Upcoming SlideShare
×

# Presentation1

192 views

Published on

Published in: Education, Technology
0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

• Be the first to like this

Views
Total views
192
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
3
0
Likes
0
Embeds 0
No embeds

No notes for slide

### Presentation1

1. 1. Analysis
2. 2.  Analysis means critical evaluation of the assembled and grouped data for studying the characteristics of the object under study and for determining the patterns of relationships among the variable relating to it.  Both quantitative & qualitative methods are used.  However, social research most often requires quantitative analysis involving the application of various statistical techniques.
3. 3. Purpose:  It summarizes large mass of data into understandable and meaningful form. E.g. Sensex, Nifty, GDP, PCI etc.  Statistics makes exact description possible E.g. percentage of literate among males and females, percentage of degree holder among males and females.
4. 4. Statistical analysis facilitates identification of the casual factors underlying complex phenomena  E.g. what are the factors that determines a variable like labor productivity or academic performance of student. It aids the drawing of reliable inferences from observational data.  E.g. what would be the growth rate of industrial production during the coming years
5. 5.  Making estimation or generalization from the result of sample survey.  Statistical analysis is useful for assessing the significance of specific sample result under assumed population condition. This type of analysis is called Hypothesis testing.
6. 6. Types of statistical analysis of data • Descriptive analysis • Inferential analysis •Computerized analysis
7. 7. Descriptive analysis:  This analysis provides us with profiles of organization, work groups, persons and other subjects of a multiple of characteristics such as size, composition, efficiency, preference etc. Inferential Analysis:  It involves an estimate of the accuracy of the inferences called reliability. The reliability is expressed in terms of probability. Computerized analysis:  Analysis with the help of software packages. E.g. R, Excel, SPSS etc.
8. 8. Approach to statistical analysis First approach, Descriptive analysis: Construction of statistical distribution & calculation of simple measures.  E.g. averages, percentages and measures of dispersion Some average value that represents the distribution is computed by using the appropriate measures of central tendency i.e. Mode, Median and Arithmetic mean
9. 9. Second aspect, comparison of two or more distribution. Measures of dispersion are used to get complete indication of the nature of distribution.  Range, standard deviation & co efficient of variation. To study relationship among variables,  Coefficient of correlation, partial & multiple correlation & Regression are used for prediction purpose.
10. 10.  Other methods like ratio, proportion and percentage are used for comparison of groups of unequal size. E.g. liquidity ratio is used for inter/ intra firm comparison.  Third aspect, Coefficients of correlation, partial and multiple correlation and regression are used for to find out the nature of relationship among variables.
11. 11.  In survey research, result of sample may have some errors. Parametric test of significance such as “ t” test, “f” test etc and non parametric tests like chi-square test, K S test, sigh test etc are used for this purpose.  These test are also used for testing the Hypothesis relating to variable.
12. 12. Types of statistical measures  Measures of central tendency: mean, mode, median.  Measures of dispersion: ranges, deviation, standard deviation.  Measures of relation: correlation, regression, chi square test, factor analysis, discriminant analysis, cluster analysis, cannonical analysis.  Analysis of variance: one / two way of ANOVA, MANOVA .  Time series: seasonal, cyclical, trend and erratic
13. 13. Measures of central tendency- mean, median and mode Descriptive statistics that identify which value is most typical for the data set. Mean:  Adding all of the scores in a data set together and dividing by the number of scores. e.g. Height/cm; 153, 146, 151, 170, 160 Added together =780 780 divided by 5 =156cm The mean =156
14. 14. Advantages: The most powerful measure of central tendency because it is made up from all of the scores in the data set. Disadvantages:  Any outliers can distort the mean.  Sometimes the mean does not make sense in terms of the data set e.g. the number of children per family in the UK = 2.4
15. 15. The Median:  When all of the scores in a data set have been put in order, the median is the central number in the set. E.g. Age of employees/years; 21, 29, 34, 44, 56 The median age of the employees is 34. Advantages:  The median is less effected by extreme scores than the mean Disadvantages:  It is not suited to being used with small sets of data especially if containing widely varying scores e.g. 7, 8, 9, 102, 121 where the median would be 9. A more real median would be 60!
16. 16. The Mode The most frequent occurring number in the data set when put in order. e.g. 3, 5, 6, 6, 6, 8, 9. Mode = 6 The data set could be Bimodal (two modes) or even multimodal. Advantages: The mode is normally unaffected by extreme scores and may give an idea of how often something is occurring e.g. what size of shoes sell most when ordering stock Disadvantages: The mode may not be central measure, and a set of data may not have a most frequent