Please help fit an analogous quadratic regression model to see which statements are true

In the le 'MMP13csv' are time-course data on the expression of a gene known as main): metallopeptr'daae 13. Matrix mettalopeptidases comprise a gene familv that encodes proteins that breakdovm extracellular matrix proteins. such as collagen. during normal [e.g., developmental tissue remodeling] and disease leg.r arthritis} processes. The data contained in this le correspond to MMP'FS expression (log2_R} during noimal developmental tissue remodeling {Time}. Fit a linear regression model treating log2_Fi. as the response and "time as the predictor. Then, t an analogous quadratic regression model. Use the results of these tvvo models to evaluate each of the statements below as TRUE or FALSE and select the statements that are TRUE. |:J The error variance of the quadratic model is not constant |:J Although there are some issues with the t of the quadratic model. it is clear that it provides a better description of these data than the linear model |:J The quadratic model accounts for far more of the variaon in the data than the linear model B The residuals from both models are not l'tl'l'l'lally' distiibuted Animal ID Time log2_R 2 28 0 -0.12758 36 2 0.676948 A 48 4 1.123076 10 6 0.697547 22 8 1.421839 CO O U 37 10 0.505238 40 12 0.673202 LO 21 14 1.222039 10 7 16 1.905134 11 15 18 2.192661 12 53 20 3.099899 13 22 3.463918 14 23 24 3.751445 15 32 26 -0.04922 16 20 28 0.221456 17 19 30 0.059109 18 49 0 0.255163 19 35 2 0.055364 20 3 4 2.00409721 39 6 1.670041 22 47 8 1.757768 23 52 10 1.164274 24 58 12 2.719026 25 59 14 1.128694 26 43 16 1.459005 27 12 18 0.579857 28 41 20 1.109968 29 11 22 0.94013 30 13 24 1.754023 31 27 26 -0.41324 32 46 28 1.557969 33 44 30 1.376896 34 42 32 0.400656 35 55 0 -0.12758 36 34 2 -0.20969 37 33 4 -0.20782 38 54 6 1.399367 39 38 8 -0.57746 40 4 10 2.03593241 45 12 2.38497 42 25 14 1.399367 43 16 1.565459 44 24 18 1.10435 45 51 20 0.751567 46 16 22 2.648152 47 5 24 3.282844 48 17 26 -1.23649 49 57 28 0.764675 50 14 30 0.234564 51 18 32 -0.1332