{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":44695,"title":"What score did they give?","description":"Your task in this problem is to figure out the most recent score, |S|, submitted.  \r\n\r\n\u003c\u003chttps://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/Star_rating_4.5_of_5.png/799px-Star_rating_4.5_of_5.png\u003e\u003e\r\n\r\nMany websites allow users to rate things like restaurants, books, films, and even computer programs.  Often these are rated _from one to five stars_, and the display shows the _average_ rating after _rounding to one decimal place_, |R|, and the number of users who have rated the item/place/thing, |N|.  \r\n\r\nThis morning you checked the website and noted |R| and |N|.  This evening you check again:  |N| has increased by one, and the rounded version of the updated overall rating is now displayed, |R_new|.  You then deduce what possible scores, |S|, were submitted by the additional person during the day.  \r\n\r\nEXAMPLE\r\n\r\n % Inputs\r\n N = 11\r\n R = 2.5\r\n R_new = 2.6\r\n % Output\r\n S = [3 4]\r\n\r\nExplanation:  Given |N|=11, the unrounded version of |R| must have been either 2 ⁵/₁₁ or 2 ⁶/₁₁. Increasing |N| by 1 with an additional score of either 4 stars or 3 stars (respectively) would mean that the unrounded version of |R_new| would become 2 ⁷/₁₂, consistent with the displayed value.  \r\n\r\n|S| shall be a row vector, or scalar, or empty.  If |S| is a row vector, scores shall be listed in ascending order, without repetition.  ","description_html":"\u003cp\u003eYour task in this problem is to figure out the most recent score, \u003ctt\u003eS\u003c/tt\u003e, submitted.\u003c/p\u003e\u003cimg src = \"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/Star_rating_4.5_of_5.png/799px-Star_rating_4.5_of_5.png\"\u003e\u003cp\u003eMany websites allow users to rate things like restaurants, books, films, and even computer programs.  Often these are rated \u003ci\u003efrom one to five stars\u003c/i\u003e, and the display shows the \u003ci\u003eaverage\u003c/i\u003e rating after \u003ci\u003erounding to one decimal place\u003c/i\u003e, \u003ctt\u003eR\u003c/tt\u003e, and the number of users who have rated the item/place/thing, \u003ctt\u003eN\u003c/tt\u003e.\u003c/p\u003e\u003cp\u003eThis morning you checked the website and noted \u003ctt\u003eR\u003c/tt\u003e and \u003ctt\u003eN\u003c/tt\u003e.  This evening you check again:  \u003ctt\u003eN\u003c/tt\u003e has increased by one, and the rounded version of the updated overall rating is now displayed, \u003ctt\u003eR_new\u003c/tt\u003e.  You then deduce what possible scores, \u003ctt\u003eS\u003c/tt\u003e, were submitted by the additional person during the day.\u003c/p\u003e\u003cp\u003eEXAMPLE\u003c/p\u003e\u003cpre\u003e % Inputs\r\n N = 11\r\n R = 2.5\r\n R_new = 2.6\r\n % Output\r\n S = [3 4]\u003c/pre\u003e\u003cp\u003eExplanation:  Given \u003ctt\u003eN\u003c/tt\u003e=11, the unrounded version of \u003ctt\u003eR\u003c/tt\u003e must have been either 2 ⁵/₁₁ or 2 ⁶/₁₁. Increasing \u003ctt\u003eN\u003c/tt\u003e by 1 with an additional score of either 4 stars or 3 stars (respectively) would mean that the unrounded version of \u003ctt\u003eR_new\u003c/tt\u003e would become 2 ⁷/₁₂, consistent with the displayed value.\u003c/p\u003e\u003cp\u003e\u003ctt\u003eS\u003c/tt\u003e shall be a row vector, or scalar, or empty.  If \u003ctt\u003eS\u003c/tt\u003e is a row vector, scores shall be listed in ascending order, without repetition.\u003c/p\u003e","function_template":"function S = latestScore(N, R, R_new)\r\n    \r\nend","test_suite":"%% No silly stuff\r\n% This Test Suite can be updated if inappropriate 'hacks' are discovered \r\n% in any submitted solutions, so your submission's status may therefore change over time.  \r\nassessFunctionAbsence({'regexp', 'regexpi', 'str2num'}, 'FileName','latestScore.m')\r\n\r\nRE = regexp(fileread('latestScore.m'), '\\w+', 'match');\r\ntabooWords = {'ans'};\r\ntestResult = cellfun( @(z) ismember(z, tabooWords), RE );\r\nmsg = ['Please do not do that in your code!' char([10 13]) ...\r\n    'Found: ' strjoin(RE(testResult)) '.' char([10 13]) ...\r\n    'Banned word.' char([10 13])];\r\nassert(~any( testResult ), msg)\r\n\r\n\r\n%% N = 11\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.2;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.3;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.4;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [2 3];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.6;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [3 4];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [4 5];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.8;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.9;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n\r\n\r\n%% N = 12\r\nN = 12;\r\nR = 2.3;\r\nR_new = 2.2;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 12;\r\nR = 2.3;\r\nR_new = 2.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [4 5];\r\nassert(isequal(S, S_correct))\r\n\r\n\r\n\r\n%% N = 17\r\nN = 17;\r\nR = 1.6;\r\nR_new = 1.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [2 3 4];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 2.6;\r\nR_new = 2.