | 0 |
| 37 | 0 | Finance | Full Time | 44 | 3.64 | 3.55 | 6 | 1 |
| 38 | 0 | No Major | Unemployed | 54 | 2.96 | 3.1 | 6 | 0 |
| 39 | 0 | Finance | Full Time | 51 | 3.59 | 3.9 | 6 | 1 |
| 40 | 0 | No Major | Part Time | 42 | 3.33 | 3.9 | 6 | 1 |
| 41 | 0 | Finance | Full Time | 45 | 3.38 | 3.6 | 6 | 0 |
| 42 | 0 | Finance | Full Time | 55 | 3.44 | 3.35 | 6 | 1 |
| 43 | 0 | No Major | Full Time | 47 | 3.31 | 3.9 | 7 | 0 |
| 44 | 1 | Finance | Unemployed | 43 | 3.03 | 3.25 | 7 | 0 |
| 45 | 0 | Finance | Full Time | 57 | 3.26 | 3.4 | 7 | 1 |
| 46 | 1 | Finance | Full Time | 36 | 3.04 | 4 | 7 | 0 |
| 47 | 1 | No Major | Part Time | 58 | 2.98 | 3.1 | 7 | 0 |
| 48 | 1 | Finance | Full Time | 46 | 2.8 | 3.05 | 7 | 0 |
| 49 | 1 | Finance | Full Time | 53 | 3.75 | 3.75 | 3 | 1 |
| 50 | 0 | Finance | Full Time | 59 | 3.64 | 3.65 | 3 | 1 |
| 51 | 0 | No Major | Full Time | 49 | 3.65 | 3.8 | 3 | 1 |
| 52 | 0 | Finance | Full Time | 34 | 3.18 | 3.3 | 3 | 0 |
| 53 | 0 | No Major | Full Time | 46 | 3.44 | 4 | 3 | 1 |
| 54 | 1 | Finance | Unemployed | 46 | 3.06 | 3.15 | 3 | 1 |
| 55 | 1 | Finance | Full Time | 33 | 3.51 | 3.75 | 10 | 0 |
| 56 | 1 | Marketing | Part Time | 56 | 3.33 | 3.4 | 2 | 1 |
| 57 | 1 | Marketing | Full Time | 39 | 2.81 | 3.05 | 2 | 0 |
| 58 | 1 | Marketing | Full Time | 51 | 3.64 | 3.8 | 8 | 1 |
| 59 | 1 | Leadership | Part Time | 55 | 3.05 | 3.4 | 7 | 0 |
| 60 | 1 | Leadership | Full Time | 38 | 2.85 | 3.25 | 3 | 1 |
| 61 | 1 | Marketing | Full Time | 33 | 3.56 | 3.6 | 7 | 1 |
| 62 | 1 | Marketing | Full Time | 34 | 2.92 | 3.1 | 5 | 0 |
| 63 | 1 | Marketing | Full Time | 31 | 3.35 | 3.5 | 7 | 1 |
| 64 | 1 | Marketing | Full Time | 37 | 3.46 | 3.35 | 10 | 1 |
| 65 | 1 | Marketing | Full Time | 46 | 3.59 | 3.75 | 8 | 1 |
| 66 | 1 | No Major | Unemployed | 31 | 3.11 | 3.2 | 6 | 0 |
| 67 | 1 | No Major | Full Time | 47 | 3.65 | 3.7 | 8 | 1 |
| 68 | 1 | No Major | Part Time | 54 | 3.17 | 3.5 | 7 | 0 |
| 69 | 1 | No Major | Full Time | 52 | 2.97 | 3.1 | 5 | 1 |
| 70 | 1 | Marketing | Part Time | 43 | 3.77 | 3.9 | 8 | 1 |
| 71 | 1 | Leadership | Full Time | 44 | 3.21 | 3.2 | 6 | 1 |
| 72 | 1 | Leadership | Part Time | 34 | 3.17 | 3.15 | 6 | 0 |
| 73 | 1 | Leadership | Full Time | 59 | 3.65 | 3.65 | 10 | 0 |
| 74 | 1 | Leadership | Full Time | 45 | 2.94 | 3.1 | 5 | 0 |
| 75 | 1 | Leadership | Full Time | 30 | 3.53 | 3.7 | 8 | 1 |
| 76 | 1 | No Major | Full Time | 32 | 3.65 | 3.6 | 7 | 1 |
| 77 | 1 | Leadership | Full Time | 32 | 3.61 | 3.7 | 8 | 1 |
| 78 | 1 | No Major | Full Time | 40 | 3.7 | 3.9 | 8 | 1 |
| 79 | 1 | Leadership | Full Time | 48 | 2.91 | 3.1 | 5 | 1 |
| 80 | 1 | Leadership | Unemployed | 51 | 3.09 | 3.25 | 6 | 0 |
| 81 | 1 | Leadership | Full Time | 30 | 3.77 | 3.95 | 9 | 1 |
