| Course | MST-7000 Managerial Statistics |
|---|---|
| Module | Module 1 |
| Paper type | Module 1 written assignment: descriptive statistics |
| Length | About 563 words, 5 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | MBA in Data Analytics |
| Updated | October 2026 |
Free sample paper for MST 7000 Module 1
What Does a Typical Order Look Like? Descriptive Statistics for Sixty Furniture Orders
Student Name
MBA in Data Analytics, Thomas Edison State University
MST-7000: Managerial Statistics
Mentor Name
Month Day, Year
What Does a Typical Order Look Like? Descriptive Statistics for Sixty Furniture Orders
The Question and the Data
The marketing manager asked for one figure to describe a typical order so she could set a free-delivery threshold. I drew a simple random sample of 60 orders from last quarter's 3,412 orders, using Excel's RAND function to shuffle order numbers, and recorded each order's value in dollars before tax and delivery.
Summary Measures
I used Excel's Descriptive Statistics tool from the Data Analysis ToolPak and checked each value with the matching function, such as MEDIAN, STDEV.S and QUARTILE.INC.
Anderson et al. (2011) explain that the mean is pulled toward extreme values while the median is not, and that a positive skewness coefficient indicates a long right tail. Here the mean exceeds the median by about $12, and the skewness of 1.11 confirms a moderate right skew: most orders cluster between $400 and $800, with a few large orders stretching the tail.
| Measure | Value | Excel function |
|---|---|---|
| Mean | $685.53 | AVERAGE |
| Median | $673.50 | MEDIAN |
| Standard deviation | $261.97 | STDEV.S |
| Coefficient of variation | 38.2 percent | STDEV.S / AVERAGE |
| Minimum and maximum | $243 and $1,620 | MIN, MAX |
| First and third quartiles | $498.00 and $786.25 | QUARTILE.INC |
| Skewness | 1.11 | SKEW |
Frequency Distribution
About two in three orders fall between $400 and $799. The histogram built from this table, with Excel's chart tool, shows a single peak and a tail to the right.
| Order value | Orders | Percent |
|---|---|---|
| Under $400 | 6 | 10.0 |
| $400 to $599 | 19 | 31.7 |
| $600 to $799 | 21 | 35.0 |
| $800 to $999 | 7 | 11.7 |
| $1,000 to $1,199 | 3 | 5.0 |
| $1,200 to $1,599 | 3 | 5.0 |
| $1,600 and over | 1 | 1.7 |
Screening for Outliers
I applied two rules. The interquartile range is $288.25, so the upper fence, Q3 plus 1.5 times the IQR, is $1,218.63. Four orders exceed it: $1,228, $1,237, $1,279 and $1,620. Hoaglin et al. (1986) studied this fence rule and found that in samples from symmetric distributions it labels some values as outliers by chance, which argues for checking flagged values rather than deleting them. The z-score rule, which marks any value lying over three standard deviations from the mean, catches only the $1,620 order, with a z-score of 3.57. I checked all four orders against the order system; each is a genuine sale of a sectional sofa or a dining set, so I kept them.
Interpretation for the Manager
The typical order is best described by the median, about $674, because the large orders inflate the mean. Half of all orders fall between $498 and $786. A free-delivery threshold set near $700 would sit just above the typical order and could encourage customers below it to add an item. The coefficient of variation of 38 percent shows that order values vary widely, so a single threshold will affect customers very differently; the marketing team may want to test two thresholds. For delivery planning, the quartiles are more useful than the standard deviation: the warehouse can expect a quarter of orders above $786, most of them sofas and dining sets that need two-person delivery.
Limitations
The sample covers one quarter, which included a holiday sale, and 60 orders is a modest sample. Tukey (1977) recommended exploring data before drawing conclusions, and the next step is to compare these figures with another quarter before setting the threshold. The sample also excludes canceled orders and gift card sales, which do not ship.
References
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2011). Essentials of modern business statistics with Microsoft Excel (5th ed.). South-Western Cengage Learning.
Hoaglin, D. C., Iglewicz, B., & Tukey, J. W. (1986). Performance of some resistant rules for outlier labeling. Journal of the American Statistical Association, 81(396), 991-999. https://doi.org/10.1080/01621459.1986.10478363
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
Reading the MST 7000 Module 1 assignment instructions
MST-7000 sets seven written assignments, together a 35 percent share of the grade, and each asks you to work business problems from the module in Excel. This first one covers descriptive statistics. You will compute center, spread and shape for a data set, chart a frequency distribution, screen for outliers and explain what the figures mean for a decision. Use Excel's functions and the Data Analysis ToolPak, and submit both the workbook and a written explanation. Show which function produced each figure, and interpret every result in plain language for a manager who is not a statistician. Attach the workbook with formulas visible, since mentors check how each figure was produced as well as the figure itself.
How the MST 7000 Module 1 example is put together
The memo opens with the manager's question and how the sample was drawn. A table reports seven summary measures with the Excel function for each, and the text interprets the gap between mean and median and the skewness. A frequency table shows where orders cluster. A section applies the IQR fence rule and the z-score rule, notes what research says about fence rules and explains why the flagged orders were kept. The memo closes with a recommendation for the threshold and the limits of the sample. It also notes what the sample leaves out, canceled orders and gift cards, so the manager knows exactly which orders the figures describe. Every table is followed by a sentence that tells the manager what it means.
Where the marks sit in the MST 7000 Module 1 rubric
Mentors grade written assignments on correct calculations, appropriate use of Excel, accurate interpretation and clear explanation. In this first assignment, choosing the right measure of center for skewed data, and explaining why, shows statistical judgment beyond computation. Treating outlier rules as screening tools, and checking flagged values against the source, demonstrates sound practice. Tying every figure to the manager's decision, here the delivery threshold, is what separates managerial statistics from arithmetic. Mentors also look for correct Excel functions, such as STDEV.S for a sample, and for frequency classes that do not overlap. Tables labeled with units and a one-line reading under each make the work easy to follow.
Common MST 7000 Module 1 mistakes, and how to avoid them
Students lose points by reporting the mean for skewed data without comment, by deleting outliers without checking them, by pasting ToolPak output with no interpretation or by confusing the sample and population standard deviation. Others build frequency tables with overlapping classes. Use STDEV.S for samples. Compare the mean and median and say what the gap means. Check unusual values before deciding what to do with them. Write a sentence of interpretation under every table. State how the sample was drawn and what it leaves out. Round dollar figures sensibly, to the cent for means and to the dollar for ranges, and say what each figure means for the manager's question. A free first sample on your own MST-7000 descriptive statistics assignment is available on request.
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MST 7000 Module 1 questions, answered
Where can I find a free MST 7000 Module 1 sample paper?
Sixty furniture orders summarized with Excel's descriptive statistics, for a manager, are worked in the assignment above.
When is the median better than the mean?
When the data lean to one side or include extreme values, since a few extreme values pull the mean toward them while the median stays put.
What does a positive skewness coefficient mean?
The distribution has a longer tail to the right, with a few large values.
How are outliers identified with the IQR?
Values beyond Q1 minus 1.5 times the IQR or Q3 plus 1.5 times the IQR are flagged for checking.
Which Excel function gives a sample standard deviation?
STDEV.S, which divides by n minus 1.