Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information about a random sample of 15 McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. We wish to use the total fat content (in grams) to better understand the number of calories in the lunch/dinner menu items at McDonald's. Computer output from a regression analysis and a scatterplot (with regression line) of the data are provided.
Use two decimal places when reporting the results from any calculations, unless otherwise specified.
The regression equation is Calories = 137.1 + 15.06 Total Fat (g)
Predictor | Coef | SE Coef | T | P |
Constant | 137.08 | 40.64 | 3.37 | 0.005 |
Total Fat (g) | 15.055 | 1.649 | 9.13 | 0.000 |
S = 62.7442 R-Sq = 86.5% R-Sq(adj) = 85.5%
Predicted Values for New Observations
Total Fat (g) | Fit | SE Fit | 95% CI | 95% PI |
25 | 513.5 | 16.7 | (477.4, 549.5) | (373.2, 653.7) |
![A scatterplot with a regression line shows the relationship between Total fat and Calories. The horizontal axis is labeled, Total fat (grams) and has markings from 10 to 40 in increments of 5. The vertical axis is labeled, Calories and has markings from 200 to 800 in increments of 100. A regression line starts from (9, 285), increases toward the right, and ends at (40, 720). The dots are randomly scattered, such that a few dots lie above the regression line, a few dots lie below the regression line, and a few dots lie on the regression line. The concentration of the dots is more between the points 8 and 27 on the horizontal axis and between the points, 240 and 540 on the vertical axis. The dots are plotted as follows: (8, 240), (8, 350), (12, 300), (16, 490), (18, 290), (19, 380), (19, 400), (23, 410), (23, 540), (26, 500), (37, 620), (39, 760), and (40, 770). All values are approximate.](data:image/png;base64, 