MATLAB vs Python vs R comparison - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output difference in matrix multiplication

What is the output of this MATLAB code snippet?

A = [1 2; 3 4]; B = [5 6; 7 8]; C = A * B;

MATLAB

A = [1 2; 3 4];
B = [5 6; 7 8];
C = A * B;

A [19 22; 43 50]

B [17 20; 39 46]

C [5 12; 21 32]

D [12 16; 28 40]

💡 Hint

Remember that * in MATLAB means matrix multiplication, not element-wise.

In MATLAB, * performs matrix multiplication. Multiplying A and B results in [[19 22]; [43 50]].

❓ Predict Output

intermediate

Python vs MATLAB element-wise multiplication output

What is the output of this Python code snippet using NumPy?

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
C = A * B
print(C)

MATLAB

import numpy as np
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
C = A * B
print(C)

A

[[19 22]
 [43 50]]

B

[[1 2]
 [3 4]]

C

[[5 6]
 [7 8]]

D

[[5 12]
 [21 32]]

💡 Hint

In NumPy, * means element-wise multiplication, unlike MATLAB's *.

NumPy's * operator multiplies elements one by one, so C is [[5 12]; [21 32]].

❓ Predict Output

advanced

R vector recycling behavior output

What is the output of this R code?

v1 <- c(1, 2, 3, 4)
v2 <- c(10, 20)
v3 <- v1 + v2
print(v3)

MATLAB

v1 <- c(1, 2, 3, 4)
v2 <- c(10, 20)
v3 <- v1 + v2
print(v3)

A [10 20 10 20]

B [11 22 13 24]

C [11 22 33 44]

D Error: vectors must be same length

💡 Hint

R recycles shorter vectors to match the length of longer vectors.

R repeats v2 elements to add to v1: 1+10=11, 2+20=22, 3+10=13, 4+20=24.

🧠 Conceptual

advanced

Key difference in indexing between MATLAB and Python

Which statement correctly describes a key difference in indexing between MATLAB and Python?

A MATLAB uses 1-based indexing; Python uses 0-based indexing.

B Both MATLAB and Python use 0-based indexing.

C MATLAB uses 0-based indexing; Python uses 1-based indexing.

D Both MATLAB and Python use 1-based indexing.

💡 Hint

Think about the first element position in each language.

MATLAB arrays start at index 1, while Python arrays start at index 0.

❓ Predict Output

expert

Output of mixed language style code snippet

What is the output of this MATLAB code?

arr = [1, 2, 3, 4];
result = sum(arr > 2);
disp(result);

MATLAB

arr = [1, 2, 3, 4];
result = sum(arr > 2);
disp(result);

A Error: Unsupported operation

B 7

C 2

D 3

💡 Hint

arr > 2 creates a logical array; sum counts true values.

Elements greater than 2 are 3 and 4, so sum counts 2 true values.

Practice

1. Which language is primarily designed for engineering and mathematical computations?

easy

A. R

B. Python

C. MATLAB

D. Java

Solution

1. Step 1: Understand the main purpose of MATLAB

   MATLAB is created mainly for engineering and math tasks, making it ideal for numerical computations.

2. Step 2: Compare with other languages

   Python is general-purpose, and R focuses on statistics, so they are not primarily for engineering math.

3. Final Answer:

   MATLAB -> Option C

4. Quick Check:

   Engineering math = MATLAB [OK]

Hint: MATLAB = math and engineering focus [OK]

Common Mistakes:

• Confusing Python as engineering-specific
• Thinking R is for engineering
• Choosing Java which is unrelated here

2. Which of the following is the correct way to create a list in Python?

easy

A. list = {1, 2, 3}

B. list = (1, 2, 3)

C. list = <1, 2, 3>

D. list = [1, 2, 3]

Solution

1. Step 1: Recall Python list syntax

   Python lists use square brackets [] to hold ordered elements.

2. Step 2: Check each option

   list = [1, 2, 3] uses square brackets, correct for lists. Curly braces {1, 2, 3} create a set, parentheses (1, 2, 3) create a tuple, and <1, 2, 3> is invalid syntax.

3. Final Answer:

   list = [1, 2, 3] -> Option D

4. Quick Check:

   Python list = [] [OK]

Hint: Python lists use square brackets [] [OK]

Common Mistakes:

• Using parentheses () for lists
• Using curly braces {} for lists
• Using invalid angle brackets

3. What will be the output of this R code snippet?

nums <- c(1, 2, 3, 4)
mean_val <- mean(nums)
print(mean_val)

medium

A. 2.5

B. 2

C. 3

D. Error

Solution

1. Step 1: Understand the R code

   The vector nums contains 1, 2, 3, 4. The mean() function calculates the average.

2. Step 2: Calculate the mean

   (1 + 2 + 3 + 4) / 4 = 10 / 4 = 2.5

3. Final Answer:

   2.5 -> Option A

4. Quick Check:

   Mean of 1,2,3,4 = 2.5 [OK]

Hint: mean() in R calculates average [OK]

Common Mistakes:

• Calculating sum instead of mean
• Using integer division
• Expecting syntax error

4. Identify the error in this MATLAB code snippet:

arr = [1, 2, 3, 4];
for i = 1:5
    disp(arr(i))
end

medium

A. Index exceeds array bounds error

B. Syntax error in for loop

C. disp function undefined

D. No error, code runs fine

Solution

1. Step 1: Analyze the loop range and array size

   Array arr has 4 elements, but loop runs from 1 to 5.

2. Step 2: Understand MATLAB indexing

   Accessing arr(5) causes 'Index exceeds array bounds' error because arr only has 4 elements.

3. Final Answer:

   Index exceeds array bounds error -> Option A

4. Quick Check:

   Loop exceeds array size = Index error [OK]

Hint: Loop index must not exceed array length [OK]

Common Mistakes:

• Assuming MATLAB allows out-of-bounds indexing
• Thinking disp is undefined
• Believing for loop syntax is wrong

5. You want to analyze a dataset with many statistical tests and create plots easily. Which language is best suited for this task?

hard

A. MATLAB

B. R

C. Python

D. C++

Solution

1. Step 1: Identify the task requirements

   The task involves many statistical tests and easy plotting.

2. Step 2: Match language strengths

   R is specialized for statistics and data visualization, making it ideal for this task.

3. Step 3: Compare other options

   MATLAB is more engineering-focused, Python is general-purpose, and C++ is low-level and less suited for quick stats and plots.

4. Final Answer:

   R -> Option B

5. Quick Check:

   Statistics and plotting = R [OK]

Hint: R specializes in stats and plotting [OK]

Common Mistakes:

• Choosing MATLAB for stats
• Picking Python without stats libraries
• Selecting C++ for quick analysis