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 2.6;\r\nR_new = 2.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [3 4 5];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 2.6;\r\nR_new = 2.8;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 3.4;\r\nR_new = 3.2;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 3.4;\r\nR_new = 3.3;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2 3];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 3.4;\r\nR_new = 3.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n\r\n%% N = 48\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.5;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n\r\n%%\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.6;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.8;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.2;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:4;\r\nassert( isempty(S) )\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.3;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:4;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.4;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.5;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-07-09T03:13:05.000Z","updated_at":"2025-09-14T14:31:48.000Z","published_at":"2018-07-09T04:06:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task in this problem is to figure out the most recent score,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, submitted.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMany websites allow users to rate things like restaurants, books, films, and even computer programs. Often these are rated\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efrom one to five stars\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and the display shows the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eaverage\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e rating after\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003erounding to one decimal place\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and the number of users who have rated the item/place/thing,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis morning you checked the website and noted\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This evening you check again: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e has increased by one, and the rounded version of the updated overall rating is now displayed,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR_new\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You then deduce what possible scores,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, were submitted by the additional person during the day.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % Inputs\\n N = 11\\n R = 2.5\\n R_new = 2.6\\n % Output\\n S = [3 4]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExplanation: Given\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=11, the unrounded version of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e must have been either 2 ⁵/₁₁ or 2 ⁶/₁₁. Increasing\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by 1 with an additional score of either 4 stars or 3 stars (respectively) would mean that the unrounded version of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR_new\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e would become 2 ⁷/₁₂, consistent with the displayed value.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e shall be a row vector, or scalar, or empty. If\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a row vector, scores shall be listed in ascending order, without repetition.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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\"}]}"},{"id":44545,"title":"\"Percentages may not total 100 due to rounding\"","description":"*Percentages* are commonly *rounded* when presented in tables.  As a result, the sum of the individual numbers does not always add up to 100%.  A warning is therefore sometimes appended to such tables, along the lines: _\"Percentages may not total 100 due to rounding\"_.\r\n\r\nEXAMPLE 1:\r\nA survey of eleven people for their opinion on a new policy found five to be in favour, five opposed, and one undecided/neutral.  Percentage-wise this becomes\r\n\r\n  In favour:          45% (5 of 11)\r\n  Undecided/neutral:   9% (1 of 11)  \r\n  Opposed:            45% (5 of 11)  \r\n\r\nThe total of these is 99%, rather than the expected 100%.  Despite this conflict, in this example *all of the individual numbers have been correctly entered*.  \r\n\r\nEXAMPLE 2:\r\nIn the same report, a survey of a further ten people on a different policy found four to be in favour, four opposed, and two undecided/neutral.  Suppose the data were presented in the following table\r\n\r\n  In favour:          45%\r\n  Undecided/neutral:  20%\r\n  Opposed:            45%\r\n\r\nGiven the background information, we would quickly recognise this as inconsistent, with a spurious total of 110%, rather than the expected 100%.  In fact, we would probably guess that a copy-and-paste mistake had occurred.  