[Practical Exercise] Statistical Analysis of Student Grade Data in MATLAB

# Practical Exercise: Statistical Analysis of Student Grades in MATLAB

## 2.1 Data File Reading

### 2.1.1 Reading txt Files

MATLAB uses the `textread` function to read txt files. The syntax is as follows:

data = textread(filename, format, headerlines, delimiter)

Where:

- `filename`: The path and name of the txt file.
- `format`: Specifies the data format, such as '%f' for reading floating-point numbers.
- `headerlines`: The number of lines to skip at the beginning of the file, defaulting to 0.
- `delimiter`: Specifies the delimiter, defaulting to a space.

For example, to read a txt file named `data.txt` with data separated by spaces:

data = textread('data.txt', '%f', 0, ' ');

## 2. Data Import and Processing

### 2.1 Data File Reading

MATLAB provides various functions to read data files of different formats, including txt, csv, xls, etc.

#### 2.1.1 Reading txt Files

Txt files are simple text files where each line contains a data point. The `textscan` function can be used to read txt files:

data = textscan(filename, '%f', 'Delimiter', ',');

Where:

- `filename` is the name of the txt file.
- `'%f'` specifies the data type as floating-point numbers.
- `'Delimiter', ','` specifies the delimiter as a comma.

#### 2.1.2 Reading csv Files

Csv files are text files with values separated by commas. The `csvread` function can be used to read csv files:

data = csvread(filename);

Where:

- `filename` is the name of the csv file.

### 2.2 Data Preprocessing

Data preprocessing is a crucial step in data analysis, as it can remove noise and outliers from the data, enhancing its quality.

#### 2.2.1 Handling Missing Values

Missing values refer to data points that are missing from the dataset. Methods to handle missing values include:

- Deleting missing values: If there are few missing values, they can be deleted.
- Filling missing values: Missing values can be filled using methods such as mean, median, or mode.

#### 2.2.2 Handling Outliers

Outliers are data points that are significantly different from the others. Methods to handle outliers include:

- Deleting outliers: If outliers are clearly incorrect, they can be deleted.
- Transforming outliers: Outliers can be transformed using logarithmic transformations or other methods.
- Retaining outliers: If outliers are meaningful, they can be retained.

**Code Example:**

% Reading txt file
data = textscan('data.txt', '%f', 'Delimiter', ',');

% Handling missing values
data(isnan(data)) = mean(data);

% Handling outliers
data(data > 100) = 100;

**Logical Analysis:**

- First, the `textscan` function is used to read the txt file.
- Next, the `isnan` function detects missing values and fills them with the mean.
- Finally, a logical judgment statement is used to detect outliers and limit them to 100.

## 3.1 Descriptive Statistics

Descriptive statistics aims to summarize and describe data, providing an overview of the overall characteristics of the data. In MATLAB, descriptive statistics functions can calculate various statistics, including mean, median, standard deviation, v