SPSS Assignment only - Lab Report Example

SPSS #4 Assignment- STA-250.16 _____________________________________ Correlations Math Achievement in Twelfth Grade Reading Achievement in Twelfth GradeSchool Average Daily Attendance RateMath Achievement in Twelfth GradePearson Correlation1.636**.051Sig. (2-tailed).000.296N500500417Reading Achievement in Twelfth GradePearson Correlation.636**1.014Sig. (2-tailed).000.769N500500417School Average Daily Attendance RatePearson Correlation.051.0141Sig. (2-tailed).296.769N417417417**. Correlation is significant at the 0.

01 level (2-tailed).Question 8What is the correlation between reading achievement in 12th grade and math achievement in the 12th grade? How strong is this relationship? In what direction is this relationship? (2 points)There exists a strong positive relationship between reading achievement in 12th grade and math achievement in the 12th grade. The positive relationship is depicted by the coefficient value of 0.636 between the two variables. Question 9Does the average daily attendance rate have stronger relationship with math achievement in 12th grade or reading achievement in 12th grade?

Are either of these two relationships significant? (2 points)There is no strong relationship either between math achievement in 12th grade or reading achievement in 12th grade the coefficient is 0.051 and 0.014 respectively. None of the relationships is significant because they have a p value that is greater than 0.01.Question 10Based on the scatterplot for math achievement in 12th grade and socio-economic status, what direction is the relationship? How strong is the relationship? Be sure to explain your answers.

(2 points)There is a weak positive correlation between math achievement in 8th grade and socio-Economic Status. This is because the data points are highly scattered and the trend of the data points seems not to be linear.Question 11Based on the scatter plot for math achievement in 8th grade and math achievement in 12th grade, what direction is the relationship? How strong is the relationship? Be sure to explain you answer. (2 points)There is a strong positive relationship between math achievement in 8th grade and math achievement in 12th grade.

The trend of the scatter plot clearly shows it’s linear whereby the variables are directly proportional Question 12Based on the scatterplot for math achievement in 8th grade and math achievement in 12th grade, how many outliers are there? (1 point)There are two outliers that can be observedPart IIRun a Bivariate regression with guns as the only independent variable and interpret the results. (3 points) Model SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.159a.025.0054.01279a.

Predictors: (Constant), rateANOVAaModelSum of SquaresdfMean SquareFSig.1Regression20.145120.1451.251.269bResidual772.9194816.102Total793.06449a. Dependent Variable: Handgun Homicides per 100,000b. Predictors: (Constant), rateANOVAaModelSum of SquaresdfMean SquareFSig.1Regression20.145120.1451.251.269bResidual772.9194816.102Total793.06449a. Dependent Variable: Handgun Homicides per 100,000b. Predictors: (Constant), rateCoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)5.958.7687.754.000rate.011.010.1591.119.269a. Dependent Variable: Handgun Homicides per 100,000Is the overall model significant?

How do you know?The overall model is not significant because the p value (sig) of 0.269 is greater than 0.01.How much of the variation is explained?The analysis of variance is 1.251 and the R squared of 0.005 explains the variation of the variable.How does whether the State has a waiting period for handgun purchase influence the handgun homicide rate for that State? Remember to describe this relationship in terms of existence, strength, and direction.It positively influences the handgun homicide rate and the relationship is weakModel SummaryModelRR SquareAdjusted R SquareStd.

Error of the Estimate1.542a.294.2483.48862a. Predictors: (Constant), Unemployment Rate, Jan 1996, State has executed a prisoner since 1976, rateANOVAaModelSum of SquaresdfMean SquareFSig.1Regression233.222377.7416.388.001bResidual559.8424612.170Total793.06449a. Dependent Variable: Handgun Homicides per 100,000b. Predictors: (Constant), Unemployment Rate, Jan 1996, State has executed a prisoner since 1976, rateCoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)2.2772.685.848.401rate-.002.009-.029-.217.

829State has executed a prisoner since 1976-2.4621.010-.309-2.437.019Unemployment Rate, Jan 19961.511.429.4553.525.001a. Dependent Variable: Handgun Homicides per 100,000Coefficient CorrelationsaModelUnemployment Rate, Jan 1996State has executed a prisoner since 1976rate1CorrelationsUnemployment Rate, Jan 19961.000-.053-.279State has executed a prisoner since 1976-.0531.000.211rate-.279.2111.000CovariancesUnemployment Rate, Jan 1996.184-.023-.001State has executed a prisoner since 1976-.0231.021.002rate-.001.0028.108E-005a.

Dependent Variable: Handgun Homicides per 100,000Run a Multivariate regression controlling for the other variables (unemp96 and executed) and interpret the results. (3 points)State execution has a negative influence while unemployment has a positive influence on the Hand gun Homicides per 100,000Is the overall model significant? How do you know?The overall model is significant because the p value is less than 0.01How much of the variation is explained?The R squared is 0.294 and the F value is 6.

388How does having a waiting period, unemployment rate, and number of executions influence the handgun homicide rate of a State? Remember to describe these relationships in terms of existence, strength, and direction.Number of execution has a negative influence while the unemployment has a positive influence on the handgun homicide rate of a state. Both have a weak relationship with the handgun homicide rate of a stateDid controlling for the other variables change the results? If so, how? (3 points)It never changed the results significantly