it includes the following Introduction/Context Advantages Separate codes for 1.Mean 2.Median 3.Mode 4.Range 5. co- efficient of range 6. Standard Deviation 7.Variance overview and Conclusion

1. MML
MICRO PROJECT

2. CONTEXT
Context – Building a Basic statistic calculator
In this project, the goal is to create a basic statistics calculator using the R programming language. The calculator will
serve as a versatile tool for quickly analyzing datasets by providing essential statistical measures. This project is
particularly valuable for individuals looking to strengthen their proficiency in both statistics and R programming.
The calculator's user interface will allow users to input their dataset, which can be either manually entered or
imported from an external CSV file. Once the data is provided, the calculator will offer a range of statistical functions
including mean, median, mode, variance, standard deviation, and range. Additionally, the calculator will incorporate
error handling mechanisms to ensure that the input data is valid.
A key emphasis will be placed on creating clean and understandable output displays, making it easy for users to
interpret the results. The project may also include an optional enhancement: the visualization of data through
histograms or box plots. This would add an extra layer of functionality and user-friendliness.
Overall, this project aims to empower users with a practical tool for conducting basic statistical analyses in R,
contributing to their understanding of statistics and proficiency in the language. Through a structured development
process, the final product will be reliable, user-friendly, and versatile for a wide range of statistical applications.

3. ADVANTAGES
 Skill Development: This project helps you improve your proficiency in R, a widely-used language in data analysis and statistics.
 Statistical Understanding: Implementing statistical functions from scratch enhances your understanding of these concepts, as
you'll need to grasp the underlying mathematics.
 Customization: You have control over the features and functionalities of the calculator, tailoring it to meet specific needs or
preferences.
 Practical Application: It provides a practical application of programming skills in a domain (statistics) that has real-world
relevance in various fields.
 Error Handling Practice: Implementing error handling mechanisms strengthens your ability to anticipate and manage unexpected
situations, an important skill in programming.
 Versatility: The calculator can handle different types of datasets, making it a versatile tool for a wide range of statistical analyses.
 User Interaction: If you build a graphical user interface (GUI), it enhances user experience, making it more accessible for those who
might not be comfortable with command-line interfaces..
 Problem-Solving: You'll encounter challenges along the way, and solving them will further hone your problem-solving abilities.

4. CODE FOR CALCULATING MEAN
Input
calculate_mean <- function(data) sum(data) / length(data)
# Example usage
data <- c(2, 3, 5, 7, 11, 13)
# Calculate mean and print the result
print(paste("Mean:", calculate_mean(data)))
Output
"Mean: 7"

5. CODE FOR CALCULATING MEDIAN
Input –
calculate_median <- function(data) {
sorted_data <- sort(data)
n <- length(sorted_data)
ifelse(n %% 2 == 1, sorted_data[(n + 1) / 2], mean(sorted_data[c(n/2, n/2 + 1)]))
}
# Example usage
data <- c(2, 3, 5, 7, 11, 13)
# Calculate median and print the result
print(paste("Median:", calculate_median(data)))
Output –
"Median: 6"

6. CODE FOR CALCULATING MODE
Input –
calculate_mode <- function(data) {
table_data <- table(data)
mode <- as.numeric(names(table_data[table_data == max(table_data)]))
if(length(mode) == length(data)) "No mode" else mode
}
data <- c(2, 3, 5, 7, 2, 11, 13, 2)
# Calculate mode and print the result
mode_result <- calculate_mode(data)
print(paste("Mode:", mode_result))
Output –
"Mode: 2"

7. CODE FOR CALCULATING RANGE
Input –
calculate_range <- function(data) diff(range(data))
#Example usage
data <- c(2, 3, 5, 7, 11, 13)
range_result <- calculate_range(data)
print(paste("Range:", range_result))
Output –
"Range: 11"

8. CODE FOR CALCULATING CO- EFFICIENT OF RANGE
Input –
calculate_coefficient_of_range <- function(data) diff(range(data)) / sum(range(data))
# Example usage
data <- c(2, 3, 5, 7, 11, 13)
# Calculate coefficient of range and print the result
cor_result <- calculate_coefficient_of_range(data)
print(paste("Coefficient of Range:", cor_result))
 Output-
"Coefficient of Range: 0.611111111111111"

9. CODE FOR CALCULATING STANDARD -
DEVIATION
Input -
calculate_standard_deviation <- function(data) {
sd_value <- sqrt(var(data))
return(sd_value)
}
data <- c(2, 3, 5, 7, 11, 13)
sd_result <- calculate_standard_deviation(data)
print(paste("Standard Deviation:", sd_result))
Output -
"Standard Deviation: 4.13118223595458"

10. CODE FOR CALCULATING VARIANCE
Input –
calculate_variance <- function(data) {
variance_value <- var(data)
return(variance_value)
}
data <- c(2, 3, 5, 7, 11, 13)
variance_result <- calculate_variance(data)
print(paste("Variance:", variance_result))
Output –
"Variance: 17.1388888888889"

11. OVERVIEW
The Basic Statistics Calculator is a simple R program designed to perform fundamental statistical
calculations on a given dataset. It provides functions for calculating the mean, median, mode,
variance, standard deviation, and range of a set of numeric values.
1. Mean-
 Function: calculate_mean
 Description: Calculates the mean of a dataset.
 Parameters:
data: A numeric vector containing the dataset.

12. 2. Median –
Function: calculate_median
Description: Calculates the median of a dataset.
Parameters:
data: A numeric vector containing the dataset.
3. Mode –
Function: calculate_mode
Description: Calculates the mode of a dataset.
Parameters:
data: A numeric vector containing the dataset.

13. CONCLUSION : -
This basic statistics calculator project serves as a valuable tool for performing fundamental
statistical analyses on numeric datasets using the R programming language. The calculator
includes functions for calculating measures such as mean, median, mode, variance, standard
deviation, range, and coefficient of range. It also incorporates error handling to ensure that
input data is valid.
The project is designed to be user-friendly, providing clear instructions for inputting data and
obtaining accurate statistical results. Additionally, the code is well-organized and documented,
making it easy for users to understand and utilize the functions effectively.
While this calculator covers basic statistical measures, it may not address more specialized
cases. Users seeking more advanced analyses are encouraged to explore specialized statistical
packages. Overall, this project is a valuable resource for individuals looking to quickly obtain
key statistics for their datasets, enhancing their understanding of statistics and proficiency in
R programming.