STATISTICS

1. Saint Paul University Philippines TuguegaraoCity, Cagayan 3500 STATISTICS Roldan C. Bangalan

2. What does STATISTICS mean?

3. The Meaning of STATISTICS • recorded data (facts and figures) • characteristics calculated for a set of data • statistical methodology or procedures and theory • The study of how we make sense of data • statistics is simply a collection of tools that researchers employ to help answer research questions

4. The Meaning of STATISTICS • is a science thatdeals with the collection, organization, presentation, analysis, and interpretation of data. • is a collection of methods for planning experiments, obtaining data, and thenorganizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.

5. Collectionrefers to the gathering of information or data. • Organizationor presentationinvolvessummarizing data or information in textual, graphical or tabularform. • Analysisinvolvesdescribing the data usingstatisticalmethods and procedures. • Interpretationrefers to the process of making conclusions based on the result of the statisticaltreatment of the data.

6. Branches of Statistics • DESCRIPTIVE • summarize or describe the important characteristics of a known set of population data • INFERENTIAL • use sample data to makeinferences (or generalizations) about a population

7. In each statement, tell whether descriptive or inferential statistics have been used. 1.By 2040 at least 3.5 billion people will run short of water (Source: World Future Society) 2.Nine out of ten on-the-job fatalities are men (Source: USA Today Weekend) 3.Allergy therapy makes bees go away (Source: Prevention) 4.Drinking decaffeinated coffee can raise cholesterol levels by 7% (Source: American Heart Association)

8. Why Biostatistics?

9. BIOSTATISTICS • Biostatistics is the branch of applied statistics directed toward applications in the health sciences and biology. • Biostatistics is sometimes distinguished from the field of biometry based upon whether applications are in the health sciences (biostatistics) or in broader biology (biometry; e.g. agriculture, ecology, wildlife biology) • Other branches of applied statistics; psychometrics, econometrics, chemometrics, astrostatistics, environmetrics, etc.

10. Why Biostatistics? What is the difference? • Because some statistical methods are more heavily used in health applications than elsewhere e.g. survival analysis, longitudinal data analysis • Because examples are drawn from health sciences • _ Makes subject more appealing to those interested in health • _ Illustrates how to apply methodology to similar problems encountered in real life.

11. Why study Statistics?

12. Why should I study Statistics? Why do I have to take this class? How useful will Statistics be in my future career?

13. Why study STATISTICS? • A knowledge of Statistics is essential for both understanding and conducting research in any of the health professions. • Whenever a new method, drug, device, or intervention is developed, a key question is, “Does it work?” Statistics are used to analyze the data and help you decide if the new idea is worthy of being incorporated into your professional lives.

14. Why study STATISTICS? • “Is the drug raloxifene as effective in reducing the chances of developing breast cancer as tamoxifen?” • “Does the Atkins diet lead to weight loss?” • Such questions require researchers to gather data, statistically analyze the data, and then interpret the results within the context of their profession.

15. Why study STATISTICS? • A knowledge of statistics can help anyone discriminate between fact and fiction in everyday life.

16. How to succeed in Statistics?

17. STUDYHINTS • The key to success in a Statistics course is to keep up with the material. • You will learn (and remember) much more if you study for short periods several times per week rather than try to condense all of your studying into one long session. • Do some work before class.

18. STUDYHINTS • Pay attention and think during class. • Test yourself regularly. • Do not kid yourself! Avoid denial. • Ask for help. Peer tutoring can be very helpful. Frederick Gravetter and Larry Walnau

19. STUDYHINTS • Keep in mind that students who are successful in mastering Statistics do not allow themselves to get behind. • Doing as many exercises as possible is one of the best ways to learn Statistics.

20. STATISTICAL TERMS

21. Population and Sample • A POPULATION is a complete collection of all elements (scores, people, measurements) to be studied. • A SAMPLE is a portion/sub-collection of elements drawn from a population.

22. EXAMPLE: Population and Sample • POPULATION • headache patients in a chiropractic office; • automobile crash victims in an emergency room • SAMPLE

23. Parameter and Statistic • A PARAMETER is a numerical measurement describing some characteristics of a population. • Summary data from a population • A STATISTIC is a numerical measurement describing a characteristic of a sample. • Summary data from a sample

24. Example: Parameter and Statistic • PARAMETER • STATISTIC

25. Data and Variable • Data • Measurements or observations of a variable • The word “data” is plural, datum is singular. • A collection of data is often called a data set (singular) • Variable • A characteristic that is observed or manipulated • Can take on different values

26. Qualitative Data and Quantitative Data • QUALITATIVE DATA (categorical) can be separated into different categories that are distinguished by some nonnumeric characteristics. • QUANTITATIVE DATA (numerical) consist of numbers representing counts or measurements.

27. Example: Qualitative Data and Quantitative Data • QUALITATIVE DATA • Smoking Status • Physical Activity at Home • Cause of death • Nationality • Race • Gender • severity of pain • QUANTITATIVE DATA • Weight • Body Mass Index • Blood Glucose • survival time • systolic blood pressure • number of children in a family

28. Discrete Data and Continuous Data • DISCRETE DATA result from either a finite number of possible values or countable number of possible values as 0, or 1, or 2, and so on. • CONTINUOUS DATA result from infinitely many possible values that can be associated with points on a continuous scale in such a way that there are no gaps or interruptions.

29. Example: Discrete Data and Continuous Data • DISCRETE DATA • The number of eggs that hens lay • number of pregnancies • number of missing teeth • CONTINUOUS DATA • The amounts of milk that cows produce • duration of a seizure • body mass index • height

30. Dependent Variable and Independent Variable • DEPENDENT VARIABLE – the variable that is being affected or explained • What is measured as an outcome in a study • Values depend on the independent variable • INDEPENDENT VARIABLE – the variable that affects or explains • Precede dependent variables in time • Are often manipulated by the researcher • The treatment or intervention that is used in a study

31. Example: Dependent Variable and Independent Variable • DEPENDENT VARIABLE • INDEPENDENT VARIABLE

32. Thesis Titles • An Evaluation of the Hepatoprotective Activity of IxoraCoccinea flower against Paracetamol induced hepatic injury in Albino Rats • A Comparative Study on the Evaluation of the Rodenticide Property of Alliumsativum and AlliumCepa Bulb Emulsion on Albino Mce

33. Thesis Titles • The effect of the juice extract of Raphanussativusagainst Diclofenac-induced in albino rats. • Effects of Euphorbia hirta leaf extract against quinine-induced thrombocytopenia in Female Albino Mice • Correlation of the Effects of Teenage Pregnancy to the Self-Esteem of the Mothers

34. Scales/Levels of Measurement • The nominal level of measurement is characterized by data that consist of names, labels, or categories only. • Example: • Survey responses of yes, no, and undecided • Civil Status • Television shows watched at 7PM • Treatment preference (e.g., manipulation, mobilization, massage) • Eye color

35. Scales/Levels of Measurement • The ordinal level of measurement involves data that may be arranged in some order but differences between data values either cannot be determined or are meaningless. • Example: • Subject Grades (K+12) • Rankings • Pain level (e.g., mild, moderate, severe) • Military rank (e.g., lieutenant, captain, major, colonel, general) • Stage of disease • Level of satisfaction

36. Scales/Levels of Measurement • The interval level of measurement is like the ordinal level, but meaningful amounts of differences can be determined. It has no inherent (natural) zero starting point. • Example: • Temperatures • Years • IQ

37. Scales/Levels of Measurement • The ratio level of measurement is the interval level modified to include the inherent zero starting point. • Example: • Weights • Prices