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In Python fun.txt -0.086107832 0.279109089 1.12109795 0.921406055 0.845488273 1.295830355 1.310718286 1.735717683 1.865533435 2.610081159 2.849604671 2.501217594 2.552051849 2.599851547 3.448864397 3.314477826 3.462990021 4.820334145 2.38704362 2.519236916 2.884221938 2.120354783

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In Python

fun.txt

-0.086107832 0.279109089 1.12109795 0.921406055 0.845488273 1.295830355 1.310718286 1.735717683 1.865533435 2.610081159 2.849604671 2.501217594 2.552051849 2.599851547 3.448864397 3.314477826 3.462990021 4.820334145 2.38704362 2.519236916 2.884221938 2.120354783 2.103660078 2.168028349 1.304734504 1.333069614 1.190773098 1.100845576 0.412218164 0.044849118 0.435212177 -0.475335839 -1.0427608 -1.148755123 -0.789322488 -1.128559146 -1.947876788 -2.000293795 -1.78053997 -2.128684422 -2.91398322 -3.007685556 -3.022193246 -3.017536587 -3.411831943 -3.052920883 -3.285814119 -2.645218976 -3.065402131 -2.261376817 -3.060206429 -2.256282254 -2.540310192 -1.798538927 -1.640145863 -1.572483589 -1.373858901 -0.488396418 -0.706629892 1.683153233 -0.245266444 0.474564321 0.34119352 1.249505367 1.146948 1.03581713 2.002415708 2.444151455 1.967420951 2.049631498 2.337816984 2.364526632 2.908585055 3.224012591 2.548947172 3.16701859 3.319472826 3.202280649 2.736463845 2.897752895 2.190311957 2.587453421 2.246005377 2.387676616 1.581343731 1.572148669 1.465105893 0.549928683 0.429586483 0.618909181 -0.277547909 -0.372770192 -1.077048092 -0.675509271 -1.416872049 -1.22860374 -1.612979311 -2.058432551 -1.762287842 -2.202628723 -2.923986261 -3.142393839 -2.414849717 -2.69695442 -2.95537267 -2.898666643 -2.537817211 -2.862775632 -3.216002968 -2.58933555 -2.815880238 -2.298735685 -1.808861214 -2.167423587 -1.512402912 -1.468894734 -1.45268034 -1.232953153 -0.758424456 0.118859696 0.107672779 0.144173689 0.739753504 1.108951113 0.859633153 1.188718095 1.893614396 2.372081406 2.42840503 2.057077708 2.123026217 2.299399275 2.489076608 2.819082882 2.840049695 3.07635439 3.40638042 3.01163765 2.651726977 2.936212314 3.022897063 2.714540328 2.644577708 1.756317217 1.8399997 1.090421766 1.207182034 1.183251192 1.114367977 0.684738624 0.258787487 -0.596393522 -1.015782548 -0.702496287 -1.113970704 -1.681698197 -2.077699608 -1.771570939 -2.549530022 -2.261819688 -2.540679924 -3.240611064 -2.990254278 -2.604728523 -3.343340239 -3.340188802 -2.51919366 -2.963356372 -2.940989071 -3.002741193 -2.382018091 -2.634997207 -2.353808502 -1.671271368 -2.240351967 -1.342999476 -1.709258424 -0.939303514 -0.714211504 -0.057610383 -0.441101047 0.557866574 0.370487762 0.63756146 1.10112681 1.405801583 1.315267524 2.068405931 2.010596752 2.245235951 2.458484037 2.242434926 2.961870563 2.815104314 2.677253852 3.31924752 2.859216668 2.787000519 2.793594126 3.168977728 2.93071452 2.567055694 2.162006783 2.269629376 1.900953644 1.988977081 1.536911029 1.086993038 0.707372899 -0.163072541 -0.346851269 -0.507388479 -0.207294774 -1.259076188 -0.784279638 -1.961170518 -2.151571909 -2.346731884 -2.250566431 -2.284901586 -2.713131165 -2.612723414 -3.031555518 -3.04361327 -2.596802656 -3.059773031 -3.273965604 -3.322818381 -2.450846004 -2.83078703 -2.945416343 -1.982952625 -2.587208279 -2.469363285 -1.987996689 -1.201873431 -0.800039158 -1.190812624 -0.287866792 -0.58107885 0.397415723 0.01728859 0.974310859 1.380615693 -0.536933295 1.138976865 1.598945405 2.108080787 2.524828821 2.782654938 2.377155371 2.504975548 2.821738411 3.387097816 3.306476283 3.47015979 2.717457005 2.64496722 2.924309432 2.623503224 2.673880881 2.700257196 2.438667781 2.157575665 1.956852676 1.322999483 1.599292499 0.661707176 0.207139227 -0.181909098 0.441215956 -0.479458525 -0.658666111 -0.631218082 -1.12979491 -1.362525415 -2.084721699 -1.74249843 -1.788426984 -2.714613565 -2.492533116 -3.062850012 -2.672618878 -3.381482049 -2.849689887 -2.998510004 -3.475143672 -2.8709518 -2.506696244 -2.435703199 -2.483395289 -2.170722455 -2.728481836 -1.91084109 -1.32421357 -1.500142935 -1.334572068 -0.485567997 -0.735548745 -0.752910005 0.010157038 0.406661342 0.428717578 1.178748031 0.944717333 1.403896162 1.889017098 2.457330888 1.749939676 2.548630221 2.398171161 2.240938199 2.556824475 2.867613549 2.958693261 2.975307016 2.729820671 3.377701715 2.528790366 2.278058264 2.964976602 2.290013425 2.33373761 2.025235576 1.709568727 1.604392312 1.149637793 0.749554943 0.426787533 0.798830894 0.102450928 -0.443020499 -0.730823572 -0.618103114 -1.251162253 -1.62425564 -2.050293693 -1.675761879 -2.178754326 -2.262067125 -2.674074356 -2.668280023 -2.741728719 -2.741499226 -3.176710035 -3.3238527 -3.361571983 -3.122207375 -2.62030088 -2.619785223 -2.657345337 -2.63082391 -2.517771112 -2.146102724 -1.2874226 -1.814362042 -1.663435394 -1.394038361 -0.493627919 -0.310793234
Using the data in the wavy fun.txt file, do the following: 1. Plot the data 2. See if you find the outliers by examination. 3. See if you can find the outliers by using the standard deviation of the point to point difference of the data. When you find them, identify them, replace them with the average of the surrounding points. 4. Smooth the data using a sliding window. Make window sizes of 5,7 , and 9 points. Try simple average, and weighted average techniques. 5. Calculate your error for each of your sliding windows. The equation is 3sin(6x). Which is best

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