R Language for The Project Management Course — AUG University “part 2”

Welcome back! This is the 2nd chapter of the series explaining the project management course for AUG students - The computer system engineering department.

Descriptive Statistics:

First, what is descriptive statistics?

Descriptive statistics summarize or describe the characteristics of a data set. Data set is a collection of responses or observations of a sample or entire population.

Types of descriptive statistics

There are 3 main types of descriptive statistics:

1. The distribution: concerns the frequency of each value.
2. The central tendency: concerns the averages of the values.
3. The variability: concerns how to spread out the values are.

- Central tendency:

Measures of central tendency estimate the average of the data set. The mean, median, and mode are 3 ways of finding the average.

we will talk about mean and median:

Mean:

we calculate the mean by dividing the sum of all values by the total number of values.

data set : 11 ,22, 33, 44, 55, 66
mean is : (11+22+33+44+55+66) /6

In R, we can calculate the mean using the mean(x) function, x is a vector.

Median:

we can calculate the median in R using median(x)

data set : 11 ,22, 33, 44, 55, 66
median is :  n = 6 
             since n is even > median is (x(n/2)+ x((n/2) + 1))/2
                             > median is (x 3 + x 4) /2 
                             > median is (33 + 44) /2
                             > median is  38.5

another example:

data set : 11 ,22, 33, 44, 55, 66, 77
median is :  n = 7
             since n is odd > median is (x(n+1))/2
                             > median is x(7 +1) /2
                             > median is x(4)
                             > median is  44

- Measures of variability

Standard deviation:

is a measure of the amount of variation in a set of data. In R we use sd(x) to measure the standard deviation.

Variance

Is the average of squared deviations from the mean. In R we measure it by using var(x)

We can calculate the standard deviation and variance using:

photo -1-

data is 10, 20, 30
step 1: Mean is (30 +20 +10)/3 = 20
                -----------------------------------
step2:
Raw data              Deviation from mean      Squared deviation
10                         10 - 20 = -10         (-10)^2 = 100
20                         20 - 20 = 0               0
30                         30 - 20 = 10             100
                            sum = 0                sum = 200
Now to calucate the variance you have to calucate√(sum of sqared deviation / n-1) then the vairnce(result ^2)
Step 3: sum of sqared deviation / n-1 = 200/2 = 100
Step 5: √100= 10

this means that each score deviates from the mean by 10 points.

Now varince is 10^ 2 = 100

See you in the next part;)

Resources:

An introduction to descriptive statistics

What is R?