Question
Create a detailed flowchart, neatly drawn is fine or you can use software, of the process that you would use to completing a significant case
- Create a detailed flowchart, neatly drawn is fine or you can use software, of the process that you would use to completing a significant case study, report, or class project that was worth a significant proportion of you grade in a class (>25%). Did you learn anything or gain any insights as you created the flow chart?
- Consider the following data compiled over several months from a foreman responsible for quality control from a company that installs home and commercial building exteriors of the errors/problems from installations, create a check sheet for this raw data.
miss cut panel | Open joint | Open joint | Open joint | miss cut panel | Bent trim |
Open seam | Open seam | miss cut panel | Open seam | Miss cut trim | Open seam |
Open joint | Panel Flaw | Miss cut trim | Miss cut trim | miss cut panel | Panel Flaw |
miss cut panel | Bent trim | Exposed wrap | Bent trim | Open joint | miss cut panel |
Panel Flaw | Contamination | Contamination | miss cut panel | Exposed wrap | Bent trim |
Bent trim | Bent trim | Bent trim | miss cut panel | Contamination | miss cut panel |
Open joint | miss cut panel | Miss cut trim | miss cut panel | Miss cut trim | Open joint |
Open joint | Open joint | Open joint | Bent Trim | Open joint | miss cut panel |
Miss cut trim | Open seam | Exposed wrap | Open seam | Panel Flaw | Miss cut trim |
Miss cut trim | Miss cut trim | miss cut panel | miss cut panel | Panel Flaw | miss cut panel |
Open seam | miss cut panel | miss cut panel | Open seam | Miss cut trim | Open seam |
Open joint | Panel Flaw | Miss cut trim | Miss cut trim | miss cut panel | Panel Flaw |
miss cut panel | Open seam | Exposed wrap | Open seam | Panel Flaw | Miss cut trim |
Miss cut trim | Contamination | miss cut panel | miss cut panel | Open joint | miss cut panel |
3. Using the data and check sheet from Question 2, create a Pareto Chart from the data. Based on your Pareto chart, which programs do you think that you should try to correct first, and why?
4. Consider the following data and first create a histogram of the data. Do you have any observations you would like to make about this data from the histogram?
0.49419 | 0.49839 | 0.50992 | 0.48939 | 0.49509 | 0.49119 | 0.49731 | 0.47782 |
0.48744 | 0.48954 | 0.50287 | 0.48671 | 0.49636 | 0.48704 | 0.48698 | 0.49289 |
0.50277 | 0.49298 | 0.49112 | 0.49130 | 0.50512 | 0.49882 | 0.50061 | 0.49989 |
0.49766 | 0.48216 | 0.50570 | 0.47826 | 0.50932 | 0.48107 | 0.51500 | 0.48556 |
0.49504 | 0.50010 | 0.51253 | 0.47786 | 0.49028 | 0.48196 | 0.50146 | 0.49641 |
0.50412 | 0.48901 | 0.50905 | 0.48488 | 0.50108 | 0.48455 | 0.49696 | 0.50062 |
0.51657 | 0.49007 | 0.49381 | 0.49210 | 0.50698 | 0.48643 | 0.51267 | 0.48484 |
0.50875 | 0.48476 | 0.50736 | 0.48159 | 0.50075 | 0.48882 | 0.50382 | 0.49520 |
0.49236 | 0.49669 | 0.51020 | 0.47730 | 0.50903 | 0.49718 | 0.50212 | 0.48465 |
0.49328 | 0.48803 | 0.48705 | 0.50047 | 0.50533 | 0.50188 | 0.51103 | 0.48085 |
5. Create a Cause and Effect (Ishikawa or Fishbone Diagram) for the program of not being able to find a specific file you saved on your laptop a year ago for a class project that you would like to refer to for a problem at work. After you create the diagram, are there an insights you gain to potentially avoid such a problem in the future?
6. Consider the following data, create a scatterplot for both GDP per capita and population and another for GDP per Capita and Population Density and then draw a conclusion about the relationship between the variables.
