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100+ Free WACE Maths Applications Practice Questions

WACE ATAR Mathematics Applications (Units 3 & 4) practice questions are available now; exam metadata is being verified.

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Key Facts: WACE Maths Applications Exam

2 sections

Calculator-free (35%) and calculator-assumed (65%) make up the written examination

SCSA Mathematics Applications ATAR Year 12 syllabus

150 minutes

Total working time: 50 minutes calculator-free plus 100 minutes calculator-assumed

SCSA Mathematics Applications ATAR Year 12 syllabus

6 topics

Three Unit 3 topics and three Unit 4 topics are examined

SCSA Mathematics Applications ATAR Year 12 syllabus

5-10 questions

Section One Calculator-free contains 5 to 10 questions

SCSA Mathematics Applications ATAR Year 12 syllabus

8-13 questions

Section Two Calculator-assumed contains 8 to 13 questions

SCSA Mathematics Applications ATAR Year 12 syllabus

50% weighting

The exam contributes 50% of the final course mark, with school assessment the other 50%

SCSA WACE Manual and course outline

From 2025

Current syllabus is the Mathematics Applications ATAR Year 12 version for teaching from January 2025

SCSA Mathematics Applications syllabus (2025)

100

Free original multiple-choice practice questions here

OpenExamPrep

WACE ATAR Mathematics Applications is the Year 12 applied-mathematics ATAR course in Western Australia, set by SCSA on Units 3 and 4. The exam has two sections: Section One Calculator-free (35%, 50 minutes, 5-10 questions) and Section Two Calculator-assumed (65%, 100 minutes, 8-13 questions), using short-answer and extended-answer questions rather than multiple choice. The six examinable topics are bivariate data analysis, growth and decay in sequences, graphs and networks, time series analysis, loans/investments/annuities, and networks and decision mathematics. There is no fixed pass mark; the standardised exam mark is combined 50/50 with school assessment and scaled into an ATAR. This 100-question bank provides original multiple-choice practice drilling the same Unit 3 and Unit 4 content and skills.

Sample WACE Maths Applications Practice Questions

Try these sample questions to test your WACE Maths Applications exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In a scatterplot of two numerical variables, the variable plotted on the horizontal axis that is used to predict the other is called the:
A.Response variable
B.Explanatory variable
C.Residual variable
D.Dependent variable
Explanation: By convention the explanatory (independent) variable is placed on the horizontal axis and is used to explain or predict the response variable on the vertical axis. Identifying it correctly is the first step in bivariate analysis.
2A scatterplot shows points that fall close to a straight line that slopes downwards from left to right. The correlation is best described as:
A.Strong positive
B.Strong negative
C.Weak positive
D.No correlation
Explanation: Points lying close to a downward-sloping line indicate a strong, negative linear association: as one variable increases, the other decreases, and the tight clustering means the relationship is strong.
3Pearson's correlation coefficient r for a data set is -0.92. This indicates a:
A.Strong positive linear association
B.Strong negative linear association
C.Weak negative linear association
D.Perfect negative linear association
Explanation: An r value of -0.92 is close to -1, so the linear association is strong, and the negative sign shows the variables move in opposite directions. Values near +/-1 indicate a strong relationship.
4The least-squares regression line for predicting weekly sales (y, in $1000s) from advertising spend (x, in $1000s) is y = 3.2 + 1.5x. What does the gradient 1.5 mean in context?
A.Sales are $1500 when advertising is zero
B.Each extra $1000 of advertising is associated with $1500 more sales
C.Each extra $1000 of advertising is associated with $1500 less sales
D.Total sales equal 1.5 times advertising
Explanation: The gradient is the predicted change in y for each one-unit increase in x. Here, each extra $1000 (one unit of x) in advertising is associated with an increase of 1.5 units of y, i.e. $1500 in sales.
5Using the regression line y = 3.2 + 1.5x (sales in $1000s, advertising in $1000s), the predicted sales when advertising spend is $6000 is:
A.$9000
B.$12 200
C.$10 700
D.$11 000
Explanation: Advertising of $6000 means x = 6. Then y = 3.2 + 1.5 x 6 = 3.2 + 9 = 12.2, which is 12.2 thousand dollars, or $12 200.
6The coefficient of determination for a linear model is r-squared = 0.81. The best interpretation is that:
A.81% of the variation in the response is explained by the explanatory variable
B.The correlation coefficient is 0.81
C.81% of data points lie on the line
D.The gradient of the line is 0.81
Explanation: The coefficient of determination r-squared gives the proportion of the variation in the response variable that is explained by the linear relationship with the explanatory variable. Here that is 81%.
7If the coefficient of determination is r-squared = 0.64 and the scatterplot slopes upwards, the correlation coefficient r equals:
A.0.64
B.0.80
C.-0.80
D.0.41
Explanation: Since r = the square root of r-squared with the sign of the slope, r = sqrt(0.64) = 0.8, and because the slope is upward (positive), r = +0.80.
8A regression line is fitted to data on plant height (cm) versus age (days) for ages 5 to 30 days. Using the line to predict the height at age 60 days is an example of:
A.Interpolation
B.Extrapolation
C.Smoothing
D.Standardising
Explanation: Predicting outside the range of the observed data (here beyond 30 days) is extrapolation, which is unreliable because the linear pattern may not continue. Interpolation predicts within the data range.
9A residual is defined as:
A.Predicted value minus actual value
B.Actual value minus predicted value
C.Actual value divided by predicted value
D.The mean of the response variable
Explanation: A residual equals the observed (actual) value minus the value predicted by the regression line. A positive residual means the model under-predicted; a negative residual means it over-predicted.
10For the line y = 12 - 0.8x, the actual data value at x = 5 is y = 9. The residual at this point is:
A.+1
B.-1
C.+9
D.-8
Explanation: Predicted y = 12 - 0.8 x 5 = 12 - 4 = 8. Residual = actual minus predicted = 9 - 8 = +1, so the model slightly under-predicted the value.

About the WACE Maths Applications Practice Questions

Verified exam format metadata for WACE ATAR Mathematics Applications (Units 3 & 4) is pending. The practice questions above remain available while official exam length, timing, passing score, fee, and administrator details are reviewed.