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HI6007 Assignment on Statistics and Research Method for Business

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Added on  2020-04-07

HI6007 Assignment on Statistics and Research Method for Business

   Added on 2020-04-07

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Running Head: HI6007 GROUP ASSİGNMENTStudent Name:Partner(s) Name:Course:Professor Name:Date Submitted:HI6007 Group Assignment
HI6007 Assignment on Statistics and Research Method for Business_1
HI6007 Group AssignmentTask 11.Descriptive Statistics summary for the sampled data of 50 credit card users:Income($1000s)HouseholdSizeAmountCharged ($)Mean43.483.423963.86Standard Error2.0580.246132.023Median4234090Mode5423890Standard Deviation14.551.74933.55Sample Variance211.723.02871508.74Kurtosis-1.25-0.72-0.74Skewness0.010.53-0.13Range4663814Minimum2111864Maximum6775678Sum2174171198193Count505050Confidence Level (95.0%)4.140.50265.31CommentsThe average household size was computed to be 3.42. The data values are likely to deviate by 1.739 around this mean value. Further, as read from the median value, about 50% of the sampled customers had a household size of 3 or more. The minimum & maximum household sizes are 1 and 7, respectively.The mean annual Income of sampled customers is $43,480. The data values arelikely to deviate within $14,550 around this mean value. Moreover, about 50% of the customers have an annual income of $42,000 or above, while the remaining 50% below this. The maximum and minimum recorded annual incomes for the sampled data are $21,000 and $67,000, respectively.The average annual amount charged to the credit card holders is $3963.8. The data values are likely to deviate by $933.55 around this mean value. Further, the median value suggests that about 50% of the credit card holders were charged $4,090 or over while the remaining 50% below this. The minimum Page | 1
HI6007 Assignment on Statistics and Research Method for Business_2
HI6007 Group Assignmentand maximum amounts charged to credit card holders are $1,864 and $5,678, respectively. The kurtosis and skewness factors for all the three variables further indicate that distribution is approximately normal (with some measure of skewness). 2.Following regression models and equations were obtained for the 2 cases:Case 1: Income as the Independent variableThe regression equation is given as:y=β0+β1(x)Amountcharged($)=2204.24+40.47(Income($1000s))Excel Regression Output:SUMMARY OUTPUTRegression StatisticsMultiple R0.6308R Square0.3979Adjusted R Square0.3853Standard Error731.9025Observations50ANOVAdfSSMSFSig. FRegression116991228.9116991228.9131.720.0000Residual4825712699.11535681.23Total4942703928.02CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Intercept2204.24329.136.6970.00001542.472866.01Income ($1000s)40.477.195.6320.000026.0254.92CommentsThe overall model and the individual slope coefficient are statistically significant (Sig. F and p-value are less than the assumed significance level of 0.05). However, the model is a poor fit as it explains only about 39.79% of the variation in the dependent variable (annual amount charged).Page | 2
HI6007 Assignment on Statistics and Research Method for Business_3
HI6007 Group AssignmentCase 2: Household size as the Independent variableThe regression equation is given as:Amountcharged($)=2581.64+404.16¿Excel Regression Output:SUMMARY OUTPUTRegression StatisticsMultiple R0.7529R Square0.5668Adjusted R Square0.5578Standard Error620.8163Observations50ANOVAdfSSMSFSig. FRegression124204112.2824204112.2862.800.0000Residual4818499815.74385412.83Total4942703928.02CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Intercept2581.64195.2713.2210.00002189.032974.26Household size404.1651.007.9250.0000301.61506.70CommentsHere as well, the overall model and the individual slope coefficient (householdsize) are statistically significant. Further, the model is a moderate fit as it explains about 56.68% of the variationin the dependent variable (amount charged) by the predictor variable (household size).ConlcusionAs evident from the above models, the variable ‘household size’ is a better predictor of annual credit card charges at it explains about 56.68% of the variation in the dependent variable (more than that by the variable Income).Page | 3
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