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CiiigAooooAKKKKACiiigAooooAKKKKACiikoAWvJf2hf2aPAv7Tnhey0Hxxp8t1BZXS3drcWsphuIH/AIgrj+F1+Vl+h6hSPWqQkY60AY/hrw9pvg/w/puhaNZxafpGm28dpaWkK7UhhRQqIv0ArWJwM14t8U/2qfBXwy1OHQoZbrxh4xukY2vhrw1D9svJDx98LxEvzD5nI4z1xXHS/D74t/tDXJ/4T+/f4YeBiDnwx4cv9+pXnznAubtOEQpwUi6/3q9SlltRwVbEP2cH1e7/AMMd3+Xdozc1ey1Z1PxA/ax8I+FfEDeFvDkN58QvHHzf8U94XT7XLDhlVvPkHyQAFhnewr5V8EftW+KP2uP2gtb+C+teIbj4TwWomha08Llbi61CWH/j4h+3Mv7nbsflE5wRu/vfdHw3+E/hH4Q6CNG8HaBZ6Bp28ytFbJ80jnALux5ZuByxPQUaP8IPBfhzx7rXjbTPDenWPizWY0i1DWIIFWe4Vexb8Bn+9tXOcVs8bh8N7uDp6r7UrN/JfDH8Wu4crfxMpfCr4JeC/gvpMlh4R0GDTFmdnuLpiZbm5ckktLM+XkOSfvE16B0GcUoAxQeK8arVq1pupVk5N9Xqy0klZDqKKKQwooooAKKKKACiiigAooooAKKKKACiiigArz74/wD/ACQn4i87f+Ke1D5v+3Z69Brz79oHH/CiPiNkZX/hHNR/9JnoW4I3Ph3/AMk98L8Y/wCJZbf+iVrpa5z4c5/4V/4ayct/Zlt/6KWujqVsB5J8Ef8AkoPx3/7HaH/0waPRR8Ef+Sg/Hf8A7HaH/wBMGj0VQCeHv+Tr/iB/2JPhz/0v1yvWXz2xnHGa8n8P/wDJ2Hj7/sSfDv8A6X63XrdDA/Nb9q3Utbk8V2d58VtH8DeFNTmsZbFUtPiHeac+s6cHZ/s91HHb5kg3M3ynuz89a/QnwXPHc+ENDmt47WKF7KFo47KTfAq7FwI27p6H0r4q+PWiW3xr8NfFn41+Fr/VdC1Hwbb3PhdC9lHcC6h095/tlxCjMPvJd3cQDY+aIN6V9nfDoWS+APDY026kvNOGm2wtrif78kXlrtY+5FC+EmTvIZ4w+GfhP4iLZDxR4a0rxELJ2e1/tS0S48liNrMm8HGRVv8A4Qfw9/wkw8Sf2Hp/9vi3+zf2n9lT7R5P9zfjO32rfooKMHwz4N0HwVa3Ft4f0aw0W2nma4mi0+2SFZJD1dgo5J9a4P4H4/4Sb4vYH/M4v6f8+FlXrVeTfBD/AJGf4v8A/Y4v/wCkFlQB6wOlLSDpS0AFFFFABRRRQAUUUUAFfGv7anw2/aR8Z/FX4b33wb8UHSPC1tPGNQt4bnyBBcCQs1xcrkfaINm1fK55U/L8+a+yqKAILcSJDGsrK8oUbmQYDHvgVPRSUALRSUEjFADe1HWvF/it+1H4O+F2qp4eg+1+L/G1yP8AR/C3huH7XesdyrmTHywr868yFeM4ziuQvfCnx6+OEEcWt6vZfBvwzOH86y8Pz/bNaZD0Q3BXy4Tj+KPd1r1KeW1XFVa7VOD2cuvot36pW8zPnWy1O2+MP7S/hX4Sapb6EUvfEvjS+j82x8LaFbtc3tyPn+baOETMb5c9MVwkXwo+LXx7tIbn4l+Jpvh74dnUO/g3whcbZ5AT9y5vcb+mAVi2jrXq3wq+Bfg34LWd4nhnSfJvb+Vp9Q1O6ka4vr2Uszb5p3y7nLt1OBuNWfi78bfBHwI8ORa9488Q23h3SpbhLaKWfczSyseFREDO3qcLwASeBW312jhFy4KPvfzySb/7dWqj+L81sPlb+Il+G/wd8F/CHSU07wd4a07QbcRpG7WkCrLNtUKGkk+9I2B95iTXcEYHSqGjarY+IdJtNT0y6hv9OvIkuLa5t3DxzRsMq6kdQQa0COa8apUqVZOdSTbfV6spJLYdRRRSGFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQA3+GuA/aAYr8CviMQAzf8I7qGB/27PXf/AMNef/tBZ/4UT8RtvX/hHdQ/9JnoW41ub3w6x/wr/wAMben9mW2P+/K10Y6Vzfw4x/wgHhjHH/Ertv8A0SldIOlMhbHkvwR/5KD8d/8Asdof/TBo9FJ8E/8AkoHx3/7HaH/0waPRSKF8P/8AJ2Hj7/sSfDv/AKX63XrdeSeH/wDk7Dx9/wBiT4d/9L9br1ugD5t8V/sV6B4h1XxNFZ+LvEvh/wAGeJ7ttQ13wdpc8cVjfzv/AK5yxTzEEu1d6o4DfN619DafYW+mWFtZ2sSw2tvGsUMSfdRFGFA/CrlFHSwCZxR0r56/bC+MfiD4X/D+5tvDEGqWWr3tjdTJr9rodzqcGnCFA3zrDG4V33YVnwgwxJ+WvN/EP7RfirRdI8QeOrLULq8tNK1lPDkGg3lr5KzRnR4rz7TKhVZEm81953cCNWG3+KpTvcLM+zcV5P8ABD/kZ/i//wBji/8A6QWVcl8IviLrem+KvFmieIdXuPEtjYeGtJ8VR3EVoZbuNrxrzzoFSFcuAbZWRQu7a+Pm61sfs3a/aeK7j4oavYreRW154td0j1Gxmsrgf6BZ8PDMiSJ/wIcj5hwaolO57aOlLRRQUFFFFABRRRQAUUUUAFFJmgnAoAb1pB6nGBXjHxS/an8HfDfVU8PWpu/GXjWfi38MeGo/td4TuVf3u35IF+deZGXvjOK4yX4e/Gv47zCXxn4hHwm8JPtJ8N+GLlZtSuEycrPeYxHlSAViz/vV6lLLZuKrYmSpQezlu/8ADHd+trd2jNy6LU674w/te/C34ILewa/4mt7jWrNN8miaV/pd6PlD/NEn3Plbdl8DHNeB/B342+Jv+Ch0OvjQvFEfw68B6ZcR22oaTpTl9fuEdSVLzY2QRvtYApuY7H5XFfTPgf8AZ2+GPwu8O3mmaJ4Q0mwsbi0e1vp5oVkluoSvzieZ8u4I67zWP+zJ4A+DXgXwhq6/BVdFl0O6v5Ptt1o199t3XA/geXezfIG4TPyg8datYrD4SV8FG7/mkk3fulql8+Z+Ycsn8R03wo+CPgr4JaQ2meDvD9tpMcjFp7j5pLmdiS2ZJny8nJ/iJxXoWSP8KCBmlNeVVq1K03UqycpPdvVlpJKyHV4j+09+yh4O/av8Kadovi+S+tn025+02d9p0wSWEtgOvKspV1XByO3GK9uoqRmB4I8H6V8PfB+i+GNEhe20fSLOKxs4XkZ2SGNAiLubk8LW/RRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV59+0FhvgN8Rt33f+Ec1DP/AIDPXoNefftA/wDJCPiPxu/4p3UP/SZ6AN34d5/4QHw1u6/2Zbf+ilrpK5v4ef8AIg+Ge/8AxLLb/wBFLXSUkSeSfBH/AJKD8d/+x2h/9MGj0UnwT/5KB8d/+x2h/wDTBo9FMoXw/wD8nYePv+xJ8O/+l+t163Xknh//AJOw8ff9iT4d/wDS/W69boAKKKKAMrxFoFj4o0K/0jUoBdaffwPb3EJYr5kbjDDI56VzqfCDwjH4q/4SIaPEdT+zi1JZ3aEgJ5e8xE7DJ5f7vzdu/Z8m7bxXb0UAcf4D+GPhv4aRX0Xh3SxYm9k82eUyPLK+OETe5Y7EHyomdqLwoArmPggxbxP8Xs5+Xxe45Of+YfY16vXknwNYt4k+Lozkjxg/f/pwsfc0Aet0Ug6V8i+Bv+CgGleN/wBsTWfgdB4Q1S3FlNc2UesMeZLmDcZS8X8EOEfa+47vlOBmgD67opKM0ALRRSZoAapFBPBJFeNfFv8Aab8LfCzV4vDdvBfeMfHV0nmW3hXw7D9pvHXKLvkx8sKfODvkK8BsZxXFT/Df4y/HgpJ468RD4YeFJgpk8MeFbjzb+ZfmylxebfkyGAKxZHH3q9Sll03BVsRJU4PZvd/4Y7v1280ZufRas9B+LH7Sfgn4Q31ro+pXs+q+Kb1d1j4a0WFrvUbrkj5YU6DhvmbA+U+lebnwj8ZP2jrh38XXd78G/ArxBU0HRLxJNYvsnnz7kDEAwB8kfzfMfmr134X/AAD8B/B1Lh/Cnh220+8u3d7rUZC1xeTszFj5lxIWkfk/xNXonrWyxuHwa/2OneX88rN/9ux1S+fM+qaE4uXxM4L4V/BXwX8F9Hk03wZoFto0Uh3TTLl57hsk7pJXy8h5PLE13vQZxSgDFBrxqtWpWm6tWTk31erNEklZHO/ELwRpvxL8C6/4T1nzf7K1uxm0+68h9j+VIhRtp7HDV4v+x9+xt4e/Y+0DxDY6LrOoeILvW7pJrq6vsRqEjDCJFRflGN75bq2fQAV9GUUhhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXn37QOf+FEfEbb97/hHdQ/9Jnr0GvPf2gsf8KH+I2en/COaj2/6dnoBG98O8f8ACv8AwuQMf8Sy2/8ARK10TVz3w7z/AMID4az1/sy2z/36WuhagSPJfgn/AMlA+O//AGO0P/pg0eij4J/8lA+O/wD2O0P/AKYNHooGL4f/AOTsPH3/AGJPh3/0v1uvW68j0D/k7Dx9/wBiT4c/9L9br1ygAooooASk4ryn9obxz4g+GfgC78U6NqOjWNrpkbzXSatbyytcnjyoYtjLtLt8uTn7wrzjxD+1Rq3hUa5r+oaNap4Z0LVP+EfvLNG3XbXv9mpeeYkobZ5e+RYcY3fxe1K4WfQ+ncjNeTfA1s+JPi9zk/8ACYP3/wCnCx9zVD4Y/GLVdV1vxBo/jKHT7O40/QtP8TC708OsK2d39pAjdXZm3xtayZbowZeF6Ufs3+I9N8W3fxS1fR72PUdLuvF0jQXMWdj4sbNWxn/aBpkpp7HtI6VgW3gnw9Z+J7jxLBoWmxeIrmBbafV1tEF3JGv3UaXG9h7E10FIaChgPynjFA456V5h8U/2h/Anwcjih8Qa0j61Ov8AoehWCtc6jeNnAEUCZc5bjOMepryxrT4zftK2cDXoufgj4FnCStbwSCXxDfRsp4ZvuWi8pxzJww+WvToZdVqQ9tWap0/5pdf8K3l8l62M3NbR1Z6T8W/2l/BHwi1C00fUbyfVvFd6P9C8NaJA15qF11+7En3R8rfM+B8przuPw/8AHL4+O7eIr3/hS/guaJP+JXo8q3Wt3IIfeHuMbIP4eEDN/umtTxVZfCj9g74Qa745g8OGKO0A+0Xcam51LUJ5HCqHuHy7F3YZZm2iu7/Z1+PWg/tJfCnS/HXh2K5tLO7Lwy2t2m2SCZDh0J6Ng/xDg10fXMNg1bBwvL+aSTfyjql8+Z9mhcrl8RL8JPgH4K+CNhcw+FdHW2u7s77zU7iRp7y7fu8sz5ZiTzjpknivShwDxS4o/CvHrVqlebq1pOUn1erNEklZDqKKKgYUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFACDpXn37QX/JCfiNgZ/4pzUP/SZ69BHSvP8A9oHP/CifiLg4P/CO6h/6TPQNbm78O/8Akn/hfnP/ABLLb/0StdGOlc58O+fh/wCGCOP+JZbf+iVrox0oIWx5J8E/+SgfHf8A7HaH/wBMGj0UfBP/AJKB8d/+x2h/9MGj0UFC+H/+TsPH3/Yk+Hf/AEv1uvW68k8P/wDJ2Hj7/sSfDv8A6X63XrdABRRRQBwnxL+HEvxIsI9Ok8Rano+nSwTWl9a2Hl7LyGQAOrb1JBwDtYdMmubf9mnwbJqk5eCeTQ7hluJdBaT/AEV7kWy2nnt/GX8hFTlu2772TXr9FKwHnvw6+Eul/Dy9v72C6