yearmean (ppm)annual CO2 emmissions in billion metric tons1959315.988.861960316.919.391961317.649.421962318.459.751963318.9910.271964319.6210.831965320.0411.321966321.3711.871967322.1812.241968323.0512.911969324.6213.771970325.6814.901971326.3215.511972327.4616.231973329.6817.091974330.1917.021975331.1317.051976332.0317.991977333.8418.501978335.4119.081979336.8419.621980338.7619.501981340.1219.041982341.4818.881983343.1519.011984344.8719.661985346.3520.331986347.6120.631987349.3121.271988351.6922.111989353.222.411990354.4522.761991355.723.241992356.5422.581993357.2122.811994358.9622.971995360.9723.461996362.7424.161997363.8824.301998366.8424.211999368.5424.732000369.7125.452001371.3225.672002373.4526.282003375.9827.652004377.728.642005379.9829.612006382.0930.592007384.0231.512008385.8332.092009387.6431.562010390.133.362011391.8534.492012394.0635.012013396.7435.322014398.8135.582015401.0135.562016404.4135.522017406.7636.102018408.7236.832019411.6537.082020414.2135.262021416.4137.122022418.5337.49 Use the attached file to construct the followingco2maunaloacontentrations annualemissions.xlsxa. What are the random variables whose data are shown in the attached file.What is the type of these random variables?b. Draw two (or a single merged) time series of the two random variablesc. Which of the two variables do you want to take as explanatory and whichone as response?d. Make a scatterplot of the response/explanatory random variables. Why thescatteplot is the correct form of graphical representation?e. What does the scatterplot suggest?f. Can you derive a linear model for these two random variables? Will themodel be valid? do you need to make any assumptions to make the modelvalid?

Answered: Use the attached file to construct the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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year	mean (ppm)	annual CO2 emmissions in billion metric tons
1959	315.98	8.86
1960	316.91	9.39
1961	317.64	9.42
1962	318.45	9.75
1963	318.99	10.27
1964	319.62	10.83
1965	320.04	11.32
1966	321.37	11.87
1967	322.18	12.24
1968	323.05	12.91
1969	324.62	13.77
1970	325.68	14.90
1971	326.32	15.51
1972	327.46	16.23
1973	329.68	17.09
1974	330.19	17.02
1975	331.13	17.05
1976	332.03	17.99
1977	333.84	18.50
1978	335.41	19.08
1979	336.84	19.62
1980	338.76	19.50
1981	340.12	19.04
1982	341.48	18.88
1983	343.15	19.01
1984	344.87	19.66
1985	346.35	20.33
1986	347.61	20.63
1987	349.31	21.27
1988	351.69	22.11
1989	353.2	22.41
1990	354.45	22.76
1991	355.7	23.24
1992	356.54	22.58
1993	357.21	22.81
1994	358.96	22.97
1995	360.97	23.46
1996	362.74	24.16
1997	363.88	24.30
1998	366.84	24.21
1999	368.54	24.73
2000	369.71	25.45
2001	371.32	25.67
2002	373.45	26.28
2003	375.98	27.65
2004	377.7	28.64
2005	379.98	29.61
2006	382.09	30.59
2007	384.02	31.51
2008	385.83	32.09
2009	387.64	31.56
2010	390.1	33.36
2011	391.85	34.49
2012	394.06	35.01
2013	396.74	35.32
2014	398.81	35.58
2015	401.01	35.56
2016	404.41	35.52
2017	406.76	36.10
2018	408.72	36.83
2019	411.65	37.08
2020	414.21	35.26
2021	416.41	37.12
2022	418.53	37.49

Use the attached file to construct the following
co2maunaloacontentrations annualemissions.xlsx
a. What are the random variables whose data are shown in the attached file.
What is the type of these random variables?
b. Draw two (or a single merged) time series of the two random variables
c. Which of the two variables do you want to take as explanatory and which
one as response?
d. Make a scatterplot of the response/explanatory random variables. Why the
scatteplot is the correct form of graphical representation?
e. What does the scatterplot suggest?
f. Can you derive a linear model for these two random variables? Will the
model be valid? do you need to make any assumptions to make the model
valid?

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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