Entity | GDP per capita, PPP (constant 2017 international $) | Population density | Population (historical estimates) |
Afghanistan | 2058.4 | 54.596 | 35643420 |
Albania | 12770.96 | 105.086 | 2879361 |
Algeria | 11737.4 | 17.272 | 41136548 |
Argentina | 23597.12 | 16.098 | 44054616 |
Armenia | 12115.14 | 100.173 | 2851929 |
Australia | 48398.55 | 3.197 | 24590336 |
Austria | 54172.99 | 106.61 | 8797497 |
Azerbaijan | 14121.41 | 121.852 | 10071569 |
Bangladesh | 4160.703 | 1242.944 | 161793968 |
Brazil | 14524.61 | 24.946 | 208504960 |
Cambodia | 3928.374 | 89.682 | 15830694 |
Canada | 48317.18 | 4.077 | 36554344 |
Chile | 24470.7 | 24.704 | 18368582 |
China | 14243.53 | 149.636 | 1410275968 |
Colombia | 14171.32 | 43.58 | 48351668 |
Croatia | 27154.08 | 74.085 | 4192468 |
Denmark | 55356.68 | 143.432 | 5737286 |
Ecuador | 11617.91 | 67.229 | 16696947 |
Egypt | 11014.49 | 102.255 | 101789384 |
El Salvador | 8454.052 | 302.445 | 6266661 |
Finland | 47570.13 | 18.124 | 5508146 |
France | 44577.07 | 117.146 | 64144092 |
Hungary | 29501.12 | 107.264 | 9788940 |
Iceland | 55638.49 | 3.408 | 343641 |
India | 6182.922 | 455.469 | 1354195712 |
Indonesia | 10935.63 | 140.877 | 264498848 |
Iran | 14535.87 | 51.883 | 84505080 |
Mexico | 19721.26 | 63.191 | 122839256 |
Nepal | 3565.198 | 196.606 | 28183424 |
Netherlands | 55088.63 | 511.054 | 17207204 |
New Zealand | 42285.32 | 18.025 | 4746253 |
Nicaragua | 6004.014 | 53.852 | 6480534 |
Nigeria | 5190.356 | 212.453 | 193495904 |
Pakistan | 4571.205 | 280.692 | 216379648 |
Poland | 30064.5 | 125.854 | 38532816 |
Portugal | 33044.71 | 112.521 | 10307528 |
Romania | 27141.9 | 85.617 | 19698852 |
Russia | 25926.44 | 8.882 | 145452544 |
Rwanda | 1975.249 | 495.758 | 12230343 |
South Korea | 40957.42 | 528.216 | 51511640 |
Spain | 39528.93 | 93.242 | 46584172 |
Sweden | 51947.95 | 24.694 | 10058191 |
Switzerland | 69103.57 | 213.88 | 8451688 |
Tunisia | 11234.49 | 76.026 | 11811441 |
Turkey | 27913.82 | 106.661 | 82089824 |
United Kingdom | 46372.39 | 273.074 | 66064808 |
United States | 60109.66 | 36.053 | 329791232 |
Vietnam | 7155.443 | 303.264 | 94033048 |
Zambia | 3485.022 | 23.269 | 17298056 |
Zimbabwe | 3795.642 | 38.131 | 14751101 |
7. Construct a set of X-bar and R-Charts for the following data and interpret the charts to see whether the process looks like it is in-control.
Sample # | |||||
1 | 5.026927 | 5.005555 | 4.95668 | 4.994999 | 5.026142 |
2 | 5.048867 | 5.052075 | 5.027222 | 4.993921 | 5.018301 |
3 | 4.944734 | 5.011558 | 5.010126 | 4.962489 | 5.022458 |
4 | 5.012786 | 5.037651 | 4.970024 | 4.958413 | 4.933708 |
5 | 4.990558 | 5.011921 | 5.011902 | 4.956729 | 4.990985 |
6 | 4.972817 | 4.979059 | 5.055553 | 4.961528 | 4.917318 |
7 | 5.024894 | 4.978343 | 4.958551 | 4.952427 | 5.003415 |
8 | 4.97988 | 4.945376 | 4.982025 | 5.010008 | 5.017826 |
9 | 4.928019 | 4.981581 | 4.919429 | 4.954324 | 5.000255 |
10 | 5.013187 | 5.021026 | 4.937678 | 4.944423 | 5.015729 |
11 | 5.10605 | 4.96354 | 4.955739 | 5.059814 | 5.009434 |
12 | 5.034777 | 4.955624 | 4.949033 | 5.053548 | 4.951553 |
13 | 5.030386 | 4.991413 | 4.978321 | 5.110369 | 5.037815 |
14 | 5.068179 | 4.973174 | 5.001058 | 4.910348 | 5.041896 |
15 | 4.963863 | 5.050507 | 4.978714 | 5.076796 | 5.041754 |
16 | 4.999933 | 5.017094 | 5.00387 | 5.036427 | 4.971726 |
17 | 5.021264 | 4.993136 | 4.97147 | 5.051446 | 4.968699 |
18 | 4.991311 | 4.93365 | 4.96019 | 5.036548 | 5.106264 |
19 | 4.961819 | 4.983278 | 4.902357 | 5.012143 | 4.992442 |
20 | 5.002846 | 5.044409 | 4.969796 | 4.961295 | 5.004543 |
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