However, it is important to realise that even if we looked at this table alone, in isolation, without any background knowledge of the survey size or the true number of responses in any category, just by knowing that there are only three categories we can firmly conclude that in this example *one or more of the individual numbers must have been entered incorrectly*.  \r\n\r\nYOUR JOB:  \r\nGiven a list (vector) of integer percentages, determine whether among the individual values at least one of them _must_ have been incorrectly entered (return |true|), as in Example 2, or whether there might not be any incorrect entries (return |false|), as in Example 1.","description_html":"\u003cp\u003e\u003cb\u003ePercentages\u003c/b\u003e are commonly \u003cb\u003erounded\u003c/b\u003e when presented in tables.  As a result, the sum of the individual numbers does not always add up to 100%.  A warning is therefore sometimes appended to such tables, along the lines: \u003ci\u003e\"Percentages may not total 100 due to rounding\"\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eEXAMPLE 1:\r\nA survey of eleven people for their opinion on a new policy found five to be in favour, five opposed, and one undecided/neutral.  Percentage-wise this becomes\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eIn favour:          45% (5 of 11)\r\nUndecided/neutral:   9% (1 of 11)  \r\nOpposed:            45% (5 of 11)  \r\n\u003c/pre\u003e\u003cp\u003eThe total of these is 99%, rather than the expected 100%.  Despite this conflict, in this example \u003cb\u003eall of the individual numbers have been correctly entered\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eEXAMPLE 2:\r\nIn the same report, a survey of a further ten people on a different policy found four to be in favour, four opposed, and two undecided/neutral.  Suppose the data were presented in the following table\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eIn favour:          45%\r\nUndecided/neutral:  20%\r\nOpposed:            45%\r\n\u003c/pre\u003e\u003cp\u003eGiven the background information, we would quickly recognise this as inconsistent, with a spurious total of 110%, rather than the expected 100%.  In fact, we would probably guess that a copy-and-paste mistake had occurred.  However, it is important to realise that even if we looked at this table alone, in isolation, without any background knowledge of the survey size or the true number of responses in any category, just by knowing that there are only three categories we can firmly conclude that in this example \u003cb\u003eone or more of the individual numbers must have been entered incorrectly\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eYOUR JOB:  \r\nGiven a list (vector) of integer percentages, determine whether among the individual values at least one of them \u003ci\u003emust\u003c/i\u003e have been incorrectly entered (return \u003ctt\u003etrue\u003c/tt\u003e), as in Example 2, or whether there might not be any incorrect entries (return \u003ctt\u003efalse\u003c/tt\u003e), as in Example 1.\u003c/p\u003e","function_template":"% \"May not sum to total due to rounding: the probability of rounding errors\"\r\n% Henry Bottomley, 03 June 2008\r\n% http://www.se16.info/hgb/rounding.pdf\r\n\r\n% \"How to make rounded percentages add up to 100%\"\r\n% https://stackoverflow.com/questions/13483430/how-to-make-rounded-percentages-add-up-to-100\r\n% cf. https://stackoverflow.com/questions/5227215/how-to-deal-with-the-sum-of-rounded-percentage-not-being-100\r\nfunction containsMistake = checkForMistake(z)\r\n    containsMistake = true * + false,\r\nend","test_suite":"%% Basics\r\nassessFunctionAbsence({'regexp', 'regexpi'}, 'FileName','checkForMistake.m')\r\n\r\n%% Example 1 (Row vector)\r\nxVec = round( 100*[5 1 5]/11 );\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) )\r\n\r\n%% Example 1 (Column vector)\r\nxVec = round( 100*[5 1 5]/11 )';\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) )\r\n\r\n%% Example 2 (Row vector)\r\nxVec = [42 20 45];\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) )\r\n\r\n%% One percentage, over and under\r\nxVec = [100];\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test a')\r\nxVec = round([100.5]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test b')\r\nxVec = round([99.49]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test c')\r\n\r\n%% Two percentages, over and under\r\nxVec = [50 50];\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test a')\r\nxVec = round([49.5 50.5]);\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test b')\r\nxVec = round([50.5 50.5]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test c')\r\nxVec = round([49.49 50.49]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test d')\r\n\r\n%% Equal percentages\r\nfor j = [2:250 1000:1000:10000]\r\n    xVec = round( repelem(100/j, j) );\r\n    containsMistake = false;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Geometric series (from Bottomley, §5)\r\nfor j = 10:30\r\n    xVec = round( 10 * (9/10).