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,