Four variables from a composite birth dataset are each summarized with the matching statistic and graph in this PU525 Unit 2 data description exercise example, missing values reported. Searches like "pu 525 unit 2 assignment example", "pu525 unit 2 sample" and "pu525 unit 2 example" land here.
What a finished PU525 Unit 2 data description exercise looks like
A variable table opens the exercise, listing each field with its measurement level and units: birth weight in grams, gestational age grouped as preterm, term or post-term, maternal smoking during pregnancy as yes or no, and the trimester in which prenatal care began. Birth weight gets a histogram with 250-gram bins and a summary line: mean 3,290 grams, standard deviation 560, median 3,340, slightly left-skewed by preterm births. The three categorical variables get frequency tables with counts and percentages and one bar chart each, ordered categories kept in order. A pair of box plots then compares birth weight by smoking status. Low birth weight, under 2,500 grams, is reported as 8.1 percent with its count. A missing-data line closes each variable's section, noting, for smoking status, that 3 percent of records leave it blank.
How a PU525 Unit 2 example is structured
Measurement level decides everything in the exercise, so the variable table comes first and every later choice refers back to it. Birth weight is the only continuous variable and the only one given a mean and standard deviation; the example explains that a slight left skew makes the median worth reporting beside them. Categorical variables follow in the table's order, each with counts before percentages so a reader can see the base. Ordered categories keep their order in the bar charts, since sorting trimesters by frequency would scramble a sequence that means something. The comparison by smoking comes after all four variables are described individually, because a two-group display only makes sense once each variable is understood alone. Low birth weight is handled last as a derived binary measure, and the exercise states the cut point and its source before reporting the percentage.
Variables and their levels
Four rows naming continuous, ordinal and nominal measures, with units and valid ranges. Two impossible birth weights flagged during checking are listed here along with how each was handled.
Birth weight in grams
Histogram, mean, standard deviation, median, and the lowest and highest tenth. The example notes that preterm births pull the left tail and that a mean alone would understate how many infants sit far below it.
Counts before percentages
Frequency tables for gestational age group, smoking and prenatal care timing, each with a total row. Percentages are calculated on valid responses, and the denominator is printed beneath each table.
Two box plots on one axis
Birth weight among infants of mothers who smoked and did not, drawn on a shared scale. The median gap of about 200 grams is described without being tested, since inference belongs to later units.
Low birth weight as a derived measure
The cut point of 2,500 grams, its origin in World Health Organization definitions, and the resulting 8.1 percent with its count of 186 births.
Where marks go in PU525 Unit 2
Data description rubrics tend to reward correct matching of statistics to variable types, clear graphs, accurate calculation and a written interpretation. The example earns the matching criterion by never computing a mean for a category, which is the error graders in many sections check first. Graph marks depend on labeled axes with units, bins suited to the data, and bar charts for categories instead of histograms. Calculation marks go to percentages with visible denominators. Interpretation marks require sentences that say what the numbers show about the births, not what a histogram is. Frequent deductions include pie charts with a dozen slices, averages reported for trimester codes, missing values silently excluded, standard deviations quoted without units, and graphs pasted from software with default titles. Rounding is kept consistent within each table.
Get a PU525 Unit 2 example written to your instructions
Upload the dataset your PU525 Unit 2 exercise supplies, or describe its variables, with the instructions and the rubric. Free as an opening order and ready inside 24-48 hours, the custom sample classifies every variable by level, pairs it with a suitable graph, prints the denominator under each percentage, and reports missing values openly.
PU525 Unit 2 questions, answered
Is a pie chart acceptable for categorical variables?
It can be for two or three categories, but bar charts are easier to read and compare, which is why most statistics instructors prefer them. For ordered categories such as prenatal care timing, a bar chart also keeps the sequence visible. The example uses bars throughout and would switch only if a prompt specifically asked for a pie chart.
What counts as a missing value, and how should it be reported?
Any blank, refused or out-of-range entry that cannot be used in a calculation. The example reports the number missing for each variable, states the denominator used for percentages, and describes how impossible values were identified. Dropping them silently is what loses marks, because readers cannot tell whether the remaining records still represent the population.
Should the exercise test whether smokers' infants weigh less?
Not usually at this stage. Unit 2 prompts in many PU525 sections ask for description, and a box plot comparison shows the difference without claiming it is statistically significant. The example describes the gap and notes that later units would test it. Adding a hypothesis test before the course introduces one can distract from the description the rubric rewards.