first image is the assignment, second image is the data table ABObs.123st450167560010UX55LO68405 1615 19141215DY23162528in m513751515660EY-hat00000000OO00Fe323162528513751515660G529256625784260113692601260131363600HYour ModelBoB₁Sum of ErrorSSESSXS.E. of the EstimateS.E. of B₁K39818102IIiIIL regression predictions that depend on the values found in the table called "Your Model". Here is thebreakdown of the data in the first table: Obs. is the observation number. It runs from 1 to 10.X is the set of values for an independent variable being used to predict the dependent variable.Y is the set of values for the dependent variable being predicted by the regression.Y-hat is the set of predicted values found by multiplying X by the coefficient for B₁ and thenadding the value for B₂. This is what the regression line predicts for Y for a given value of X.e is the set of residuals for the regression. It is the difference between Y and Y-hat. e² is the residuals squared. That simple.Investigate the table of data and familiarize yourself with the equations used to calculate the regressionpredictions. Then start entering different values for B₁ and B₂ in the "Your Model" table. You'll noticethings change when you do this. "Sum of Error" adds up the values of column F in the data table. "SSE"adds up the values in column G in the data table, and is otherwise known as the "Sum of Squares Error".Below this is the SSX or "Sum of Squares for X" which you will need to calculate the S.E. of the Estimate,or "Standard Error of the Estimate" below. And in the last row, you have to calculate the S.E. of B₁ or the"Standard Error of the Slope".Sxy cov(x,y)var (x)B₁ = 53/2==SZBa =ỳ - BSe (S. E. of the Estimate) :SB, (S.E.of B₁) = √SSXSSEn-2You need to calculate values for each of the tan cells in the spreadsheet. For B₁ and 3₂ you will need towrite your own formulas which reference the data for X and Y in the data table. For SSX, remember thatthe SSX is the numerator of the variance of X, which you can find by multiplying the variance by n-1. ForS.E. of the Estimate and the S.E. of B₁ you will again need to write your own formula which uses the SSE,SSX, and the count of observations.

Question

first image is the assignment, second image is the data table ABObs.123st450167560010UX55LO68405 1615 19141215DY23162528in m513751515660EY-hat00000000OO00Fe323162528513751515660G529256625784260113692601260131363600HYour ModelBoB₁Sum of ErrorSSESSXS.E. of the EstimateS.E. of B₁K39818102IIiIIL regression predictions that depend on the values found in the table called &#34;Your Model&#34;. Here is thebreakdown of the data in the first table:>> Obs. is the observation number. It runs from 1 to 10.>>X is the set of values for an independent variable being used to predict the dependent variable.Y is the set of values for the dependent variable being predicted by the regression.>>>>Y-hat is the set of predicted values found by multiplying X by the coefficient for B₁ and thenadding the value for B₂. This is what the regression line predicts for Y for a given value of X.>>e is the set of residuals for the regression. It is the difference between Y and Y-hat.>> e² is the residuals squared. That simple.Investigate the table of data and familiarize yourself with the equations used to calculate the regressionpredictions. Then start entering different values for B₁ and B₂ in the &#34;Your Model&#34; table. You'll noticethings change when you do this. &#34;Sum of Error&#34; adds up the values of column F in the data table. &#34;SSE&#34;adds up the values in column G in the data table, and is otherwise known as the &#34;Sum of Squares Error&#34;.Below this is the SSX or &#34;Sum of Squares for X&#34; which you will need to calculate the S.E. of the Estimate,or &#34;Standard Error of the Estimate&#34; below. And in the last row, you have to calculate the S.E. of B₁ or the&#34;Standard Error of the Slope&#34;.Sxy cov(x,y)var (x)B₁ = 53/2==SZBa =ỳ - BSe (S. E. of the Estimate) :SB, (S.E.of B₁) = √SSXSSEn-2You need to calculate values for each of the tan cells in the spreadsheet. For B₁ and 3₂ you will need towrite your own formulas which reference the data for X and Y in the data table. For SSX, remember thatthe SSX is the numerator of the variance of X, which you can find by multiplying the variance by n-1. ForS.E. of the Estimate and the S.E. of B₁ you will again need to write your own formula which uses the SSE,SSX, and the count of observations.

Accepted Answer

The following given formulas are used to calculate the desired entities:

β^1=Covx,yVarx…