| 82 | 1 | Leadership | Full Time | 31 | 3.79 | 3.8 | 8 | 1 |
| 83 | 1 | Leadership | Full Time | 35 | 3.59 | 3.6 | 7 | ) |
| 84 | 1 | Leadership | Full Time | 33 | 3.38 | 3.5 | 8 | 1 |
| 85 | 1 | No Major | Full Time | 35 | 4 | 3.5 | 8 | 1 |
| 86 | 1 | Marketing | Full Time | 31 | 2.97 | 3.1 | 8 | 0 |
| 87 | 1 | Marketing | Full Time | 38 | 3.44 | 3.65 | 8 | 1 |
| 88 | 1 | No Major | Part Time | 46 | 3.64 | 3.55 | 8 | 1 |
| 89 | 1 | Finance | Full Time | 45 | 3.48 | 3.4 | 8 | 1 |
| 90 | 1 | Finance | Full Time | 59 | 2.76 | 3.1 | 8 | 1 |
| 91 | 1 | Finance | Full Time | 58 | 3.73 | 3.8 | 8 | 1 |
| 92 | 1 | Finance | Full Time | 46 | 2.91 | 3.05 | 8 | 1 |
| 93 | 1 | Finance | Full Time | 35 | 3.78 | 3.95 | 9 | 1 |
| 94 | 1 | Finance | Part Time | 53 | 3.5 | 3.4 | 7 | 1 |
| 95 | 1 | Finance | Full Time | 31 | 3.13 | 3.15 | 6 | 1 |
| 96 | 1 | Finance | Full Time | 50 | 3.14 | 3.25 | 6 | 1 |
| 97 | 1 | Finance | Full Time | 38 | 3.24 | 3.3 | 6 | 1 |
| 98 | 1 | Finance | Full Time | 50 | 3.56 | 3.5 | 7 | 1 |
| 99 | 1 | Finance | Full Time | 48 | 3.16 | 3.25 | 6 | 1 |
| 100 | 1 | Finance | Full Time | 53 | 3.53 | 3.55 | 7 | 1 |
| 101 | 0 | No Major | Unemployed | 53 | 3.7 | 3.15 | 6 | 0 |
| 102 | 0 | Marketing | Full Time | 30 | 3.3 | 3.35 | 6 | 1 |
| 103 | 0 | Marketing | Part Time | 32 | 4 | 3.6 | 7 | 0 |
| 104 | 0 | Leadership | Full Time | 42 | 3.5 | 3.4 | 7 | 0 |
| 105 | 0 | Leadership | Full Time | 56 | 3.39 | 3.4 | 7 | 1 |
| 106 | 0 | No Major | Full Time | 46 | 3.65 | 3.8 | 8 | 1 |
| 107 | 0 | Leadership | Full Time | 49 | 2.78 | 3.7 | 8 | 1 |
| 108 | 0 | No Major | Part Time | 32 | 3.44 | 3.6 | 7 | 0 |
| 109 | 0 | No Major | Full Time | 36 | 3.88 | 3.95 | 9 | 1 |
| 110 | 0 | No Major | Full Time | 42 | 2.84 | 3.95 | 9 | 1 |
| 111 | 0 | No Major | Part Time | 37 | 3.53 | 3.6 | 7 | 1 |
| 112 | 0 | No Major | Full Time | 31 | 3.22 | 3.3 | 6 | 0 |
| 113 | 0 | No Major | Full Time | 31 | 3.56 | 3.8 | 8 | 1 |
| 114 | 0 | No Major | Unemployed | 42 | 3.2 | 3.25 | 6 | 1 |
| 115 | 0 | No Major | Full Time | 39 | 3.56 | 3.3 | 6 | 1 |
| 116 | 0 | No Major | Full Time | 47 | 3.41 | 3.6 | 7 | 1 |
| 117 | 0 | Leadership | Part Time | 28 | 3.56 | 3.7 | 8 | 1 |
| 118 | 0 | Leadership | Unemployed | 28 | 3.34 | 3.6 | 7 | 0 |
| 119 | 0 | Leadership | Full Time | 52 | 2.56 | 3.6 | 7 | 1 |
| 120 | 0 | Leadership | Part Time | 35 | 3.76 | 3.8 | 8 | 1 |
| 121 | 1 | Finance | Full Time | 38 | 3.55 | 3.45 | 7 | 1 |
| 122 | 1 | No Major | Full Time | 44 | 3.88 | 3.9 | 8 | 1 |
| 123 | 1 | No Major | Part Time | 38 | 3.31 | 3.45 | 7 | 1 |
| 124 | 1 | Finance | Full Time | 52 | 3.09 | 3.15 | 6 | 1 |
| 125 | 1 | Finance | Unemployed | 53 | 3.82 | 4 | 9 | 0 |