vtV1K9trawkvtQkV3W1t/M+zwLgKAiebL2yS7E1j/BFy3ib4u8jC+MHG0dv9AsvevWc15L8DW3eJPi7znHjB++f+XCx9zTJSsesketePfFv4W+PfiXrttZ6b8RZfBvgpoFW9t9Isl/tK4fL71W5dmEaFWT7qbuOtewj1r5f8Jft8eBvF/7VmqfBG00/U11Ozeazj1h4sW815CC00Oz76hdrgOeGKHHBVm2oVp4aftIWv5pP87jeuh6d8Kf2b/h38GZZ7vwv4cgtdTu/+PnVLh3uby4bqzPNIWcktknnrXqXbpQcUvQUq1etiJ+0rScn3buwSSVkYPjHwhovxA8Naj4d8Q6bb6to2oRGG5srpN6SIfb+vY1F4H8EaH8N/Cum+G/DWl2+jaHp8flWtjapsSNevHqSclieWJJNdJRWQwooooAKKKKACvJf2oPiT4r+EXwN8UeK/BHhs+KfEOnQCSGxJO1E3ASTFRy4RMvsHJxXrVIRmgDwX9i34xeNvjr8BdH8WePvDP8Awjms3LssTKjJHqMG1Sl2kZ+ZEfccA9du4fKy173TEUKNoxgdBT6ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvPv2gsf8KH+I2en/COah2/6dnr0GvPv2gf+SEfEfjd/xTuof+kz0Ab3w6z/AMIB4az97+zLb/0UtdHXNfDz/kn/AIY53f8AEstv/RSV0tBB5H8E/wDkoHx3/wCx2h/9MGj0UvwS/wCSg/Hb/sdYf/TBo9FBYeH/APk7Dx9/2JPh3/0v1uvW68k8P/8AJ2Hj7/sSfDv/AKX63XrdABRRSGgAAozivM/jf418S/DzwVe+JdEt9GlsdJtpr7Uf7WuXiLRxjd5cW0Y3vhhljwcda881T9q99In1nWbvw80Xg3Sr1dHupC7fbkvTp6Xzfutu3YBKkPXO/J+7SuCWtj6QryT4GsW8SfF0ZyR4wfv/ANOFj7mk+FXxg1Dxb4g1fw/4k0u20fVrLSbDX0ltLnzLd7K8acRZLBSJENs6v/D90hucBPgRKlxr/wAWpIZUnifxe5SSN94I+wWXfJpkHrg6Vw9j8HfBWnfE29+IFt4X02Dxne2wtJ9ajgAuJI/Qt68KC3UhQOgruaKCwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArz79oHP/CiPiNg7T/wjuof+kz16DXn37QWP+FD/ABGz0/4RzUe3/Ts9AG78Osf8K/8ADBxj/iWW3/ola6Suc+HX/Ig+Gs/e/sy2/wDRS10dQB5J8Ev+Sg/Hb/sdYf8A0waPRR8Ev+Sg/Hb/ALHWH/0waPRVgJoH/J2Hj7/sSfDn/pfrdeuV5JoH/J1/j/8A7Enw5/6X63XrdABRRRQB5j8YfhDd/FcaBHH4pu9CstMvPtk9jFaw3EF+648pZlcfMiMN23ucZ6Vzep/st6F4g1LUjq+q3l9pGr3X9q6lpCRxxQ3OpGzW0a73Ku9cxpnYGxv+avcqKVgPLfAnwM0vwrc67d6rfXHiy91iyttLnbVYo9n2C383yLbylUIQvny5YjLb+aw9G/Z61DwTea5H4E8ayeCdA1S+/tBdE03RLL7PaOYY4mEXycA+Vv8Aqxr26imB5Q3wv+IP/RYNT/8ABJY//EUN8L/iD/0WDU//AASWP/xFer1FIGZGCELJjgkZANAHiFx4F+LEPjLS9Pt/iXqEugz2N1PeakdHsN0FykkAhiVdvR1eYn5T/quq553P+FYfEEH/AJLDqZ/7glj/APEV454b8e+NPE3hzwT4d1bxNeJP4h8d+JdF1HXtOiS3lS2spdRaGKL5WEefs0S/3tqkZLV7V+zd441b4kfAvwX4m15EXWNQ05Jrl402LK/TzFHYPjd+NT0uK/QhX4X/ABBX/msOpt/3BLH/AOIpB8LviEP+aw6mf+4JY/8AxFerjpS1Qzyf/hV/xB4/4vDqf/gksf8A4il/4Vf8Qf8AosOp/wDglsf/AIivVu1ch8Vp9Ytfht4ouPD2oQ6XrcGmzy2d7cQeckMqxsVdkyM1LdlcaV3Y85i8C/FyTxnc6c3xJv08NJp8U8Wqf2TYeZJdF3V4SNu4KqKjZ29XPzcYre/4Vf8AEHn/AIvDqf8A4JLH/wCIrxXxtrHjh/hz8LviRd67f3fg6x8L2134lt9M1lrC+knk+zOLpEWFln2hZf3bOincetfXVrKs8EciksjqCCRgmqatchO55efhd8Qj/wA1h1Mf9wSx/wDiKVvhf8QW/wCaw6mv/cEsf/iK9XooKPKD8L/iDn/ksOp/+CSx/wDiKT/hV3xD/wCiw6n/AOCSy/8AiK9YrzH4/XWm6d8Nr+81fxDrXh61gYOkmgTrFe3U2CIreLgl3dyu1B947R0zUt2BK5zOneBPi3deKNbs734l31rolskDaffrpFgz3TuCZgy7eNhCgfKv3u9bn/CrviH/ANFh1P8A8Ell/wDEV4KPif8AErSQbnxLqstl4o8J6f4Vt7nSYceTqNzqEmy88xFHznjClflVom2/xV9omqtYlO55SPhf8Qc/8lh1P/wSWP8A8RQvwv8AiCv/ADWHU2/7glj/APEV6vRQUeTj4XfEIf8ANYdTP/cEsf8A4ilX4X/EHj/i8Opn/uCWP/xFer14p+1Ddy6b4A+1WWv67pmv7nt9D0/QJQk2pahIu23jZdp3oG+Y9AF3MelAFLwZ4I+LusaPcz+IPiRfaHfJqN7bw20WlWEu+0jupEtZiwU/PJAsTn3f7oxiug/4Vf8AEHn/AIvDqf8A4JLH/wCIrzjHj3wZ8b9F1bxzfXV3oHiG8stL0uPSdbZY7G5/s8GRLix8pUdDcRXB81JGb96nygLx9P0xXPJz8LviEf8AmsOpj/uCWP8A8RSt8L/iC3/NYdTX/uCWP/xFer0UhnlB+F/xBz/yWHU//BJY/wDxFIPhj8QQNzfF7VJMdl0axXP/AI5XrFfPn7Q11rPhPxV4T8U6XrPiGzgXU7CyvvIuIW0eztzcqHa5t8+c5mEvlKUDYKqzbAM0dQLfgPwR8XNe8F6HqXib4kX3h7xFdWkc2oaVFpNhIlrMQpeNWCndg5H3j1rfX4YfEJSM/GDU26f8wSx/2f8AY9j/AN9V5l8AfiZ4n1zxn8P5dW1aW8HjvwpqXiPUtLnKsmm3MFzZRxJCv3o49ly6FT94xZ+9ur6moA8mX4YfEJSM/GDU2PGf+JJY/wCz/sex/wC+qE+F/wAQVIB+MGpNjH/MEsf9n/Y9j/31XrNFAHkw+GHxCyM/GDU+2f8AiS2X+z/sex/76rH8V+Avizo/hm/vNA+Jl7retQx5trGXSLBEmf5eCxVcfxfxd69wNfN3xfufEHgv40+DNQsNe1+1sfEGpf2Vcz3d1C2iQ77SQQ2iwLmYTvMiOsu3aNzbnPCVN+g0d0vwv+IWFz8YNSzjn/iSWP8A8RQPhd8Qh/zWHUz/ANwSx/8AiK4j9mCbxP4e8R+KfBPj271G98dafY6fqN5dtrj6np9zDP8AaESaDfFE0DtJbzb4SmAAm1mzX0fVCPJ/+FX/ABB4/wCLw6n/AOCSx/8AiKP+FXfEP/osOp/+CSy/+Ir1iigDygfC/wCIOf8AksOp/wDgksf/AIisXxP4E+K+ladFNovxKv8AWbxrmCJ4ZtIsEVYXlVZZPur8yJuYe/8AC3SvcCcCviL4s/tJ+OdB+LN+9vHrmg2VtoevJaaFdaJJ9n32slskWoS3BTZIreZIQqOyIpi34Z2FTu7AfQv/AAq/4grx/wALg1L7u3/kC2Ptz9z6/nWd4n+CXjXxb4Z1vQL/AOLWpmy1azmsp9ujWasqSJsbB2ddpNR/ALX9YPi34peEL/VL7xBY+FtYtLex1XUCrzOs+n21zJE7hQHKSSu3sJVX+EV7dVNWJTuZ2iaTFomjWGnQs7wWcCW6NIfmYIoAJ9+K0aKKCjyT4Kf8lA+O3/Y6w/8Apg0eij4Kf8lA+O3/AGOsP/pg0eigA0D/AJOv8f8A/Yk+HP8A0v1uvW68j8P/APJ1/j7/ALErw5/6X63XrlABRRRQAUUUUAFFFFABUFxAlzA8UgOx1KHBxwanopWA8w0j9nn4faP4X1Tw7b+HETR9SuRe3Nq88jqbjqZVZmyjlsksuCSxJ616FY6fb6VZW1naQx29pbxrFDDEu1I0A2qoHYAVdopgFFFFABWL4q8L6X4z0G90TWbUXml30flXEDOyean93KkGtqigDzGD9nP4eW9t4ftx4bha20DcunRSyyOkSmTzNjAt86B+VV9wXtivTqKKACiiigArk/Hnw28NfE7T7Ox8TaTHqkFncpe2u8sj286ghZEdCGVhuPIPeusooA4ey+EHhKwvNDul0iOa80aLybK5uZHmlRMlhuZ2JcqWYqXztLNjGa6+a9t7Z0WWaOIvwiu4GfpVgnFfJH/BTHw/bXv7LniDV001rjWbG4sktbu2jdriJHvIPMVNnzYZcg8ijqEUtj6wtruG8hEsEqTRN0dG3A1NuHrXwf8AGL45eM/gv4913wz4Q8jw3pvh2y0GTwv4Pt9NSQeKzd3ey82uVLp5fzJ8hBB+Y1x/gj4y+Lvhtp3xH02/8ba4vim6+LV9pEFrqMtsltY20gR4Z7iaaGU21q2Ww6Ruh2qqINxNH9fjYaV/68rn6JHVrMaitgbuAXzJ5i2plHmlfXZ1x71yHxF+Gfgr4l3OmHxPZQXl9prSfYJhcvBPbvImx9joysCysB+NfGH7M/xC8QfGb49fAvxv4oEc/iC88Ca7BeXdrbbIpzBqhgR+ny7lTf0X73GM7a2fhdpGo3v7WH7TmsvY2OoWWg6hpt6n2vTZb28jxZSv/oK7wgk3IvGOTt9qLd+zf3OxPVpeX4q59aaX8FPBek+K4PEtrokZ1qGKOKO6mlkk2bIhCrAOSN/lqqb/ALxHeu1vb62021luru4itraJd0ks7hEQerE9K/Lj4i/tNfFD4hfDf4j6TYa/qL2H/CGWHiywvLGSF9UsX/tWOF4ZmtoYhBJ5Lb3h/eMmzPm/MQPoj9qe+tPFnwZ+Bul3niI+KPBniXxlo1lr2rukfl6jaHe/70oqoiPMkQbp6USi0k+7X46D6/f+H/APsG3u4by3SeCVJYZEDrIh3BgehB9Kg0fWLLXLJLzT723vrRiyrPbSiRGIbDAMOOCCK+HfG37RXj/wz8bZtBGv2nhuyi+I2jeEdP8ABTafbsLnSJocveByu8+aW+XaVVNu3k16V+yha/8ACN/HX9pDwpodrHbeBtM8Q2Fzp0NuuLeG8uLJHvokxwNr7CUH3S3vRGN1dbf8N/mTJ2dv66/5H1TXD6z8H/B/iDxza+L9R0KG88Q2iwiG7kd8L5TO0RZN2wshlcqxXIzxXcUUFHKaB8OfDfhXWdU1bR9HtbLUtRffdXMSYd8nJAP8Kk/NtXAyWbqxNdXRRQAUUUUAFefv8EfBEvji68XPoEMmvXbFpriR3ZGYwiFn8sts3mNQm7GcV6BRQBx3gH4W+GPhla3kPhnSY9NF9KstzLveSSYgbV3O5ZiFHCrnAHSuxoooAKKKKACsHVfCmia5qdvqF/pdte3ttbT2UU08Qdkhn2edHz/C/lpuHfaK3qKAOb8G+BdD8A6M2m6DpsWm2jSeY6x5LSPgLvdjy5wqjJ7Ko6CukoooAKKKKAPJPgp/yUD47f8AY6w/+mDR6Kd8E/8AkoPx2/7HWH/0waPRQAzw/wD8nX+Pv