Question

Dave Fletcher, the general manager of North Carolina Engineering Corporation (NCEC), thinks that his firm's engineering services contracted to highway construction firms are directly related to the volume of highway construction business contracted with co n armes n his goograph area. He wonders this is eal y su and t is an his f rmati n help hım p an tı s ope a or's belter by fo e剰 ng he quar y u tvs engin enn s rv es 'equired by u ns uch n firms in each quarter yearつ he follo ing table presents the sales of his services and total amounts ot contracts tor highway construction over the last& quarters better by forecasling the quanlily of his er Quarter Sales of NCEC Services (In S thousands) 15 9 16 10 2 12 153 175 200 178 182 22 20B 23 Contracts Released (in $ thousands) a) using the given data, the least-squares regression equation tor predicting the level ot demand ot NCEC's services is fround your responses to tvo decimai places) y922 11x where y DepenMent Variable and - Independent Variable b) The coe cert or correlation for he least squares regresson mode: б round your respo se io two dec ra, places For the least-squares model, the standard emor ot the estmate thousand (round your response to two decimai placea)

Please show work, I am having trouble finding where I am going wrong on this part.

Answer 1

Using Excel data analysis

The independent variable is contracts released (x)

The dependent variable is the sales of NCEC services(Y)

The regression equation is

Book1 Excel FILE HOME INSERT PAGE LAYOUT FORMULAS DATA REVIEW VIEW Sign in Data Analysis t ㄧ囹Connections 쇱지 From Access 三囝 Solver From Web From Other Existing Refresh Text to Flash Remove Data Consolidate What-If Relationships Group Ungroup Subtotal Columns ll Duplicates Validation From TextConnections AllEdit Links Analysis Connections All. Edit Links 亂Sort Filter "Reapply Get External Data Sort & Filter Data Tools lysis G17 Contracts released Sales of NCEC services 153 SUMMARY OUTPUT 175 6 16 Regression Statistics 10 178 Multiple R 0.810861 12 R Square 0.657496 10 12 Adjusted F 0.600412 Standard 1.881277 15 12 223 Observati 13 ANOVA 15 MS nificonce F 16 Regressior 1 40.76477 40.76477 11.51806 0.014607 Residual 6 21.23523 3.539205 18 Total 62 19 Coefficientsandard Err t Stat P-value Lower 95%Upper 95%over 9509pper 95.0% Intercept 9.14022 6.117958 1.494 0.185794 24.1103 5.829886 24.1103 5.829886 20 833 Contracts 0.108561 0.031988 3.393827 0.014607 0.03029 0.186833 0.03029 23 24 Sheet1 + 100%

y=-9.14+0.11x

b)

Coefficient of correlation from excel = 0.81

The standard error estimate is = from the regression output = 1.88

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