| 126 | 1 | Finance | Part Time | 53 | 3.01 | 3.2 | 6 | 1 |
| 127 | 1 | Finance | Full Time | 31 | 3.66 | 3.85 | 8 | 1 |
| 128 | 1 | Finance | Part Time | 47 | 3.64 | 3.7 | 8 | 1 |
| 129 | 1 | Finance | Full Time | 51 | 3.59 | 3.65 | 7 | 1 |
| 130 | 1 | Finance | Unemployed | 37 | 3.49 | 3.55 | 7 | 1 |
| 131 | 1 | Finance | Part Time | 46 | 3.13 | 3.2 | 6 | 1 |
| 132 | 1 | Finance | Full Time | 48 | 3.83 | 3.9 | 8 | 1 |
| 133 | 1 | Leadership | Full Time | 54 | 3.04 | 3.15 | 6 | 1 |
| 134 | 1 | Leadership | Full Time | 48 | 3.91 | 4 | 10 | 1 |
| 135 | 1 | Leadership | Full Time | 36 | 3.56 | 3.7 | 8 | 1 |
| 136 | 1 | Finance | Unemployed | 39 | 3.96 | 4 | 9 | 1 |
| 137 | 1 | Finance | Full Time | 28 | 3.46 | 3.4 | 7 | 1 |
| 138 | 1 | Finance | Part Time | 45 | 3.22 | 3.15 | 6 | 0 |
| 139 | 1 | Finance | Full Time | 31 | 3.27 | 3.2 | 6 | 0 |
| 140 | 1 | Finance | Full Time | 47 | 3.43 | 3.45 | 7 | 1 |
| 141 | 1 | Finance | Part Time | 35 | 3.85 | 3.95 | 9 | 1 |
| 142 | 1 | Finance | Full Time | 52 | 3.89 | 3.9 | 8 | 1 |
| 143 | 0 | Finance | Part Time | 52 | 3.37 | 3.45 | 7 | 1 |
| 144 | 1 | Finance | Unemployed | 55 | 3.32 | 3.3 | 6 | 0 |
| 145 | 1 | Finance | Full Time | 52 | 3.54 | 3.55 | 7 | 1 |
| 146 | 1 | Finance | Part Time | 46 | 3.8 | 3.9 | 8 | 1 |
| 147 | 1 | Leadership | Full Time | 31 | 3.74 | 3.85 | 8 | 1 |
| 148 | 1 | Leadership | Unemployed | 33 | 3.6 | 3.45 | 7 | 1 |
| 149 | 1 | Leadership | Part Time | 45 | 2.6 | 3.55 | 7 | 1 |
| 150 | 1 | Leadership | Unemployed | 50 | 3.8 | 3.3 | 6 | 1 |
| 151 | 1 | No Major | Part Time | 33 | 2.67 | 3.45 | 7 | 1 |
| 152 | 1 | No Major | Full Time | 37 | 3.95 | 4 | 9 | 1 |
| 153 | 1 | No Major | Unemployed | 33 | 3.56 | 3.75 | 8 | 0 |
| 154 | 1 | Marketing | Full Time | 46 | 3.79 | 3.75 | 8 | 1 |
| 155 | 1 | Marketing | Unemployed | 55 | 3.93 | 4 | 9 | 1 |
| 156 | 1 | Marketing | Full Time | 30 | 3.79 | 3.85 | 8 | 1 |
| 157 | 1 | Marketing | Full Time | 51 | 3.71 | 3.85 | 8 | 1 |
| 158 | 1 | Marketing | Unemployed | 35 | 3.05 | 3.35 | 6 | 1 |
| 159 | 1 | Marketing | Unemployed | 40 | 3.22 | 3.2 | 6 | 1 |
| 160 | 0 | Marketing | Part Time | 29 | 3.85 | 3.95 | 9 | 1 |
| 161 | 1 | Marketing | Full Time | 52 | 3.82 | 3.95 | 9 | 1 |
| 162 | 1 | Marketing | Unemployed | 27 | 3.23 | 3.95 | 9 | 1 |
| 163 | 1 | Marketing | Full Time | 51 | 3.56 | 3.65 | 7 | 1 |
| 164 | 0 | Marketing | Part Time | 56 | 3.53 | 3.65 | 7 | 1 |
| 165 | 1 | Marketing | Unemployed | 35 | 3.62 | 4 | 9 | 1 |
| 166 | 1 | Leadership | Full Time | 46 | 3.8 | 3.95 | 9 | 1 |
| 167 | 1 | Leadership | Part Time | 39 | 3.47 | 3.35 | 6 | 0 |
| 168 | 1 | Leadership | Full Time | 31 | 3.64 | 3.65 | 7 | 1 |
| 169 | 1 | Leadership | Part Time | 52 | 3.03 | 3.15 | 5 | 1 |
