Pseudo code: Input a list of employee names and salaries, and determine the
mean (average) salary as well as the number of salaries above and
below the mean
I will show a brief pseudocode for you to expand on. You can post the improved version for discussions.
// data input
total <- 0
while(more employees)
{
input employee.name, employee.salary
total <- total+employee.salary
increment count
}
if count equals zero, quit
// calculate mean
mean <- total/count
// calculate above and below mean
highCount <- 0
equalCount <-0
lowCount <- 0
while(more employees)
{
compare employee.salary with mean and increment highCount, equalCount or lowCount according to the case
}
// Output
Print highCount, equalCount and lowCount.
To solve this problem, you can use the following pseudo code:
1. Start by initializing variables to keep count of the total number of salaries, the sum of all salaries, and the number of salaries above and below the mean.
2. Input a list of employee names and salaries.
3. Iterate over each salary in the list:
- Add the salary to the sum.
- Increment the count of the total number of salaries.
4. Calculate the mean salary by dividing the sum by the total number of salaries.
5. Iterate over each salary in the list again:
- If the salary is above the mean, increment the count of salaries above the mean.
- If the salary is below the mean, increment the count of salaries below the mean.
6. Output the mean salary, the count of salaries above the mean, and the count of salaries below the mean.
Here is an example implementation in Python:
```
# Step 1
total_salaries = 0
sum_salaries = 0
above_mean = 0
below_mean = 0
# Step 2
employee_salaries = [
{"name": "John", "salary": 50000},
{"name": "Jane", "salary": 60000},
{"name": "Mike", "salary": 45000},
{"name": "Emily", "salary": 70000},
{"name": "Tom", "salary": 55000}
]
# Step 3
for employee in employee_salaries:
sum_salaries += employee["salary"]
total_salaries += 1
# Step 4
mean_salary = sum_salaries / total_salaries
# Step 5
for employee in employee_salaries:
if employee["salary"] > mean_salary:
above_mean += 1
elif employee["salary"] < mean_salary:
below_mean += 1
# Step 6
print("Mean Salary:", mean_salary)
print("Salaries above the mean:", above_mean)
print("Salaries below the mean:", below_mean)
```
This will give you the mean salary as well as the number of salaries above and below the mean.
1. Start by defining a function that takes a list of employee names and salaries as input.
2. Initialize variables to keep track of the sum of salaries, the number of salaries above the mean, the number of salaries below the mean, and the total number of salaries.
3. Iterate through each salary in the list and add it to the sum of salaries.
4. Calculate the mean salary by dividing the sum of salaries by the total number of salaries.
5. Iterate through each salary in the list again and compare it to the mean salary.
6. If the salary is above the mean, increment the counter for salaries above the mean.
7. If the salary is below the mean, increment the counter for salaries below the mean.
8. Print the mean salary, the number of salaries above the mean, and the number of salaries below the mean.
Here is a sample implementation in Python:
```python
def calculate_salary_stats(employee_salaries):
total_salaries = len(employee_salaries)
sum_of_salaries = 0
above_mean = 0
below_mean = 0
# Calculate the sum of salaries
for salary in employee_salaries:
sum_of_salaries += salary
# Calculate the mean salary
mean_salary = sum_of_salaries / total_salaries
# Determine the number of salaries above and below the mean
for salary in employee_salaries:
if salary > mean_salary:
above_mean += 1
elif salary < mean_salary:
below_mean += 1
# Print the results
print("Mean Salary:", mean_salary)
print("Number of Salaries Above Mean:", above_mean)
print("Number of Salaries Below Mean:", below_mean)
```
To use the function, you can pass a list of employee names and salaries as an argument:
```python
employee_salaries = [3000, 5000, 4000, 2000, 6000]
calculate_salary_stats(employee_salaries)
```
This will output:
```
Mean Salary: 4000.0
Number of Salaries Above Mean: 2
Number of Salaries Below Mean: 2
```