^[0:j] );\r\n    containsMistake = j \u003c 21;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Geometric series (from Bottomley, §5), permuted\r\nfor j = 2:40\r\n    xVec = round( 10 * (9/10).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 21;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) I\r\nfor j = 2:100\r\n    xVec = round( (99/100).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 66;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) II\r\nfor j = 2:30\r\n    xVec = round( 20 * (4/5).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 12;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) III\r\nfor j = 2:20\r\n    xVec = round( 25 * (3/4).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 10;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) IV\r\nfor j = 2:20\r\n    xVec = round( 50 * (1/2).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 5;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Systematic overestimation\r\nfor j = 2:100\r\n    num = randi(round(100/j)+1) - 1;\r\n    xRaw = repelem(num+0.5, j);   % cf. https://oletus.github.io/float16-simulator.js/\r\n    sm = sum(xRaw);\r\n    xRaw = [xRaw max(100-sm, 0)];\r\n    if sm \u003e 100.5,                % Not sm\u003e100, because need to account for extra zero added.\r\n        containsMistake = true;\r\n    else\r\n        containsMistake = false;\r\n    end;\r\n    xVec = round( xRaw );\r\n    xVec = xVec( randperm(j+1) );\r\n    assert( isequal(checkForMistake(xVec), containsMistake) , ['Failed with xRaw = ' num2str(xRaw)] )\r\nend;\r\n\r\n%% Systematic underestimation\r\nfor j = 2:100\r\n    num = randi(round(100/j)+1) - 1;\r\n    xRaw = repelem(num+0.499755859375, j);   % cf. https://oletus.github.io/float16-simulator.js/ \u0026 https://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html\r\n    cs = cumsum(xRaw);\r\n    if cs(end) \u003e 100,\r\n        xRaw( cs \u003e 100 ) = [];\r\n        containsMistake = true;\r\n    else\r\n        xRaw = [xRaw (100-cs(end))];\r\n        containsMistake = false;\r\n    end;\r\n    xVec = round( xRaw );\r\n    assert( isequal(checkForMistake(xVec), containsMistake) , ['Failed with xRaw = ' num2str(xRaw)] )\r\nend;","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-03-23T08:02:20.000Z","updated_at":"2018-03-24T13:53:40.000Z","published_at":"2018-03-24T13:53:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePercentages\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e are commonly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003erounded\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e when presented in tables. As a result, the sum of the individual numbers does not always add up to 100%. A warning is therefore sometimes appended to such tables, along the lines:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Percentages may not total 100 due to rounding\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 1: A survey of eleven people for their opinion on a new policy found five to be in favour, five opposed, and one undecided/neutral. Percentage-wise this becomes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[In favour:          45% (5 of 11)\\nUndecided/neutral:   9% (1 of 11)  \\nOpposed:            45% (5 of 11)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe total of these is 99%, rather than the expected 100%. Despite this conflict, in this example\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eall of the individual numbers have been correctly entered\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 2: In the same report, a survey of a further ten people on a different policy found four to be in favour, four opposed, and two undecided/neutral. Suppose the data were presented in the following table\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[In favour:          45%\\nUndecided/neutral:  20%\\nOpposed:            45%]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the background information, we would quickly recognise this as inconsistent, with a spurious total of 110%, rather than the expected 100%. In fact, we would probably guess that a copy-and-paste mistake had occurred. However, it is important to realise that even if we looked at this table alone, in isolation, without any background knowledge of the survey size or the true number of responses in any category, just by knowing that there are only three categories we can firmly conclude that in this example\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eone or more of the individual numbers must have been entered incorrectly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYOUR JOB: Given a list (vector) of integer percentages, determine whether among the individual values at least one of them\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emust\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e have been incorrectly entered (return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etrue\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), as in Example 2, or whether there might not be any incorrect entries (return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efalse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e), as in Example 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":44695,"title":"What score did they give?","description":"Your task in this problem is to figure out the most recent score, |S|, submitted.  \r\n\r\n\u003c\u003chttps://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/Star_rating_4.5_of_5.png/799px-Star_rating_4.5_of_5.png\u003e\u003e\r\n\r\nMany websites allow users to rate things like restaurants, books, films, and even computer programs.  Often these are rated _from one to five stars_, and the display shows the _average_ rating after _rounding to one decimal place_, |R|, and the number of users who have rated the item/place/thing, |N|.  \r\n\r\nThis morning you checked the website and noted |R| and |N|.  This evening you check again:  |N| has increased by one, and the rounded version of the updated overall rating is now displayed, |R_new|.  You then deduce what possible scores, |S|, were submitted by the additional person during the day.  \r\n\r\nEXAMPLE\r\n\r\n % Inputs\r\n N = 11\r\n R = 2.5\r\n R_new = 2.6\r\n % Output\r\n S = [3 4]\r\n\r\nExplanation:  Given |N|=11, the unrounded version of |R| must have been either 2 ⁵/₁₁ or 2 ⁶/₁₁. Increasing |N| by 1 with an additional score of either 4 stars or 3 stars (respectively) would mean that the unrounded version of |R_new| would become 2 ⁷/₁₂, consistent with the displayed value.  \r\n\r\n|S| shall be a row vector, or scalar, or empty.  If |S| is a row vector, scores shall be listed in ascending order, without repetition.  ","description_html":"\u003cp\u003eYour task in this problem is to figure out the most recent score, \u003ctt\u003eS\u003c/tt\u003e, submitted.\u003c/p\u003e\u003cimg src = \"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b9/Star_rating_4.5_of_5.png/799px-Star_rating_4.5_of_5.png\"\u003e\u003cp\u003eMany websites allow users to rate things like restaurants, books, films, and even computer programs.  Often these are rated \u003ci\u003efrom one to five stars\u003c/i\u003e, and the display shows the \u003ci\u003eaverage\u003c/i\u003e rating after \u003ci\u003erounding to one decimal place\u003c/i\u003e, \u003ctt\u003eR\u003c/tt\u003e, and the number of users who have rated the item/place/thing, \u003ctt\u003eN\u003c/tt\u003e.\u003c/p\u003e\u003cp\u003eThis morning you checked the website and noted \u003ctt\u003eR\u003c/tt\u003e and \u003ctt\u003eN\u003c/tt\u003e.  This evening you check again:  \u003ctt\u003eN\u003c/tt\u003e has increased by one, and the rounded version of the updated overall rating is now displayed, \u003ctt\u003eR_new\u003c/tt\u003e.  You then deduce what possible scores, \u003ctt\u003eS\u003c/tt\u003e, were submitted by the additional person during the day.\u003c/p\u003e\u003cp\u003eEXAMPLE\u003c/p\u003e\u003cpre\u003e % Inputs\r\n N = 11\r\n R = 2.5\r\n R_new = 2.6\r\n % Output\r\n S = [3 4]\u003c/pre\u003e\u003cp\u003eExplanation:  Given \u003ctt\u003eN\u003c/tt\u003e=11, the unrounded version of \u003ctt\u003eR\u003c/tt\u003e must have been either 2 ⁵/₁₁ or 2 ⁶/₁₁. Increasing \u003ctt\u003eN\u003c/tt\u003e by 1 with an additional score of either 4 stars or 3 stars (respectively) would mean that the unrounded version of \u003ctt\u003eR_new\u003c/tt\u003e would become 2 ⁷/₁₂, consistent with the displayed value.\u003c/p\u003e\u003cp\u003e\u003ctt\u003eS\u003c/tt\u003e shall be a row vector, or scalar, or empty.  If \u003ctt\u003eS\u003c/tt\u003e is a row vector, scores shall be listed in ascending order, without repetition.\u003c/p\u003e","function_template":"function S = latestScore(N, R, R_new)\r\n    \r\nend","test_suite":"%% No silly stuff\r\n% This Test Suite can be updated if inappropriate 'hacks' are discovered \r\n% in any submitted solutions, so your submission's status may therefore change over time.  \r\nassessFunctionAbsence({'regexp', 'regexpi', 'str2num'}, 'FileName','latestScore.m')\r\n\r\nRE = regexp(fileread('latestScore.m'), '\\w+', 'match');\r\ntabooWords = {'ans'};\r\ntestResult = cellfun( @(z) ismember(z, tabooWords), RE );\r\nmsg = ['Please do not do that in your code!' char([10 13]) ...\r\n    'Found: ' strjoin(RE(testResult)) '.' char([10 13]) ...\r\n    'Banned word.' char([10 13])];\r\nassert(~any( testResult ), msg)\r\n\r\n\r\n%% N = 11\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.2;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.3;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.4;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [2 3];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.6;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [3 4];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [4 5];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.8;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 11;\r\nR = 2.5;\r\nR_new = 2.9;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n\r\n\r\n%% N = 12\r\nN = 12;\r\nR = 2.3;\r\nR_new = 2.2;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 12;\r\nR = 2.3;\r\nR_new = 2.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [4 5];\r\nassert(isequal(S, S_correct))\r\n\r\n\r\n\r\n%% N = 17\r\nN = 17;\r\nR = 1.6;\r\nR_new = 1.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [2 3 4];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 2.6;\r\nR_new = 2.