application of statistics.pptx

Applications of statistics in various fields

1. St. Peter’s Institute of Higher Education and Research (Declared Under section 3 of the UGC Act. 1956) Avadi , Chennai – 600 054. Tamil Nadu . Website: www.spiher.ac.in   Department of Mathematics Day: 8 Date: 11.04.2020 APPLICATIONS OF STATISTICS IN VARIOUS FIELDS Department of Mathematics, SPIHER

2. What is statistics ? Statistics is a collection of data and arrangement of facts. Statistics refers to Numerical facts. It also signifies the method or methods of dealing with Numerical facts. Statistics are numerical statements of facts in any department of inquiry. Department of Mathematics, SPIHER

3. Application of Statistics Application of statistics are in various Fields………. Biostatistics: data from: Medicine, Biological sciences (business, education, psychology, agriculture, economics...) Daily Life Business Computer Science Environmental Science Agriculture Social Science Psychology etc.., Department of Mathematics, SPIHER

4. Application of Statistics in DAILY LIFE (Economics) The Statistics is the basis for almost all the activities of Individuals, Group, Society, Community and Country. FOR EXAMPLE, Literacy rate Below poverty line people Employment Status Satisfaction level for any activity Average Rainfall Department of Mathematics, SPIHER

5. Average temperatures during different seasons Average rainfall for constructing houses, buildings, offices, etc., Earthquake Statistics All Insurance activities based on statistics Petroleum exploration Monsoon activity in the region etc.., Application of Statistics in DAILY LIFE (Economics) Department of Mathematics, SPIHER

6. Every area of business uses sta t ist i cs in dec i sion mak i ng. Best w a y to rea c h to the mar k et: sur v e y , dir e ct mail, cat a logs etc… Re d u c ing the stress on job: c o n d u c ti n g v a rio u s typ e s of sur v ey by usi n g v a rio u s statistical tec h niq u es. Ap p l i cations o f Sta ti s ti c s i n Business Department of Mathematics, SPIHER

7. T a k ing v a rio u s typ e s of fin a n c ial d e cisio n s su c h as, exp a nsi o n, Me r g e r & ac q uisition s , n e w pr o d u ct or ser v ice lau n c h , tec h n o logy u p gr a de etc . . Ec o n o m i c d e cisio n s su c h as calc u lating the GDP rate, u n em p loy m ent rate, pr e dict the fut u re b u sin e ss climat e s. Im p a c t of T e c h n ology su c h as, I n ter n et sur v e y , Gra p h a n aly s is, In e x p ensiv e ly a n aly s is of d a ta. Ap p l i cations o f Sta ti s ti c s i n Business Department of Mathematics, SPIHER

8. App li c atio n s o f St a t is t ics i n C omp ute r S ci e n c e a n d En g i nee r ing M a c hin e l e arnin g . D at a m i n ing ( d ata management and data anal y si s ). Department of Mathematics, SPIHER

9. A ppli cat ions o f St ati s t ics i n M a c hin e l e ar n ing Mac h ine lea r ning i s a s ubse t o f a r tificial intel l igen c e in the field o f com p ute r scienc e that o f ten u s e s statistica l tech niq u e s to give com p ute r s the abil i ty to " l ear n " w i t h data, w i t ho u t be ing explicitl y p r ogrammed. Department of Mathematics, SPIHER

10. Machi ne l ea r ning and statistic s a r e c l os e l y r e l a ted fi e l d s . The i d e as of ma ch ine l ea r nin g , from m ethodolo g i cal pr i nc i p l e s to theor e ti c al tools , have had a long pr e - histo ry i n s t at i s t i c s. The te r m d ata sci ence a s a p la ce hold e r to c a l l the over a l l fi e l d of Machi ne l ea r ning . Two s t at i s t i cal mod e l l ing p a r a d i gms i n ma chine l e a r ning : d a ta m od e l and a lgor ithmi c mod e l . “ A l g orithm i c mod e l " m e ans mor e or l e s s the ma ch ine l ea r ning a l g orithms l i k e Random for e st. Machin e learning’s Rel a tion t o S tatistics Department of Mathematics, SPIHER

