$\%$ Experimental data

solar$\_$irradiance $=[20,30,40,50,60,70]$;

operating$\_$temperature $=[200,400,600,800,1000,1000]$;

data $=[$

$60\mathrm{.}02,57\mathrm{.}68,61\mathrm{.}46,51\mathrm{.}06,56\mathrm{.}68,50\mathrm{.}26$;

$58\mathrm{.}88,61\mathrm{.}81,56\mathrm{.}14,57\mathrm{.}8,50\mathrm{.}36,53\mathrm{.}09$;

$124\mathrm{.}82,120\mathrm{.}97,110\mathrm{.}15,106\mathrm{.}25,100\mathrm{.}39,100\mathrm{.}17$;

$123\mathrm{.}55,113\mathrm{.}55,116\mathrm{.}11,113\mathrm{.}33,101\mathrm{.}79,103\mathrm{.}85$;

$187\mathrm{.}26,179\mathrm{.}25,168\mathrm{.}98,159\mathrm{.}5,155\mathrm{.}63,150\mathrm{.}03$;

$187\mathrm{.}62,176\mathrm{.}65,167\mathrm{.}84,159\mathrm{.}31,158\mathrm{.}81,150\mathrm{.}69$;

$245\mathrm{.}43,236\mathrm{.}87,224\mathrm{.}65,212\mathrm{.}85,204\mathrm{.}04,203\mathrm{.}76$;

$240\mathrm{.}99,231\mathrm{.}41,223\mathrm{.}92,215\mathrm{.}46,209\mathrm{.}34,196\mathrm{.}16$;

$309\mathrm{.}25,293\mathrm{.}03,285\mathrm{.}02,271\mathrm{.}35,256\mathrm{.}25,250\mathrm{.}61$;

$309\mathrm{.}11,296\mathrm{.}82,282\mathrm{.}69,269\mathrm{.}94,260\mathrm{.}65,252\mathrm{.}38$

$]$;

$\%$ Perform statistical analysis

mean$\_$data $=$ mean$($data$,2)$;

std$\_$dev$\_$data $=$ std$($data$,0,2)$;

std$\_$err$\_$data $=$ std$\_$dev$\_$data $/$ sqrt$($size$($data$,2))$;

$\%$ Experimental data

solar$\_$irradiance $=[20,30,40,50,60,70]$;

operating$\_$temperature $=[200,400,600,800,1000,1000]$;

data $=[$

$60\mathrm{.}02,57\mathrm{.}68,61\mathrm{.}46,51\mathrm{.}06,56\mathrm{.}68,50\mathrm{.}26$;

$58\mathrm{.}88,61\mathrm{.}81,56\mathrm{.}14,57\mathrm{.}8,50\mathrm{.}36,53\mathrm{.}09$;

$124\mathrm{.}82,120\mathrm{.}97,110\mathrm{.}15,106\mathrm{.}25,100\mathrm{.}39,100\mathrm{.}17$;

$123\mathrm{.}55,113\mathrm{.}55,116\mathrm{.}11,113\mathrm{.}33,101\mathrm{.}79,103\mathrm{.}85$;

$187\mathrm{.}26,179\mathrm{.}25,168\mathrm{.}98,159\mathrm{.}5,155\mathrm{.}63,150\mathrm{.}03$;

$187\mathrm{.}62,176\mathrm{.}65,167\mathrm{.}84,159\mathrm{.}31,158\mathrm{.}81,150\mathrm{.}69$;

$245\mathrm{.}43,236\mathrm{.}87,224\mathrm{.}65,212\mathrm{.}85,204\mathrm{.}04,203\mathrm{.}76$;

$240\mathrm{.}99,231\mathrm{.}41,223\mathrm{.}92,215\mathrm{.}46,209\mathrm{.}34,196\mathrm{.}16$;

$309\mathrm{.}25,293\mathrm{.}03,285\mathrm{.}02,271\mathrm{.}35,256\mathrm{.}25,250\mathrm{.}61$;

$309\mathrm{.}11,296\mathrm{.}82,282\mathrm{.}69,269\mathrm{.}94,260\mathrm{.}65,252\mathrm{.}38$