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 2.6;\r\nR_new = 2.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [3 4 5];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 2.6;\r\nR_new = 2.8;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 3.4;\r\nR_new = 3.2;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 3.4;\r\nR_new = 3.3;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2 3];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 17;\r\nR = 3.4;\r\nR_new = 3.5;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n\r\n%% N = 48\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.5;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n\r\n%%\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.6;\r\nS = latestScore(N, R, R_new);\r\nS_correct = [1 2];\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.7;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 3.7;\r\nR_new = 3.8;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.2;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:4;\r\nassert( isempty(S) )\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.3;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:4;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.4;\r\nS = latestScore(N, R, R_new);\r\nS_correct = 1:5;\r\nassert(isequal(S, S_correct))\r\n\r\n%%\r\nN = 48;\r\nR = 4.4;\r\nR_new = 4.5;\r\nS = latestScore(N, R, R_new);\r\nassert( isempty(S) )\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-07-09T03:13:05.000Z","updated_at":"2025-09-14T14:31:48.000Z","published_at":"2018-07-09T04:06:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour task in this problem is to figure out the most recent score,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, submitted.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMany websites allow users to rate things like restaurants, books, films, and even computer programs. Often these are rated\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efrom one to five stars\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and the display shows the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eaverage\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e rating after\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003erounding to one decimal place\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and the number of users who have rated the item/place/thing,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis morning you checked the website and noted\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This evening you check again: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e has increased by one, and the rounded version of the updated overall rating is now displayed,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR_new\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. You then deduce what possible scores,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, were submitted by the additional person during the day.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ % Inputs\\n N = 11\\n R = 2.5\\n R_new = 2.6\\n % Output\\n S = [3 4]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExplanation: Given\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=11, the unrounded version of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e must have been either 2 ⁵/₁₁ or 2 ⁶/₁₁. Increasing\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by 1 with an additional score of either 4 stars or 3 stars (respectively) would mean that the unrounded version of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR_new\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e would become 2 ⁷/₁₂, consistent with the displayed value.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eS\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e shall be a row vector, or scalar, or empty. 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\"}]}"},{"id":44545,"title":"\"Percentages may not total 100 due to rounding\"","description":"*Percentages* are commonly *rounded* when presented in tables.  As a result, the sum of the individual numbers does not always add up to 100%.  A warning is therefore sometimes appended to such tables, along the lines: _\"Percentages may not total 100 due to rounding\"_.\r\n\r\nEXAMPLE 1:\r\nA survey of eleven people for their opinion on a new policy found five to be in favour, five opposed, and one undecided/neutral.  Percentage-wise this becomes\r\n\r\n  In favour:          45% (5 of 11)\r\n  Undecided/neutral:   9% (1 of 11)  \r\n  Opposed:            45% (5 of 11)  \r\n\r\nThe total of these is 99%, rather than the expected 100%.  Despite this conflict, in this example *all of the individual numbers have been correctly entered*.  \r\n\r\nEXAMPLE 2:\r\nIn the same report, a survey of a further ten people on a different policy found four to be in favour, four opposed, and two undecided/neutral.  Suppose the data were presented in the following table\r\n\r\n  In favour:          45%\r\n  Undecided/neutral:  20%\r\n  Opposed:            45%\r\n\r\nGiven the background information, we would quickly recognise this as inconsistent, with a spurious total of 110%, rather than the expected 100%.  