Built as a p chart with day-by-day limits for 25 production days, this MT475 Unit 4 analysis finds one spike, one suspicious run and a stable rate beneath both. Searches like "mt 475 unit 4 assignment example", "mt475 unit 4 sample" and "mt475 unit 4 example" land here.
What a finished MT475 Unit 4 control chart analysis looks like
Five pages: a data table, the chart, the limit calculation and two pages of interpretation. The table lists 25 days of air-and-water test results, 15,857 windows tested and 309 failures, with daily volumes from 590 to 672. The overall proportion is 1.95 percent. Because volume varies, limits are recalculated each day; at 640 windows they fall near 0.31 and 3.59 percent. Day 11 plots above its upper limit at 5.24 percent. Days 17 through 24 sit below the center line, eight in a row, averaging about 1.2 percent. Revised limits, built from the sixteen days with neither signal, center on 2.10 percent, with limits near 0.40 and 3.80 percent at typical volume. The verdict closes the paper: stable at about 2.1 percent once the two causes are removed, and too high.
How a MT475 Unit 4 example is structured
The choice of chart is justified before any limit appears: each window passes or fails and daily counts vary, so a p chart with variable limits fits, while an np chart would need constant volume. The formula is printed with one day worked by hand, and a short script computes the rest. Interpretation takes the two signals separately. Day 11 is traced to one lot of insulated glass with a thin secondary seal, an assignable cause outside the plant. The run is treated with suspicion. It coincides with a substitute operator on the test rig who ran a shorter cycle, so the drop reflects a measurement change rather than better windows. Both are removed and limits recalculated. The final paragraph keeps stability and adequacy apart, since a predictable 2.1 percent failure rate is still a system result.
Why a p chart
Pass-or-fail data with daily volumes between 590 and 672 calls for a proportion chart whose limits move with each day's sample. The paper explains why an np chart would mislead here.
One day worked by hand
At 649 windows, the day 11 limits come to about 0.32 and 3.58 percent. Showing that arithmetic once lets a reader trust the script's output for the other twenty-four days.
A lot of glass on day 11
The spike traces to one supplier lot of insulated glass whose secondary seal measured thin. Because the cause came through receiving, the fix belongs with incoming controls rather than the line.
Eight good days, one question
Days 17 through 24 fall below center, a run the chart flags as a signal. The paper asks who ran the test those days, and finds a substitute operator using a shorter cycle.
Stable and still too high
With both causes removed, the process runs predictably near 2.1 percent. Predictable is not acceptable, and the paper says the remaining failures belong to the system, not to any single day.
Where marks go in MT475 Unit 4
Control chart work in MT475 turns on picking a chart that fits the data and reading it honestly. Using an X-bar chart for pass-or-fail data, or an np chart when daily volume varies, undermines every limit, and graders check the choice before the arithmetic. Limits that stay flat while sample size changes are a common construction error on p charts. Interpretation is where most of the credit sits. Treating a run below the center line as proof of improvement, without asking whether measurement changed, is a trap many prompts set on purpose. Each signal should be tied to a plausible assignable cause from the case, and revised limits should exclude exactly those points. Stopping at in control or out of control, without asking whether the stable level is acceptable, leaves the analysis one step short.
Get a MT475 Unit 4 example written to your instructions
Supply the Unit 4 dataset, including daily counts and sample sizes, plus the chart type if your prompt names one and the rubric. The composite analysis returned justifies the chart, computes limits by script, traces every signal to an assignable cause, revises the limits and judges whether the stable level is good enough. First custom sample free, in 24-48h.
MT475 Unit 4 questions, answered
When should an MT475 analysis use a p chart instead of a c chart?
A p chart tracks the proportion of units that are defective, where each unit either passes or fails, such as windows failing a water test. A c chart counts defects on a unit or area of constant size, where one item can carry several flaws, such as blemishes per door. If the inspected area or quantity varies, a u chart replaces the c chart.
Why do the limits on a p chart change from day to day?
Because the standard error of a proportion depends on sample size. Days with more units inspected have tighter limits, and days with fewer have wider ones. Some textbooks allow a single set of limits based on average sample size when volumes differ little, but calculating each day's limits is more accurate and often expected in your course.
Is a run below the center line a good sign?
It is a signal that something changed, and the change needs explaining before anyone celebrates. It may be a real improvement, or it may be a measurement change: a new inspector, a shorter test, a gauge drifting. Most rules flag a run of seven to nine points on one side, depending on the textbook's version.