RESEARCH-STATISTICAL-TREATMENT-OF-DATA.pptx
- 1. STATISTICAL TREATMENT OF DATA What is statistical treatment of data? Statistical treatment of data is when you apply some form of statistical method to a data set to transform it from a group of meaningless numbers into meaningful output.
- 2. Types of statistical analysis: 1. Descriptive Statistics Fundamentally, it deals with organizing and summarizing data using numbers and graphs. It makes easy the massive quantities of data for intelligible interpretation even without forming conclusions beyond the analysis or responding to any hypotheses. 2. Inferential Statistics The inferential statistical analysis basically is used when the inspection of each unit from the population is not achievable, hence, it extrapolates, the information obtained, to the complete population.
- 3. 3. Predictive Analysis Predictive analysis is implemented to make a prediction of future events, or what is likely to take place next, based on current and past facts and figures. 4. Prescriptive Analysis The prescriptive analysis examines the data to find out what should be done, it is widely used in business analysis for identifying the best possible action for a situation.
- 4. 5. Causal Analysis In general, causal analysis assists in understanding and determining the reasons behind “why” things occur, or why things are as such, as they appear.
- 5. Statistical treatment of data involves the use of statistical methods such as: MEAN PERCENTAGE MEDIAN STANDARD DEVIATION FREQUENCIES VARIANCE REGRESSION DISTRIBUTION RANGE
- 6. • mean – denotes the equal distribution of values for a given data set. •median – a statistical measure that determines the middle value of a dataset listed in ascending order. • frequencies - a measure of the number of occurrences of a particular score in a given set of data.
- 7. •regression – is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. • percentage – is a display of data that specifies the percentage of observation that exist for each data point or grouping of data points.
- 8. •standard deviation – is the measure of dispersion of a set of data from its mean. • variance - a measure of variability. - tells the degree of spread in your data set. •distribution range – is the spread of your data from the lowest to the highest value in the distribution
- 9. EXAMPLE OF MEAN, MEDIAN , VARIANCE, STANDARD DEVIATION AND DISTRIBUTION RANGE STUDENTS EXAM SCORE Xi Xi - X (Xi - X)2 JUSHUA 85 85 7 49 ANDY 76 76 -2 4 ERIC 73 73 -5 25 PRINCESS 80 80 2 4 MARA 72 72 -6 36 CRISTINE 81 81 3 9 MARK 79 79 1 1 546 128 X = = 78 MEAN( X) = 78 MEDIAN = 79 Middle value
- 10. V = = 21.33 S. D = = 4.67 RANGE= 85 - 72 = 13
- 11. EXAMPLE OF FREQUENCIES AND PERCENTAGE BLOOD TYPE FREQUENCY PERCENTAGE(%) A 5 25% B 5 25% O 6 30% B 4 20% A, O ,A ,B ,B, AB ,B, B, O, A, O, O,O, AB, B, AB, AB, A, O ,A TO GET THE PERCENTAGE (%) here is the fOrmula; % = x 100 % = = 0.25 x 100 = 25%