| 170 | 1 | Leadership | Unemployed | 35 | 3.17 | 3.25 | 6 | 1 |
| 171 | 1 | Leadership | Full Time | 32 | 3.22 | 3.2 | 6 | 1 |
| 172 | 0 | Leadership | Part Time | 44 | 3.92 | 4 | 10 | 1 |
| 173 | 1 | Leadership | Unemployed | 43 | 3.82 | 3.95 | 9 | 1 |
| 174 | 1 | Leadership | Part Time | 38 | 3.26 | 3.55 | 7 | 1 |
| 175 | 1 | Leadership | Full Time | 54 | 3.8 | 3.85 | 8 | 1 |
| 176 | 1 | Leadership | Full Time | 30 | 3.2 | 3.2 | 6 | 0 |
| 177 | 0 | Leadership | Part Time | 38 | 3.46 | 3.35 | 6 | 1 |
| 178 | 1 | Leadership | Full Time | 45 | 3.67 | 3.75 | 8 | 1 |
| 179 | 1 | Leadership | Unemployed | 48 | 4 | 3.4 | 7 | 0 |
| 180 | 1 | Leadership | Full Time | 43 | 3.66 | 3.85 | 8 | 0 |
| 181 | 0 | Leadership | Full Time | 34 | 3.96 | 4 | 10 | 1 |
| 182 | 1 | Leadership | Full Time | 54 | 3.75 | 3.85 | 8 | 1 |
| 183 | 1 | Leadership | Full Time | 36 | 3.83 | 3.85 | 8 | 1 |
| 184 | 1 | Leadership | Full Time | 45 | 3.55 | 3.2 | 6 | 1 |
| 185 | 0 | Leadership | Unemployed | 55 | 3.36 | 3.35 | 6 | 1 |
| 186 | 1 | Leadership | Part Time | 45 | 3.21 | 3.25 | 6 | 1 |
| 187 | 1 | Leadership | Part Time | 34 | 2.97 | 3.15 | 5 | 1 |
| 188 | 0 | Leadership | Part Time | 54 | 3.99 | 4 | 10 | 1 |
| 189 | 1 | Leadership | Full Time | 36 | 3.07 | 3.15 | 6 | 1 |
| 190 | 1 | Leadership | Full Time | 24 | 3.65 | 3.65 | 7 | 1 |
| 191 | 1 | Leadership | Full Time | 34 | 3.67 | 3.85 | 8 | 1 |
| 192 | 1 | Leadership | Full Time | 45 | 3.06 | 3.35 | 6 | 0 |
| 193 | 1 | Leadership | Unemployed | 33 | 3.98 | 3.7 | 8 | 1 |
| 194 | 1 | Leadership | Full Time | 22 | 3.93 | 4 | 10 | 1 |
| 195 | 1 | Leadership | Unemployed | 27 | 3.41 | 3.3 | 6 | 0 |
| 196 | 1 | Leadership | Unemployed | 33 | 3.43 | 3.5 | 7 | 1 |
| 197 | 1 | Leadership | Unemployed | 36 | 3.7 | 3.65 | 7 | 0 |
| 198 | 1 | Leadership | Unemployed | 34 | 3.76 | 3.75 | 8 | 1 |
| 199 | 1 | Leadership | Unemployed | 55 | 3.9 | 3.9 | 8 | 0 |
| 200 | 1 | Leadership | Full Time | 33 | 3.23 | 3.3 | 6 | 1 |
In: Statistics and Probability
7-4 Dace company manufactures two products, Product F and Product G. The company to produce and sell 3,200 units of Product F and 2,100 units of Product G during the year. Data relating to the company’s three activity cost pools are given below for the year:
|
Activity Cost Pool |
Total Cost |
Product F |
Product G |
Totals |
|
Machine setups.. |
$7020 |
130 setups |
130 setups |
260 setups |
|
Purchase Orders. |
$69,700 |
520 orders |
1,180 orders |
1,700 orders |
|
General Factory.. |
$101,160 |
3,520 hours |
2,100 hours |
5,620 hours |
A. Calculate activity rates for each activity cost pool using activity-based costing:
b. Using the activity-based costing approach, determine the overhead cost per unit for each product.
In: Accounting