1. Obtain a linear regression equation for the data to predict the mean temperature values for...

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1. Obtain a linear regression equation for the data to predictthe mean temperature values for any given CO2 level. How good isthe linear fit for this data? Explain using residual plot andR-square value. To draw residual plot, compute the estimatedtemperatures for every value of the CO2 level using the regressionequation. Then compute the difference between observed (y) andestimated temperature values (called residual; ). Plot theresiduals versus CO2 level (called a residual plot).
320.09 8
321.44 9.29
322.17 9.39
323.09 8.61
324.68 8.95
325.74 8.36
326.33 9.11
327.52 8.43
329.78 8.39
330.24 8.18
331.18 9.06
332.09 9.12
333.88 8.11
335.43 7.51
336.83 7.42
338.78 7.78
340.17 8.2
340.99 8.6
342.97 8.9
344.23 8.04
345.94 7.18
347.26 7.89
349.06 7.66
351.56 8.9
352.91 9.68
354.21 9.98
355.54 8.88
356.29 9.46
356.97 8.83
358.69 10.29
360.71 10.27
362.41 8.01
363.53 9.28
366.64 9.3
368.16 9.78
369.45 9.88
371.12 9.6
373.24 9.73
375.88 10.35
377.6 9.48
379.87 9.53
381.89 9.94
383.79 10.59

Answer & Explanation Solved by verified expert

4.5 Ratings (973 Votes)

i have considered regression equation with no constant orinterceptxcarbondioxide valuesdependent variableytemparatureindependent variablemy estimated regression line isR square value is 0993853The definition of Rsquared is fairly straightforward it isthe percentage of the response variable variation that is explainedby a linear model OrRsquared Explained variation Total variationRsquared is always between 0 and 1000 indicates See Answer

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320.09	8
321.44	9.29
322.17	9.39
323.09	8.61
324.68	8.95
325.74	8.36
326.33	9.11
327.52	8.43
329.78	8.39
330.24	8.18
331.18	9.06
332.09	9.12
333.88	8.11
335.43	7.51
336.83	7.42
338.78	7.78
340.17	8.2
340.99	8.6
342.97	8.9
344.23	8.04
345.94	7.18
347.26	7.89
349.06	7.66
351.56	8.9
352.91	9.68
354.21	9.98
355.54	8.88
356.29	9.46
356.97	8.83
358.69	10.29
360.71	10.27
362.41	8.01
363.53	9.28
366.64	9.3
368.16	9.78
369.45	9.88
371.12	9.6
373.24	9.73
375.88	10.35
377.6	9.48
379.87	9.53
381.89	9.94
383.79	10.59