What is statistics?
Why do we need to study it?
What would be the consequences of conducting wrong statistical statistics?
These are the few questions one thinks of when he is confronted with the word 'Statistics'.
We begin with the discussion of the word statistics:
Statistics is the study of the collection, organisation, analysis, interpretation and presentation of data. It involves the planning of data collection through designs of data surveys and experiments.
The need to study it:
To be able to effectively conduct research, to be able to read and evaluate journal articles, to further develop critical thinking and analytic skills, to act as an informed consumer, and to know when you need to hire outside statistical help.
Consequences:
It is extremely important for a researcher to know what statistics they want to use before they collect their data. Otherwise data might be collected that is uninterpretable. Unfortunately, when this happens it results in a loss of data, time, and money. Also it may be very hazardous to the business.
SPSS: Statistical Package for Social Sciences, a comprehensive and flexible statistical analysis and data management solution. It can take data from almost any type of
file and use them to generate tabulated reports, charts and plots of distribution and trends, discriptive statistics and conduct complex statistical analysis (SA).
There are various techniques used in SA:
1. Univariate : Analysis of single variable.
eg. pie chart, bar graph, line graph, frequency table.
2. Bivariate : Analysis of two variables.
eg. bar graph, pie charts, frequency table.
3. Multivariate : Analysis of multiple variables.
eg. Regression analysis
Types of data :
1. Primary data: Data that is collected by oneself.
2. Secondary data: Data that is available via some other source or is derived from primary data.
The steps involved in statistical/data analysis :
- Processing/Analysis
- Observation
- Interpretation
- Strategy
There are different types of variables:
1. Continuous variable: They have very large values.
Further subdivided into :
-Continuous continuous variable: They can have fractional values. eg. salary, interest rate etc.
-Discrete continuous variable: Cannot have fractional values. eg. If we flip a coin and count the heads, the number would be any integer value between zero and +infinity.
2. Category Variable: They have few values. eg. gender (male, female), marital status (married, unmarried, divorced, widowed) etc.
Other classification for variables:
Nominal numbers: A number that is just for name keeping and has no specific meaning. In other words they dont give any information.
eg. if gender is represented in terms of numbers i.e. Male =1, Female=2, then the numbers 1 and 2 are nominal numbers.
Ordinal numbers: Numbers that have an order i.e. the order of the numbers is known but not the difference. eg. A is taller than B (here we dont know by how much)
Scale numbers: The numbers are in order and the exact difference between them is also known. eg. Age, weight etc.
Also hands on for the tool SPSS was given by doing statistical analysis of a case of the telephonic usage of various individuals in different age groups using a variety of cellular services.
References:
Wikipedia
Applied Business Statistics by Ken Black
Journal for Statistical Analysis
Written by: Pranshu Agrawal
Group Members:
Nishant Renjith
Pooja Shukla
Pranshu Agrawal
Prateek Jain
Priyanka Sudan
Why do we need to study it?
What would be the consequences of conducting wrong statistical statistics?
These are the few questions one thinks of when he is confronted with the word 'Statistics'.
We begin with the discussion of the word statistics:
Statistics is the study of the collection, organisation, analysis, interpretation and presentation of data. It involves the planning of data collection through designs of data surveys and experiments.
The need to study it:
To be able to effectively conduct research, to be able to read and evaluate journal articles, to further develop critical thinking and analytic skills, to act as an informed consumer, and to know when you need to hire outside statistical help.
Consequences:
It is extremely important for a researcher to know what statistics they want to use before they collect their data. Otherwise data might be collected that is uninterpretable. Unfortunately, when this happens it results in a loss of data, time, and money. Also it may be very hazardous to the business.
SPSS: Statistical Package for Social Sciences, a comprehensive and flexible statistical analysis and data management solution. It can take data from almost any type of
file and use them to generate tabulated reports, charts and plots of distribution and trends, discriptive statistics and conduct complex statistical analysis (SA).
There are various techniques used in SA:
1. Univariate : Analysis of single variable.
eg. pie chart, bar graph, line graph, frequency table.
2. Bivariate : Analysis of two variables.
eg. bar graph, pie charts, frequency table.
3. Multivariate : Analysis of multiple variables.
eg. Regression analysis
Types of data :
1. Primary data: Data that is collected by oneself.
2. Secondary data: Data that is available via some other source or is derived from primary data.
The steps involved in statistical/data analysis :
- Processing/Analysis
- Observation
- Interpretation
- Strategy
There are different types of variables:
1. Continuous variable: They have very large values.
Further subdivided into :
-Continuous continuous variable: They can have fractional values. eg. salary, interest rate etc.
-Discrete continuous variable: Cannot have fractional values. eg. If we flip a coin and count the heads, the number would be any integer value between zero and +infinity.
2. Category Variable: They have few values. eg. gender (male, female), marital status (married, unmarried, divorced, widowed) etc.
Other classification for variables:
Nominal numbers: A number that is just for name keeping and has no specific meaning. In other words they dont give any information.
eg. if gender is represented in terms of numbers i.e. Male =1, Female=2, then the numbers 1 and 2 are nominal numbers.
Ordinal numbers: Numbers that have an order i.e. the order of the numbers is known but not the difference. eg. A is taller than B (here we dont know by how much)
Scale numbers: The numbers are in order and the exact difference between them is also known. eg. Age, weight etc.
Also hands on for the tool SPSS was given by doing statistical analysis of a case of the telephonic usage of various individuals in different age groups using a variety of cellular services.
References:
Wikipedia
Applied Business Statistics by Ken Black
Journal for Statistical Analysis
Written by: Pranshu Agrawal
Group Members:
Nishant Renjith
Pooja Shukla
Pranshu Agrawal
Prateek Jain
Priyanka Sudan
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