Century National Banks
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Using checking account balance as the dependent variable and using as independent variables the number of ATM transactions, the number of other services used, whether the individual has a debit card, and whether interest is paid on the particular account, write a report indicating which of the variables seem related to the account balance and how well they explain the variation in account balances. Should all of the independent variables proposed be used in the analysis or can some be dropped? By using regression analysis we can now estimate or predict the account balances if we know the number of ATM transactions, the number of other services used, whether the individual has a debit card, and whether interest is paid on the particular account. Y=174.9+90.47(Trans)+59.99(Services)+161.01(Debit) +218.83(Interest) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .765 a .585 .555 398.389 ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regressio n 1.229E7 4 3073046.498 19.362 .000 a Residual 8729246.940 55 158713.581 Total 2.102E7 59 Coefficients a
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Stat Case 1 Century National Banks (1)
Transcript of Century National Banks
Using checking account balance as the dependent variable and using as independent variables the number of ATM transactions, the number of other services used, whether the individual has a debit card, and whether interest is paid on the particular account, write a report indicating which of the variables seem related to the account balance and how well they explain the variation in account balances. Should all of the independent variables proposed be used in the analysis or can some be dropped?By using regression analysis we can now estimate or predict the account balances if we know the number of ATM transactions, the number of other services used, whether the individual has a debit card, and whether interest is paid on the particular account.Y=174.9+90.47(Trans)+59.99(Services)+161.01(Debit)+218.83(Interest)Model Summary
ModelRR SquareAdjusted R SquareStd. Error of the Estimate
1.765a.585.555398.389
ANOVAb
ModelSum of SquaresdfMean SquareFSig.
1Regression1.229E743073046.49819.362.000a
Residual8729246.94055158713.581
Total2.102E759
Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.95.0% Confidence Interval for B
BStd. ErrorBetaLower BoundUpper Bound
1(Constant)174.920167.0531.047.300-159.862509.701
Transaction90.47112.452.6517.265.00065.516115.426
Services59.99227.427.1992.187.0335.026114.957
Debit161.017108.416.1351.485.143-56.254378.287
Interest218.834119.929.1631.825.073-21.508459.177
The regression coefficientsprovide information about their individual relationship with account balances. The regression coefficient for mean number of ATM transactions is 90.47. Thecoefficient is positive and shows a direct relationship between account balance and number of ATM transactions. If we increase the number of ATM transaction by 1 and hold other independent variables constant, we can estimate an increase of 90.47$ in the account balance.The number of other services used variable also shows direct relationship between the account balance and the number of other services used. It is logical that individuals with a higher account balance use more services. So for each additional service used we estimate an increase of 59.99$ in the account balance.The third variable is number of other services used, our regression analysis shows that there is positive relationship between individuals who have debit card and account balances. The data shows if the individual has a debit card then it will increase the account balance by 161.01$.The fourth variable is whether interest is paid on the particular account. Again our data shows positive and direct relationship between account balance and whether interest is paid on the particular account. So the data dhows if there interest is paid in a particular accountthen we can estimate there will be an increase of 218.83$ in the account balance.However, not all the independent variables should be used because not all of them are effective predictors of the account balance. In order to determine which independent variable to tale out we look at the p-value, if the p-value is greater than the .05 significant levels we conclude it is not a good predator. Looking at the table we see the variables debit card and interest their p-value are higher than the significant level. We remove the variable debit and we run the regression analysis again.Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.95.0% Confidence Interval for B
BStd. ErrorBetaLower BoundUpper Bound
1(Constant)228.688164.8291.387.171-101.505558.881
Transaction89.78512.577.6467.139.00064.590114.980
Services67.66327.225.2242.485.01613.125122.201
Interest178.305118.033.1331.511.137-58.144414.754
We observe the p-value associated with interest is still greater than the significant level. So next we remove the interest variable and run the regression analysis again.
Coefficientsa
ModelUnstandardized CoefficientsStandardized CoefficientstSig.95.0% Confidence Interval for B
BStd. ErrorBetaLower BoundUpper Bound
1(Constant)258.292165.4901.561.124-73.096589.681
Transaction91.43312.670.6587.217.00066.062116.804
Services67.88327.529.2252.466.01712.757123.009
We observe that the Adjusted R Square values have declined. Using all four independent variables the Adjusted R Square value was .555. With the two non-significant variables removed the Adjusted R Square value is .53. This means that the two independent variables account for 53% of the variation in the account balance.
