Can someone perform a Multiple Linear Regression Analysis in excel using Ford's total supply chain costs from $2018$to $2024.$The analysis will help me determine which variables are most significant in driving supply chain costs Data:

$$Dependent Variable $($Y$)$: Total Supply Chain Costs $($USD$)$

$$Independent Variables $($X$)$:

$1.$Shipping Costs $($USD$/$Unit$)$

$2.$Raw Material Prices $($USD$/$ton$)$

$3.$Production Volume $($Units$)$

$$ Control Variables

$4.$Exchange Rate $($USD$/$CNY$)$

$5.$Fuel Prices $($USD$/$Liter$)$

$$ build the regression model and generate the following outputs:

$$Coefficients for each variable

$$p$-$values for statistical significance

$$R$-$squared value to evaluate model fit

$$A summary of the regression equation

$$Confidence intervals for each coefficient

$$Forecasts of future supply chain costs based on potential changes in these variables for $2025$and beyond

Additionally, if any variables are constant or do not have a significant effect on the outcome $($e$.$g$.,$exchange rate$),$please feel free to change it or adjust it same thing with any issues with multicollinearity or other assumptions. Noting that i already did all the steps but the regression model is always wrong.there$$s a problem with the data that I don$$t know about i hope u can fix it for me