Business statistic - Case Study Example

Results This paper aims to reveal the result of the analysis of the 62 unscheduled repair reports randomly selected to give a clear prediction of the total repair cost based on the number of hours of operation of the equipments for A & A Industrial Products. The Statistical Package for Social Science (SPSS) was utilized.Figure 1Scatter Plot Figure 1 shows the pictorial relationship between hours of operation and repair cost. It is shown in the scatter plot the relationship is almost positively perfect as reflected by the r value of .

989 which according to Hurlburt , this coefficient of correlation is “very high” (376). Also, as seen in Table 1, the r squared was found to be 0.977. This means that 97.7% of the changes in the number of hours of operation cause the changes in the total repair cost.Table 1Relationship between Repair cost and Hours of operationVariablesRR SquareRepair cost and Hours of operation.989.977 Likewise, by the use of the regression analysis, it was found that the equation of predicting the repair cost in terms of the number of hours of operation is:Repair Cost = -22537.

938 + (104.942)*(Hours of Operation)The actual repair cost for each of the number of hours of operation which was recorded is presented below together with the predicted repair cost found using the equation.Repair Cost Hours of operationPredicted repair cost11823.132918000.1369713972.693079679.2063911697.913099889.0900715119.3531210203.9155914591.3432511568.1594916228.4635414611.4728116155.8836015241.1238417676.7236715975.7167219802.3839418809.1463620567.5140319753.6229118962.2540519963.

5065918513.7240519963.5065919160.1341020488.2157820930.7941921432.6923319227.5343222796.9362320324.0643322901.8780723625.6444323951.2964621298.5645024685.8893323329.8846426155.0750723190.4446526260.0169121941.1846726469.9005824962.4947827624.2608126002.0448828673.679225865.249529408.2720730041.3950630562.6322932055.2152032031.8180331062.1652232241.7017131368.1652432451.5853931347.1652532556.5272334771.5252832871.3527435137.353433501.0037832662.0953633710.8874535039.7354734865.2476836181.3854834970.

1895235316.7555435599.8405535593.6455836019.607935800.456837069.0262936458.757938223.3865239026.1658739062.9212340711.6358939272.804941423.759139482.6885841093.2359339692.5722641027.8259539902.4559439433.5660641056.8161640832.1360741161.75845175.0464044624.8386844624.564144729.7805244994.3564445044.6060343652.5464445044.6060346024.7464645254.4897147684.4465245884.1407446191.6765546198.9662645513.5166146828.6172947420.0466246933.5591348078.7967448192.8611949139.4768349137.3377447934.5668549347.

2214251414.3369950816.4071660686.776057217.8593258862.2177158372.2195561650.9178359631.5216164142.7379160471.05632Reference:Hurlburt, Russell Understanding Behavioral Statistics Brooks/Cole Publishing Company, NY USA. 2001AppendicesAnalysis Output(Linear Regression)Model SummarybModelRR SquareAdjusted R SquareStd. Error of the Estimate1.989a.977.9772035.62385a. Predictors: (Constant), Hours_operationb. Dependent Variable: Repair_costANOVAbModelSum of SquaresdfMean SquareFSig.1Regression1.080E1011.

080E102605.924.000aResidual2.486E8604143764.448Total1.105E1061a. Predictors: (Constant), Hours_operationb. Dependent Variable: Repair_costCoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)-22537.9381127.020-19.998.000Hours_operation104.9422.056.98951.048.000a. Dependent Variable: Repair_cost