+xK8Of+l+t165Xi/iXw38RdE+NGt+MPB+ieF9f07V9A0zSZItb12506aCa1udQlJAjs7gOjLep3UgoeKvf8JF8df+hA+Hn/AIXF/wD/ACnoA9aoryT/AISH46/9CD8PP/C4v/8A5T0f8JB8dv8AoQfh3/4XF/8A/KegD1uivJP+Eg+O3/Qg/Dv/AMLi/wD/AJT0f8JB8dv+hB+Hf/hcX/8A8p6APW6K8k/4SD47f9CD8O//AAuL/wD+U9H/AAkHx2/6EH4d/wDhcX//AMp6APW6K8k/4SD47f8AQg/Dv/wuL/8A+U9H/CQfHb/oQfh3/wCFxf8A/wAp6APW6K8k/wCEg+O3/Qg/Dv8A8Li//wDlPR/wkHx2/wChB+Hf/hcX/wD8p6APW6K8k/4SD47f9CD8O/8AwuL/AP8AlPR/wkHx2/6EH4d/+Fxf/wDynoA9boryT/hIPjt/0IPw7/8AC4v/AP5T0f8ACQfHb/oQfh3/AOFxf/8AynoA9boryT/hIPjt/wBCD8O//C4v/wD5T0f8JB8dv+hB+Hf/AIXF/wD/ACnoA9boryT/AISD47f9CD8O/wDwuL//AOU9H/CQfHb/AKEH4d/+Fxf/APynoA9boryT/hIPjt/0IPw7/wDC4v8A/wCU9H/CQfHb/oQfh3/4XF//APKegD1umOoYYOGHoa8n/wCEh+Ov/Qg/Dz/wuL//AOU9H/CQ/HX/AKEH4ef+Fxf/APynoA9WaNWIYqCw6MR0pfKQg5UHPXjrXlH/AAkPx1/6EH4ef+Fzf/8Aynpf+Ei+Ov8A0IHw8/8AC4v/AP5T0AerCNVAwAMcDA6UBFUkgDJ68da8p/4SL46/9CB8PP8AwuL/AP8AlPR/wkPx1/6ED4ef+Fzf/wDynoA9V8tR0QcjniuW+I3w38O/FbwVqPhXxPpcepaJfoqzW7EpgqwZHQjlHVlDKRyCBXJf8JD8df8AoQfh5/4XF/8A/Kej/hIfjr/0IPw8/wDC4v8A/wCU9FgNW8+B3hXUvFul+IdRgvNUv9Mnju7JL68llht7mOHyVuFjY7fM2cbv9onrzWj8MfhT4a+EGhXekeGNOFhbXl9NqV0zM0klzczNmSaR25dzwNx7Ko7VzP8AwkPx1/6EH4ef+Fxf/wDyno/4SH46/wDQg/Dz/wALi/8A/lPQB63RXkn/AAkPx1/6EH4ef+Fxf/8Ayno/4SH46/8AQg/Dz/wuL/8A+U9AHrdFeSf8JD8df+hB+Hn/AIXF/wD/ACno/wCEh+Ov/Qg/Dz/wuL//AOU9AHrdFeSf8JD8df8AoQfh5/4XF/8A/Kej/hIfjr/0IPw8/wDC4v8A/wCU9AHrdFeSf8JD8df+hB+Hn/hcX/8A8p6P+Eh+Ov8A0IPw8/8AC4v/AP5T0Aet0V5J/wAJD8df+hB+Hn/hcX//AMp6P+Eh+Ov/AEIPw8/8Li//APlPQB63RXkn/CQ/HX/oQfh5/wCFxf8A/wAp6P8AhIfjr/0IPw8/8Li//wDlPQB63RXkn/CQ/HX/AKEH4ef+Fxf/APyno/4SH46/9CD8PP8AwuL/AP8AlPQB63RXkn/CQ/HX/oQfh5/4XF//APKej/hIfjr/ANCD8PP/AAuL/wD+U9AHrdFeSf8ACQ/HX/oQfh5/4XN//wDKel/4SH46f9CB8Pf/AAuL/wD+U9AC/BP/AJKD8dv+x1h/9MGj0VY+DHhLxXoF1491bxZZaPYan4m8QLqy2mi6lNeQwxpp1lZgGWS3hYsTaM2AmAGAyaKAP//Z)
Using the scatterplot, should we should have any major concerns about the conditions being met for using a linear model with these data?
▸ Yes
▸ No