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Accounting, Finance, SPSS
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Statistics Project
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# SPSS Data: Optimism and Longevity, Multicollinearity & Significant Analysis (Statistics Project Sample)

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Project description I will provide the files for the write up as well as the SPSS data. All work w=must be saved and returned in .doc NOT .docx

source..
Content:

Optimism and Longevity
PSY 870: Module 7 Problem Set
[Student’s Name]
[Institution Name]
Optimism and Longevity
Variable Correlations
An evaluation of variable correlations is important in establishing both the magnitude and the nature of the relationship that exists between variables (Coakes, 2012). In the present case, longevity (years lived after diagnosis) is the dependent variable and the predictors include socioeconomic status (SES), Age and Optimism. As such, the correlations between the dependent and the independent variables are as presented in table 1.
Table 1
Summary of correlations between variables (N = 244)
Years Lived after DiagnosisAgeSocioeconomic StatusOptimismYears Lived after Diagnosis1.00Age-.33***1.00Socioeconomic Status.37***-.29***1.00Optimism.57***-.43***.52***1.00Notes: ***p < .001 As shown in table 1, there are highly significant (p’s < .001) correlations between longevity and the predictors. In this case, the higher the age, the lower the longevity, r = -.33, p < .001. On the other hand, a higher socioeconomic status is associated with increased longevity, r = .37, p < .001. Similarly, increased optimism leads to increased longevity, r = .57, p < .001.
Multicollinearity
The variable Age has a weak-negative relationship with socioeconomic status (r = -.29, p < .001) and a moderate-negative relationship with Optimism (r = -.43, p < .001). This presents no potential problem with multicollinearity. However, there is a strong-positive relationship between optimism and socioeconomic status (r = .52, p < .001). This could indicate a possible problem with multicollinearity.
Goodness of Fit
The model has R-Squared = 0.343, with an adjusted R-Squared = 0.334. This indicates that about 33.4% of the variance around the mean in the longevity is explained by the independent variables in the model. Further, the regression model is highly significant (F(3, 240) = 41.68, p < .001).
Significant Analysis
The coefficient for the intercept, b0 = 3.73, is not significant in the model, t (240) = 1.77, p = .078. Similarly, the coefficient for Age, b1 = -0.07, is not significant in the model, t(240) = -1.68, p = .095. The coefficient for socioeconomic status, b2 = 0.24, is not significant in the model, t(240) = 1.44, p = .151. In contrast, the coefficient for Optimism (b3 = 0.18) is highly significant in the model, t(240) = 7.43, p < .001.
Results Section
In predicting the number of years lived after diagnosis of incurable cancer, the regression model is obtained as Years lived after diagnosis = 3.73 – 0.07*(Age) + 0.24 (Socioeconomic Status) + 0.18*(Optimism). This model is highly significant, F (3, 240) = 41.68, p < .001. However, significance analysis shows that Optimism is the highest and most significant contributor in the model to predict longevity. The adjusted coefficient of determination (adjusted R-squared) is .334, which means that about 33.4% of the variance in the number of years lived after diagnosis is accounted for by Age, SES, and Optimism.
Appendix: SPSS Outputs
GET
DATASET NAME DataSet1 WINDOW=FRONT.
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT yrslived
/METHOD=ENTER age ses optimism.
Regression
NotesOutput Created28-MAR-2014 10:14:04CommentsInputDataC:\Users\JT\Downloads\5996036_ch5bdata.savActive DatasetDataSet1FilterWeightSplit FileN of Rows in Working Data File244Missing Value HandlingDefinition of MissingUser-defined missing values are treated as missing.Cases UsedStatistics are based on cases with no missing values for any variable used.SyntaxREGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT yrslived
/METHOD=ENTER age ses optimism.ResourcesProcessor Time00:00:00.03Elapsed Time00:00:00.12Memory Required1948 bytesAdditional Memory Required for Residual Plots0 bytes
Descriptive StatisticsMeanStd. DeviationNYears Lived after Diagnosis12.744.470244Age29.665.976244Socioeconomic Status4.691.678244Optimism57.3812.405244CorrelationsYears Lived after DiagnosisAgeSocioeconomic StatusOptimismPearson CorrelationYears...
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