QSO 510 Quantitative Analysis for Decision MakingHomework Assignment 1

Please answer all questions

Question 1

What is all about the quantitative analysis for decision making course?

Question 2

Earlier on, data analysis was based on sample (small) data to infer the big data (population) instead of

dealing with the big data itself. However, nowadays, it has been possible to deal with the big data.

Explain very briefly, why it was not possible before to deal with big data but it is now possible.

Question 2.

It is very important to understand your data before you decide on the method of analyzing the data.

Please list the characteristics of data that you have learned so far.

Question 3.

List any three important decisions in business that may require evidences from the data before they are

made.

Question 4

What is the difference between observed and estimated data? Provide an example

Question 5

You just agreed to take position of Economic Adviser at InoSmart Inc. The manager of the company

asked you to evaluate salaries paid by the company to its employees last year. The manager delivers to

you the aggregate monthly salaries data as follows:

Months

Aggregate

Salaries

January

7434.30

February

7053.20

March

6795.00

April

7802.40

May

5019.70

June

6944.00

July

8196.55

August

6384.38

September

7911.11

October

6553.37

November

7107.64

December

8050.50

Questions 1.

Type Yes (to agree) or No (to disagree) with each of the following:

The data provided is

(a) Qualitative only …………. …

(b) Time series ……………… (c) Cross-sectional ………..

(d) Continuous ……………….

(c) Quantitative only…………….. (d) Discrete ………….

(e) Sample ……………..

(f) Population ……………. ( g) estimated …………….

(h) Observed ……………..

……….

(I) both qualitative and quantitative (non-numerical and numerical)

Normally distributed or other distributions ………….

What is statistics?

A pool of techniques/procedures that can be used to produce a

meaningful information out of raw data with an objective of providing

evidence for decision making.

What is statistics?

A pool of techniques/procedures that can be used to produce a

meaningful information out of raw data with an objective of providing

evidence for decision making.

Data

techniques/procedures(analysis)

information

decision

Data Literacy

What is data?

Facts collected for analysis or references

What is data?

Facts collected for analysis or reference

Example:

What is data?

Facts collected for analysis or reference

Example:

– Sales revenue

– profits

– Population

– Etc.

Data

Sources of Data:

– Primary data

– Secondary data

Primary data (Raw Data) – data collected/observed directly from a source

(firsthand experience) – Not processed

Methods of primary data collection:

– Surveys: interviews / questionnaires/ etc.

– Observations

– Experiments

Data

Secondary data– data collected/observed previously by someone other

than the user(s).

Sources:

– Internet

– Websites

– Organizational records

– Published sources

– Etc.

Data

Data Distribution (data set)

What is Data Distribution ?

– A collection of data (data base).

– A collection of information that is organized so that it can easily be

accessed, managed, and updated.

Data

Organizing data

1. Tabular form – data is arranged in tabular form, with rows and

columns

2. Data can be arranged in ascending or descending order

3. In a pivot table (pivot table – data summarization tool found in data

visualization programs such as excel software)

– A pivot table can automatically summarize data by organizing,

sorting, counting, providing total or give the average of the data in a

distribution.

Data

Table 1. Data set (Hypothetical)

Years of

Individual Age

Gender Education

John

29 M

Mary

36 F

Adam

24 M

Shawn

34 M

Bill

28 M

Bob

40 M

Sabina

60 F

Kelly

49 F

Years of

Experience Salary

4

2 50,000

3

12 75,000

5

2 51,000

4

5 65,000

2

10 68,000

3

8 60,000

3

6 59,000

4

5 64,000

Variables

Observations

Data

Variable (attribute)

Characteristic of an item in the distribution.

From the table above – Gender, Years of Education, Years of Experience

and Salary are variables.

Observation

List of a variable values.

E.g., observations in terms of variable “Age” in table 1 above are; 29. 36,

24, 34, 28, 40, 60 and 49

Characteristics of data (types / forms/etc.)

Characteristics of data (types / forms/etc.)

Data can either be:

– Numerical

– Non-numerical

Numerical (quantitative) – data that is expressed with digits.

Example 1: 0, 1, 2, 3, 4 (integers)

Example 2: 0.23, 0.5, 2.84, 0.0007 (decimals)

Non numerical (qualitative/ categorical) – data that is expressed with

words, letters or categories

Example 1: gender, states, countries, opinions, A, B, C, D, etc.

Example 2: category of values – 1 – 10, 10 – 20, 20- 30., category people

in terms of gender, etc.

Numerical data (quantitative)

Numerical data (quantitative)

Numerical Data can be:

– Discrete

– Continuous

Discrete: Data that can be counted or has a finite ending. It can only take

certain values (integers),

Example 1: 0, 1, 2, 3, 4, etc.

Example 2: Number of children, number of cars, number of houses, etc.

Continuous: data that has infinite number of possible values (decimals)

Example 1: 0.23, 0.5, 2.84, 0.0007

Example 2: Temperature, heights, distance, etc.

Numerical data (quantitative)

Discrete or continuous data can be:

– Observed

– Estimated

Observed data: obtained from the study (real data).

Example: the performance of students in the last term examination, etc.

Estimated data : data that is predicted (projected).

Example 1: the projected performance of students in the upcoming class.

Numerical data (quantitative)

Observed or estimated data can be:

– A Population

– A Sample

Population: Is the entire distribution of data (all entities of interest)

Example: people, overall salaries, or any other values in aggregate.

Sample: Is a subset of a population, in most cases, randomly selected to

represent the characteristics of a population as a whole

Numerical data (quantitative)

Sample selection methods:

-Simple Random Sampling (SRS) – variable or a value is selected by

chance.

-Systemic sampling – one of the first n number is selected randomly, then

every nth number after the first one will be selected.

-Stratified sampling – distribution is divided into strata and then random

samples are taken from each stratum

Numerical data (quantitative)

A population or a sample data can be:

-Cross sectional

– Time series

Cross sectional data: data collected at the same point of time.

Examples: Someone’s salary, current number of population in a town,

current number of children in a family, someone’s education level, etc.

Time series data: sequence of measurements of the same variable

collected overtime.

Examples: monthly sales, population growth, etc.

Numerical data (quantitative)

Cross sectional or time series data can be:

-Normally distributed

– Other distributions

– Normally distributed data: data that is symmetrical about the mean.

Values are equally likely to plot either above or below the mean (bellshaped distribution).

– Types:

–

Standard normal distribution

–

Non standard normal distribution

Numerical data (quantitative)

Standard normal distribution – It is the distribution that occurs when

a normal random variable has a mean of zero and a standard deviation

of one.

– The normal random variable of a standard normal distribution is

called a standard score or a z score.

Non-standard normal distribution – a distribution that occurs when

a normal random variable has a mean other than zero and

a standard deviation other than one.

Numerical data (quantitative)

Other distributions – distributions of data that skews either to the left or

to the right

A distribution is skewed if one of its tails is longer than the other ( when

the mean is pulled to either side).

– A positive skew. Distribution has a long tail in the positive direction.

– A negative skew – Distribution has a long tail in the negative direction.

Non numerical data (qualitative/categorical)

Non numerical data (qualitative/categorical)

Non-numerical data can either be:

– Nominal or

– Ordinal

Nominal data – non numerical data is nominal if there is no natural

ordering of its possible values.

Example: gender, state, countries, names of people, etc.

Ordinal data – non numerical data is ordinal if there is natural ordering of

its possible values.

Example: Education levels, priorities, ranks, etc.

Non numerical data (qualitative/categorical)

Nominal or ordinal data can be:

– A population or

– A sample

Data Analysis (techniques/procedures)