In fact, we would probably guess that a copy-and-paste mistake had occurred.  However, it is important to realise that even if we looked at this table alone, in isolation, without any background knowledge of the survey size or the true number of responses in any category, just by knowing that there are only three categories we can firmly conclude that in this example *one or more of the individual numbers must have been entered incorrectly*.  \r\n\r\nYOUR JOB:  \r\nGiven a list (vector) of integer percentages, determine whether among the individual values at least one of them _must_ have been incorrectly entered (return |true|), as in Example 2, or whether there might not be any incorrect entries (return |false|), as in Example 1.","description_html":"\u003cp\u003e\u003cb\u003ePercentages\u003c/b\u003e are commonly \u003cb\u003erounded\u003c/b\u003e when presented in tables.  As a result, the sum of the individual numbers does not always add up to 100%.  A warning is therefore sometimes appended to such tables, along the lines: \u003ci\u003e\"Percentages may not total 100 due to rounding\"\u003c/i\u003e.\u003c/p\u003e\u003cp\u003eEXAMPLE 1:\r\nA survey of eleven people for their opinion on a new policy found five to be in favour, five opposed, and one undecided/neutral.  Percentage-wise this becomes\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eIn favour:          45% (5 of 11)\r\nUndecided/neutral:   9% (1 of 11)  \r\nOpposed:            45% (5 of 11)  \r\n\u003c/pre\u003e\u003cp\u003eThe total of these is 99%, rather than the expected 100%.  Despite this conflict, in this example \u003cb\u003eall of the individual numbers have been correctly entered\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eEXAMPLE 2:\r\nIn the same report, a survey of a further ten people on a different policy found four to be in favour, four opposed, and two undecided/neutral.  Suppose the data were presented in the following table\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eIn favour:          45%\r\nUndecided/neutral:  20%\r\nOpposed:            45%\r\n\u003c/pre\u003e\u003cp\u003eGiven the background information, we would quickly recognise this as inconsistent, with a spurious total of 110%, rather than the expected 100%.  In fact, we would probably guess that a copy-and-paste mistake had occurred.  However, it is important to realise that even if we looked at this table alone, in isolation, without any background knowledge of the survey size or the true number of responses in any category, just by knowing that there are only three categories we can firmly conclude that in this example \u003cb\u003eone or more of the individual numbers must have been entered incorrectly\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eYOUR JOB:  \r\nGiven a list (vector) of integer percentages, determine whether among the individual values at least one of them \u003ci\u003emust\u003c/i\u003e have been incorrectly entered (return \u003ctt\u003etrue\u003c/tt\u003e), as in Example 2, or whether there might not be any incorrect entries (return \u003ctt\u003efalse\u003c/tt\u003e), as in Example 1.\u003c/p\u003e","function_template":"% \"May not sum to total due to rounding: the probability of rounding errors\"\r\n% Henry Bottomley, 03 June 2008\r\n% http://www.se16.info/hgb/rounding.pdf\r\n\r\n% \"How to make rounded percentages add up to 100%\"\r\n% https://stackoverflow.com/questions/13483430/how-to-make-rounded-percentages-add-up-to-100\r\n% cf. https://stackoverflow.com/questions/5227215/how-to-deal-with-the-sum-of-rounded-percentage-not-being-100\r\nfunction containsMistake = checkForMistake(z)\r\n    containsMistake = true * + false,\r\nend","test_suite":"%% Basics\r\nassessFunctionAbsence({'regexp', 'regexpi'}, 'FileName','checkForMistake.m')\r\n\r\n%% Example 1 (Row vector)\r\nxVec = round( 100*[5 1 5]/11 );\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) )\r\n\r\n%% Example 1 (Column vector)\r\nxVec = round( 100*[5 1 5]/11 )';\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) )\r\n\r\n%% Example 2 (Row vector)\r\nxVec = [42 20 45];\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) )\r\n\r\n%% One percentage, over and under\r\nxVec = [100];\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test a')\r\nxVec = round([100.5]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test b')\r\nxVec = round([99.49]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test c')\r\n\r\n%% Two percentages, over and under\r\nxVec = [50 50];\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test a')\r\nxVec = round([49.5 50.5]);\r\ncontainsMistake = false;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test b')\r\nxVec = round([50.5 50.5]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test c')\r\nxVec = round([49.49 50.49]);\r\ncontainsMistake = true;\r\nassert( isequal(checkForMistake(xVec), containsMistake) , 'Failed test d')\r\n\r\n%% Equal percentages\r\nfor j = [2:250 1000:1000:10000]\r\n    xVec = round( repelem(100/j, j) );\r\n    containsMistake = false;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Geometric series (from Bottomley, §5)\r\nfor j = 10:30\r\n    xVec = round( 10 * (9/10).