11. Ap p l i cations o f Sta ti s ti c s i n Dat a min i ng Da ta m in i ng i s the pro ce s s of d i scovering p att e rns i n l a r g e d a ta set s involvin g m ethods at the inte rse c tion of ma ch ine l ea r nin g , s t at i s t i c s , and d a tab a s e s y s t e ms. Department of Mathematics, SPIHER

12. Dat a mining relation with Statis tics Both d a ta m in i ng a nd statistic s a r e r e l a ted Both a r e a l l a bout d i scovering and i d e ntifying s t ructur e s i n d a ta, wi th the a i m of Turni n g d a ta to inform a tion A lthou g h the ai m s of both th e s e t e chn iq u e s ov e r l a p, th e y h a ve d iff e r e nt a p p ro a ch e s . S t at i s t i c s i s only a b out qu a ntifying d at a . Whi le Da ta Min i ng us e s tools to find r e l evant pro pe rti e s of d a ta, i t i s a lo t l i k e ma th . Da ta m in ing, on the other han d , bui l ds mod e l s to d e te c t p a tterns and r e l a tionships in d a ta, pa r ti c ul a r l y from l a r g e d a ta b ases. Ap p l i cations o f Sta ti s ti c s i n Dat a min i ng Department of Mathematics, SPIHER

13. Application of Statistics in AGRICUTURE (Environmental Science) Agriculture: Varieties of a crop or grass may need to be grown in large areas, while varieties of fertilizer or varying growth periods may be observed in subsets of the area. Behavioral Sciences: Many studies involve repeated measurements on the same subjects and are analyzed as a split-plot. Department of Mathematics, SPIHER

14. Blocks: b groups of experimental units to be exposed to all combinations of whole plot and subplot factors Whole plots: a experimental units to which the whole plot factor levels will be assigned to at random within blocks Subplots: c subunits within whole plots to which the subplot factor levels will be assigned to at random. Fully balanced experiment will have n= abc observations Design Structure Department of Mathematics, SPIHER

15. The Agriculture Heritage of Split-Plot Design Whole plots: large areas of land Subplots: smaller areas of land within large areas Example: Effects of variety, field, and fertilizer on the growth of a crop One variety is planted in a field (a whole plot) Each field is divided into subplots with each subplot is treated with one type of fertilizer Crop varieties : main treatments Fertilizers: sub treatments Department of Mathematics, SPIHER

16. Application of Statistics in PSYCHOLOGY(Social Science) Essential Occasionally misleading Department of Mathematics, SPIHER

17. Applications of statistics In General E c onomics: F or m ulatio n of e c o n o m ic policies, e c o n o m etrics Fin a nce: H e lps in value at risk, sto ck m arke t -deri v ative Insurance: B a sed on c o n c e pt of pr o bab i lity Ope r a t io n s: In ven t o r y , SQC, six sig m a m eth o d Department of Mathematics, SPIHER

18. HR: per f ormance ev a luations, Fee d b a ck of traini n g pr o gr a m I T : Optimiz a tion of ser v er time, T esting softw a re Data m i n i ng: Spe c ialized br a n c h of c o mbi n ation of IT & Statisti c s It is used in all fields of bu s iness Applications of statistics in General Department of Mathematics, SPIHER

19. Some s tat i st i cal te chniques and t heir applic a tions N o r m al distribu t i o n equity r esea r ch f ina n ce m a rk eting p r odu c t i on e n g g p r oj ect m anage m ent r isk m ana g e m ent pe r f o r m ance ap p raisal six sig m a PE R T / C P M Sa m pling m a rk et r esea r ch c o n su m er sur v ey T es t ing of Hyp o thesis A g ricul t ure, pa r a m edica l , P h ar m aceut i cal t e st i ng a fe r t i l i ze r , t e st i ng a d r u g, t e st i ng of d r u g , c l inic a l tri a l Decis i on theo r y Finance I n vest m ent and p o rtfo l io se l ec t ion Department of Mathematics, SPIHER

20. F o re c asting H R D In s u rance Marketi n g m anpower plan n ing Det e r m inin g p r e m iu m D e m and fo rec a st i ng Discri m in ant Analy sis Fi n anc e , M a rk eting Credit r i sk analys i s, Cust o m er p r o f ile Some st a tist i cal techniques and their appl i cat i ons Department of Mathematics, SPIHER