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# Question 2

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SPSS

SPSS

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• 1. Based on the SPSS1. Based on descriptive statistics,Descriptive StatisticsMean Std. Deviation Nmath achievement test 12.5645 6.67031 75motivation scale 2.8744 .63815 73gender .55 .501 75grades in h.s. 5.68 1.570 75parents education 4.3933 2.31665 75a. Checking on assumptions1. Correlations Statistics above 0.3Statistics Value Sig Value Alpha-value CorrelationMaths achievement x motivation scale .316 0.003 0.05 CorrelatedMaths achievement x parent’s .504 0.000 0.05 CorrelatedMaths achievement x gender -.301 0.004 0.05 CorrelatedMaths achievement x grades .389 0.000 0.05 CorrelatedSo, all the variables are correlated.2. Check on multicollinearity – Look on CoefficientTolerance value must be more than .1.Model Collinearity StatisticsTolerance VIF1(Constant)motivation scale .945 1.058gender .867 1.154grades in h.s. .895 1.117parents education .857 1.167So, there is no multicollinearity.3. Check on outliers, normality, linearity, homoscedaticity, independence of residualsSo no major diversion from normality.
• 2. b. . Look at Model SummaryModel SummarybModel R R Square Adjusted RSquareStd. Error of theEstimate1 .672a.451 .419 5.08436a. Predictors: (Constant), parents education, motivation scale, gradesin h.s., genderb. Dependent Variable: math achievement testR = .672, R squared = 0.451= 45.1% variance in mathematics achievement5. HypothesisHo – There is no statistical significance in the multiple regression.Ha – There is statistical significance in the multiple regression.Test statistics Sig Value Alpha-value DecisionF = 13.981 0.000 0.05 Able to reject Ho, Accept HaSo, There is statistical significance in F (7,63) =13.981, p<0.05.c. Look at independent variablesBeta must be the biggestModel StandardizedCoefficientst Sig.Beta1(Constant) -1.485 .142motivation scale .206 2.228 .029gender -.260 -2.696 .009grades in h.s. .467 4.917 .000parents education .186 1.921 .059So, grades in high school has the strongest unique contribution towards mathematics achievement, followed by motivation and thengender which is also significant because their p<0.05Report:A standard multiple regression has been used to analyze the combination of motivation, grades in high school, parent’s education and genderpredict mathematics achievement. Based on the descriptive statistics, the highest mean is the grades in high school which is 5.68. The datascreen has showed that the combination are correlated with each other. Further normality test shows that there is no major diversion as well asthere is no multicollinearity within the independent variables.Regression results indicate an overall model of two predictors (gender and grades in high school) significantly predicted mathematicsachievement R squared = 0.451, F (7, 63) =13.981, p<0.05. Therefore, the model which includes grades, motivation and gender explains 46.2 %of the variance in the mathematics achievement. Of these three variables, grades in high school makes the largest contribution (beta = 0.467)while motivation is 0.206 and gender has a beta = -.260. The beta values also indicate the increase of standard deviation 1.57 in grades, mathsachievement statistics will also increase by 6.6.