^[0:j] );\r\n    containsMistake = j \u003c 21;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Geometric series (from Bottomley, §5), permuted\r\nfor j = 2:40\r\n    xVec = round( 10 * (9/10).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 21;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) I\r\nfor j = 2:100\r\n    xVec = round( (99/100).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 66;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) II\r\nfor j = 2:30\r\n    xVec = round( 20 * (4/5).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 12;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) III\r\nfor j = 2:20\r\n    xVec = round( 25 * (3/4).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 10;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Other geometric series (permuted) IV\r\nfor j = 2:20\r\n    xVec = round( 50 * (1/2).^[0:j] );\r\n    xVec = xVec( randperm(j+1) );\r\n    containsMistake = j \u003c 5;\r\n    assert( isequal(checkForMistake(xVec), containsMistake) )\r\nend;\r\n\r\n%% Systematic overestimation\r\nfor j = 2:100\r\n    num = randi(round(100/j)+1) - 1;\r\n    xRaw = repelem(num+0.5, j);   % cf. https://oletus.github.io/float16-simulator.js/\r\n    sm = sum(xRaw);\r\n    xRaw = [xRaw max(100-sm, 0)];\r\n    if sm \u003e 100.5,                % Not sm\u003e100, because need to account for extra zero added.\r\n        containsMistake = true;\r\n    else\r\n        containsMistake = false;\r\n    end;\r\n    xVec = round( xRaw );\r\n    xVec = xVec( randperm(j+1) );\r\n    assert( isequal(checkForMistake(xVec), containsMistake) , ['Failed with xRaw = ' num2str(xRaw)] )\r\nend;\r\n\r\n%% Systematic underestimation\r\nfor j = 2:100\r\n    num = randi(round(100/j)+1) - 1;\r\n    xRaw = repelem(num+0.499755859375, j);   % cf. https://oletus.github.io/float16-simulator.js/ \u0026 https://au.mathworks.com/help/matlab/matlab_prog/floating-point-numbers.html\r\n    cs = cumsum(xRaw);\r\n    if cs(end) \u003e 100,\r\n        xRaw( cs \u003e 100 ) = [];\r\n        containsMistake = true;\r\n    else\r\n        xRaw = [xRaw (100-cs(end))];\r\n        containsMistake = false;\r\n    end;\r\n    xVec = round( xRaw );\r\n    assert( isequal(checkForMistake(xVec), containsMistake) , ['Failed with xRaw = ' num2str(xRaw)] )\r\nend;","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":64439,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-03-23T08:02:20.000Z","updated_at":"2018-03-24T13:53:40.000Z","published_at":"2018-03-24T13:53:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePercentages\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e are commonly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003erounded\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e when presented in tables. As a result, the sum of the individual numbers does not always add up to 100%. A warning is therefore sometimes appended to such tables, along the lines:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\\\"Percentages may not total 100 due to rounding\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 1: A survey of eleven people for their opinion on a new policy found five to be in favour, five opposed, and one undecided/neutral. Percentage-wise this becomes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[In favour:          45% (5 of 11)\\nUndecided/neutral:   9% (1 of 11)  \\nOpposed:            45% (5 of 11)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe total of these is 99%, rather than the expected 100%. Despite this conflict, in this example\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eall of the individual numbers have been correctly entered\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEXAMPLE 2: In the same report, a survey of a further ten people on a different policy found four to be in favour, four opposed, and two undecided/neutral. Suppose the data were presented in the following table\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[In favour:          45%\\nUndecided/neutral:  20%\\nOpposed:            45%]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the background information, we would quickly recognise this as inconsistent, with a spurious total of 110%, rather than the expected 100%. In fact, we would probably guess that a copy-and-paste mistake had occurred. However, it is important to realise that even if we looked at this table alone, in isolation, without any background knowledge of the survey size or the true number of responses in any category, just by knowing that there are only three categories we can firmly conclude that in this example\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eone or more of the individual numbers must have been entered incorrectly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYOUR JOB: Given a list (vector) of integer percentages, determine whether among the individual values at least one of them\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 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