Based on the SPSS

1. Based on descriptive statistics,

Descriptive Statistics

Mean Std. Deviation N

math achievement test 12.5645 6.67031 75

motivation scale 2.8744 .63815 73

gender .55 .501 75

grades in h.s. 5.68 1.570 75

parents' education 4.3933 2.31665 75

a. Checking on assumptions1. Correlations Statistics above 0.3

Statistics 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 Correlated

So, all the variables are correlated.

2. Check on multicollinearity – Look on Coefficient Tolerance value must be more than .1.

Model Collinearity Statistics

Tolerance VIF

1

(Constant)

motivation scale .945 1.058

gender .867 1.154

grades in h.s. .895 1.117

parents'

education

.857 1.167

So, there is no multicollinearity.

3. Check on outliers, normality, linearity, homoscedaticity, independence of residuals

So no major diversion from normality.

b. . Look at Model Summary

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .672a .451 .419 5.08436

a. Predictors: (Constant), parents' education, motivation scale, grades

in h.s., gender

b. Dependent Variable: math achievement test

R = .672, R squared = 0.451

= 45.1% variance in mathematics achievement

5. Hypothesis

Ho – There is no statistical significance in the multiple regression.

Ha – There is statistical significance in the multiple regression.

Test statistics Sig Value Alpha-value Decision

F = 13.981 0.000 0.05 Able to reject Ho, Accept Ha

So, There is statistical significance in F (7,63) =13.981, p<0.05.

c. Look at independent variables

Beta must be the biggest

Model Standardized

Coefficients

t Sig.

Beta

1

(Constant) -1.485 .142

motivation scale .206 2.228 .029

gender -.260 -2.696 .009

grades in h.s. .467 4.917 .000

parents' education .186 1.921 .059

So, grades in high school has the strongest unique contribution towards mathematics achievement, followed by motivation and then

gender which is also significant because their p<0.05

Report:A standard multiple regression has been used to analyze the combination of motivation, grades in high school, parent’s education and gender predict mathematics achievement. Based on the descriptive statistics, the highest mean is the grades in high school which is 5.68. The data screen has showed that the combination are correlated with each other. Further normality test shows that there is no major diversion as well as there is no multicollinearity within the independent variables.

Regression results indicate an overall model of two predictors (gender and grades in high school) significantly predicted mathematics achievement 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, maths achievement statistics will also increase by 6.6.