Question

For this problem, we must model the global averages in CO2 and N2O atmospheric concentrations. We will find

the CO2 data, with its complete NOAA header, in the file named Data_NOAA_ESRL_CO2_trend_1980-2000
global.csv and the N2O data in the file Data_NOAA_ESRL_N2O_trend_2001-2022.txt.
For Data_NOAA_ESRL_CO2_trend_1980-2000
global.csv ( year eg1980 starts from A1 and month eg 1 starts from B1 and decimal eg1980.042 starts from C1 and average eg338.55 starts from D1 and trend eg 337.93 starts from E1)
For Data_NOAA_ESRL_N2O_trend_2001-2022.txt. ( the data is in textform instead of csv format)
Year eg2001 Month eg1 Decimal Average Average_unc Trend Trend_unc eg 0.2
eg values 2001 1 2001.042 316.3 0.2 316.0 0.2
Write a MATLAB no-input, no-output function to perform least squares regression on the provided data. We will
model average CO2 (in ppm) as a function of decimal year and then model average N2O (in ppb) as a function of
decimal year. The function must
• Perform linear regression on the data to get the least-squares coefficients, first for CO2 and then for N2O.
• Determine the predicted values of each linear model.
• Calculate SSE, SST, and r2 values for each model.
• Display the linear model equation (with clear variable names), SSE, SST, and r2 to the Command Window
for each model. Make sure you can differentiate between the information for CO2 and N2O.
• Generate one figure with two subplots stacked vertically. On the top subplot, display the data and the
trend line on the same axes for CO2. On the bottom subplot, display the data and trend line on the same
axes for N2O. Format both subplots and the figure for technical presentation.
-Learn about the xlim function to set the x-axis limits of both subplots. Make your subplots have
identical axes limits on the x-axis and show all data in both subplots.
From your analyses, can you draw a conclusion about the accuracy of the data measurements?
For which data set does a linear model best explain the variation that exists in the data? Clearly state the
basis of your reasoning

Answer 1

To perform least squares regression on the provided data and determine the accuracy of the data measurements, a MATLAB function can be used. The function will calculate coefficients, predicted values, SSE, SST, r2, and generate a figure with two subplots.

Step-by-step explanation:

To model the average CO2 and N2O atmospheric concentrations, we can perform least squares regression on the provided data using MATLAB. The steps are as follows:

1. Read the CO2 and N2O data files.
2. Perform linear regression on the CO2 data to get the least-squares coefficients, predicted values, SSE, SST, and r2.
3. Perform linear regression on the N2O data to get the least-squares coefficients, predicted values, SSE, SST, and r2.
4. Create a figure with two vertically stacked subplots.
5. Plot the CO2 data and trend line on the top subplot.
6. Plot the N2O data and trend line on the bottom subplot.
7. Format the subplots and the figure for technical presentation.

Based on the calculated r2 values, we can draw conclusions about the accuracy of the data measurements and the linear model's ability to explain the variation. The dataset with a higher r2 value indicates a better linear model fit.