$]$;

$\%$ Perform statistical analysis

mean$\_$data $=$ mean$($data$,2)$;

std$\_$dev$\_$data $=$ std$($data$,0,2)$;

std$\_$err$\_$data $=$ std$\_$dev$\_$data $/$ sqrt$($size$($data$,2))$;

ci$\_$data $=$ tinv$(1-0\mathrm{.}05/2,$ size$($data$,2)-1)*$ std$\_$err$\_$data;

$\%$ Plot the graph

figure;

errorbar$($solar$\_$irradiance, mean$\_$data, ci$\_$data, $\text{'}$o$-\text{'},$ 'LineWidth', $1\mathrm{.}5)$;

xlabel$(\text{'}$Solar Irradiance $($W$/$m$^2)\text{'})$;

ylabel$(\text{'}$Output Voltage'$)$;

title$(\text{'}$PV Solar Panel Performance'$)$;

grid on;

legend$(\text{'}$Mean Output Voltage', $\text{'}95\%$ CI$\text{'})$;

$\%$ Create a table for data analysis

table$\_$data $=$ table$($solar$\_$irradiance', mean$\_$data, ci$\_$data, 'VariableNames', $\{\text{'}$Solar$\_$Irradiance', 'Mean$\_$Output$\_$Voltage', $\text{'}$CI$\text{'}\})$;

disp$(\text{'}$Table for Data Analysis:$\text{'})$;

disp$($table$\_$data$)$;

$\%$ Detail data analysis

$\%-$ The mean output voltage increases with solar irradiance.

$\%-$ The standard error of the mean decreases with increasing solar irradiance, indicating more consistent data.

$\%-$ The $95\%$ confidence interval becomes narrower with increasing solar irradiance, indicating more precise estimates of the mean.

$\%$ Conclusion

disp$(\text{'}$Conclusion:$\text{'})$;

disp$(\text{'}$The PV solar panel shows a positive correlation between solar irradiance and output voltage. Higher solar irradiance leads to higher output voltage, indicating better performance.$\text{'})$;ci$\_$data $=$ tinv$(1-0\mathrm{.}05/2,$ size$($data$,2)-1)*$ std$\_$err$\_$data;H

$\%$ Plot the graph

figure;

errorbar$($solar$\_$irradiance, mean$\_$data, ci$\_$data, $\text{'}$o$-\text{'},$ 'LineWidth', $1\mathrm{.}5)$;

xlabel$(\text{'}$Solar Irradiance $($W$/$m$^2)\text{'})$;

ylabel$(\text{'}$Output Voltage'$)$;

title$(\text{'}$PV Solar Panel Performance'$)$;

grid on;

legend$(\text{'}$Mean Output Voltage', $\text{'}95\%$ CI$\text{'})$;

$\%$ Create a table for data analysis

table$\_$data $=$ table$($solar$\_$irradiance', mean$\_$data, ci$\_$data, 'VariableNames', $\{\text{'}$Solar$\_$Irradiance', 'Mean$\_$Output$\_$Voltage', $\text{'}$CI$\text{'}\})$;

disp$(\text{'}$Table for Data Analysis:$\text{'})$;

disp$($table$\_$data$)$;

$\%$ Detail data analysis

$\%-$ The mean output voltage increases with solar irradiance.

$\%-$ The standard error of the mean decreases with increasing solar irradiance, indicating more consistent data.

$\%-$ The $95\%$ confidence interval becomes narrower with increasing solar irradiance, indicating more precise estimates of the mean.

$\%$ Conclusion

disp$(\text{'}$Conclusion:$\text{'})$;

disp$(\text{'}$The PV solar panel shows a positive correlation between solar irradiance and output voltage. Higher solar irradiance leads to higher output voltage, indicating better performance.$\text{'})$;

$***$Pls help me to rectify the ci$\_$data $=$ tinv$(1-0\mathrm{.}05/2,$ size$($data$,2)-1)*$ std$\_$err$\_$data; without using the tinv $.$ The two figure is the question.