This multiple regression analysis examined how motivation, grades in high school, parent's education, and gender predict mathematics achievement. The analysis found that the combination of these variables significantly predicted math achievement, with grades in high school having the strongest influence followed by motivation and gender. Specifically, the model explained 45.1% of the variance in math achievement, and grades in high school, motivation, and gender were individually significant predictors.

1. 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 assumptions
1. Correlations Statistics above 0.3
Statistics Value Sig Value Alpha-value Correlation
Maths achievement x motivation scale .316 0.003 0.05 Correlated
Maths achievement x parent’s .504 0.000 0.05 Correlated
Maths achievement x gender -.301 0.004 0.05 Correlated
Maths 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.

2. b. . Look at Model Summary
Model Summary
b
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .672
a
.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.