First Session- Statistical Analysis

The first session of Statistical Analysis for Business Decisions introduced us to the software "Statistical Package for Social Sciences (SPSS) " which is used for statistical analysis of data. The lecture gave us an insight on the following topics:

Definition of Statistics:

Statistics is a set of concepts, rules, and procedures that help us to organize numerical information in the form of tables, graphs, and charts. It helps us understand statistical techniques and underlying decisions that affect our lives and well-being and make informed decisions.

Data:

Data is a collection of facts, observations, and information that come from investigations.

Measurement scales:

It is important, in statistical analysis, to know about the different scales of measurement. The scale is chosen depending on the information that the data is intending to represent. The four scales of measurement of data are nominal, ordinal, interval, and ratio. Each plays a different, yet very important role in the world of statistics.

INTERVAL
The interval data measurement scale is used for numeric data that is expressed in intervals of some kind of fixed measurement. For example, if a school is classifying students based on the results of their scores, then they could say that student A scored a 25 and student B scored a 30. After stating that, they could see that student B scored 5 points higher then student A.
 

ORDINAL
The ordinal data measurement scale is used when you want to classify information based on a necessary, specific order or rank. Like nominal data, the information can be expressed either in a numeric or nonnumeric way. For example, if a school wants to classify its students based on the year of schooling that they are in, they could label the groups as freshman, sophomore, junior, etc. Additionally, they could give numeric codes to the groups by classifying ones as freshmen, twos as sophomores, and so on.
 

NOMINAL

The nominal data measurement scale is used for data that is expressed with the purpose of identifying some kind of attribute. It can be expressed using either a numeric code or some kind of non-numeric label. For example, if a university wants to classify its students into groups based on their major, they can express the information by labelling the groups with their respective major names (business, communications, health care, etc). Additionally, they can give the groups a numeric label, for example the number one could represent business, two for communications, and so on.
 

RATIO

The ratio data measurement scale is used to express the ratio of some of the values of interval data. For example, is a school is trying to create a data set of how many credit hours students have taken, they could state that student A has taken 10 hours, while student B has taken 20 hours. Using the ratio method, they could say that student B has taken twice as many credit hours of classes then student A took.

TYPES OF DATA

• Quantitative data, sometimes known as Measurement data , the result of using some instrument to measure something (e.g., test score, weight)
• Qualitative data also referred to as frequency or categorical data.  Things are grouped according to some common properties and the number of members of the group are recorded (e.g., males/females, vehicle type).
• Dependent variable -The presumed effect in an experimental study. The values of the dependent variable depend upon another variable, the independent variable. Strictly speaking, “dependent variable” should not be used when writing about non-experimental designs.
• Independent variable The presumed cause in an experimental study. All other variables that may impact the dependent variable are controlled. The values of the independent variable are under experimenter control. Strictly speaking, “independent variable” should not be used when writing about nonexperimental designs.

Variable - property of an object or event that can take on different values.  For example, college major is a variable that takes on values like mathematics, computer science, English, psychology, etc.

·        Discrete Variable - a variable with a limited number of values (e.g., gender (male/female), college class (freshman/sophomore/junior/senior).

·        Continuous Variable - a variable that can take on many different values, in theory, any value between the lowest and highest points on the measurement scale.

·        Continuous- continuous variable: It contains large values as well as fractional values. Eg.: Salary of employees.

·        Continuous-discrete variable: It includes only large values. Eg.: number of pieces an apple can be cut into.

Name- Payal Singh

Roll no. - 2013196

Team- Nupur Mandhyan, Omkar Gujar, Radhika Agarwal, Pranav Sharma