Statistical Analysis with Software Application 203 Topic 1.pptx

BASIC STAT INTRO

1. COURSE OVERVIEW STAT 203 – Statistical Analysis with Software Application Course Facilitator: Engr. Emy Lou G. Alinsod

2. Course Description This course focuses on conceptual understanding of everyday statistics, and basic statistical procedures. Topics include basic concepts of statistics, descriptive statistics, inferential statistics, especially parametric estimation and hypothesis testing, and illustrated and applied to practical situations. Statistical Analysis, coupled with the use of technology, is essential in making informed decisions as well as in conducting research effectively.  Use of statistical software like Excel and JASP to generate tables and graphs or perform computations.

3. Recommended Readings &  Course References Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365) 7th Edition - Wayne Winston  Data Analysis with Microsoft® - Kenneth N. Berk Patrick Carey

4. COURSE OUTLINE  Introduction to Statistical Concepts Definitions and Terminology Areas of Statistics Qualitative and Quantitative Variables Discrete and Continuous Variable Levels of Measurement

5. COURSE OUTLINE cont'd 2. Data Collection and Basic Concepts in Sampling Design Data Collection  Sources of Data  Methods of Collecting Primary and Secondary Data Sample Size Determination Basic Sampling Design Sources of Errors in Sampling

6. COURSE OUTLINE cont'd 3. Descriptive Statistics Data Presentation  Measures of Central Tendency Measures of Variability Normal Distribution and Areas Under a Standard Normal Curve 4.  Inferential Statistics   a. Hypothesis Testing    1. Z-test    2. T-test    3. ANOVA    4. Correlation

7. COURSE OUTLINE cont'd 4.   Inferential Statistics   a. Hypothesis Testing    1. Z-test    2. T-test    3. ANOVA    4. Correlation

8. INTRODUCTION TO STATISTICAL CONCEPTS Topic 1

9. What is Statistics? The science of statistics deals with the collection, analysis, interpretation, and presentation of data. Two areas of  statistics: Descriptive Statistics   is collection, presentation, and description of data Inferential Statistics is making decisions and drawings conclusions about populations.

10. Statistics Statistics is learning from data . A challenge is that when we collect data, we get different answers for different subjects. Data comes with variability .  Statistics allows us to describe , understand and control the variability insofar as possible and to take this uncertainty into account when making judgements and decisions.

11. Descriptive Statistics The term descriptive statistics refers to the act of describing and summarizing data.   Estimates of Central Tendency(Mean, Median, Mode) Ex. Based on research conducted by DOH , 62% of those found with diabetes were not aware that they have such disease.

12. Inferential Statistics Inferential statistics are used to make conclusions, or inferences, based on the available data from a smaller sample population. Inferential statistics techniques include: Hypothesis tests, or tests of significance Correlation analysis Logistic or linear regression analysis:  Confidence intervals

13. Statistics

14. Statistical Paradigm

15. Population and Samples A population as a collection of persons, things, or objects under study. To study the population, we select a sample . The idea of sampling is to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. "There are 1100 freshmen college students in PUPSRC, 100 of them were randomly asked if they smoked cigarettes regularly."

16. Parameter and Statistic A parameter is a descriptive measure from population data A statistic is a descriptive measure computed from sample data For example, say you want to know the mean income of the subscribers to a particular streaming app—a parameter of a population. You draw a random sample of 100 subscribers and determine that their mean income is P45,000 (a statistic).

17. Variable and Data Variables are the characteristics or attributes that you are observing, measuring and recording data for.  The word data refers to observations and measurements which have been collected in some way, often through research. "A researcher may be interested in the relation between class size and academic performance for the population of the grade 7 students."

18. Quantitative and Qualitative Variables Quantitative Variables: Sometimes referred to as “numeric” variables, these are variables that represent a measurable quantity.   Examples include: Number of students in a class Number of square feet in a house Population size of a city Age of an individual Height of an individual

19. Quantitative and Qualitative Variables Qualitative Variables : Sometimes referred to as “categorical” variables, these are variables that take on names or labels and can fit into categories. Examples include:     Eye color (e.g. “blue”, “green”, “brown”)     Gender (e.g. “male”, “female”)     Breed of dog (e.g. “lab”, “bulldog”, “poodle”)     Level of education (e.g. “high school”, “Associate’s degree”, “Bachelor’s degree”)     Marital status (e.g. “married”, “single”, “divorced”)

20. Quantitative and Qualitative Variables

21. Summarizing Quantitative & Qualitative Variables We can use many different metrics to summarize  quantitative variables , including: Measures of central tendency like the mean, median, and mode. Measures of dispersion like the range, interquartile range, and standard deviation. However, we can only use frequency tables and relative frequency tables to summarize  qualitative v ariables .

22. Variable Types and Examples

23. Variable Types and Examples

24. Levels of Measurement

25. Levels of Measurement

26. Levels of Measurement

27. Levels of Measurement

28. Levels of Measurement

29. ACTIVITY Determine what the key terms refer to in the following study. A study was conducted at a local college to analyze the average cumulative GPA’s of students who graduated last year. Fill in the letter of the phrase that best describes each of the items below. Population ____ Statistic ____ Parameter ____ Sample ____ Variable ____ Data ____ ______________________________ all students who attended the college last year. the cumulative GPA of one student who graduated from the college last year. 3.65, 2.80, 1.50, 3.90. a group of students who graduated from the college last year, randomly selected. the average cumulative GPA of students who graduated from the college last year. all students who graduated from the college last year. the average cumulative GPA of students in the study who graduated from the college last year.

30. ACTIVITY Determine what the key terms refer to in the following study. We want to know the average (mean) amount of money spent on school uniforms each year by families with children at Knoll Academy. We randomly survey 100 families with children in the school. Three of the families spent $65, $75, and $95, respectively. The population ______________________________________________ The sample _________________________________________________ The parameter ______________________________________________ The statistic ________________________________________________ The variable ________________________________________________ The data ___________________________________________________

31. ACTIVITY Determine what the key terms refer to in the following study. We want to know the average (mean) amount of money spent on school uniforms each year by families with children at Knoll Academy. We randomly survey 100 families with children in the school. Three of the families spent $65, $75, and $95, respectively. The population is all families with children attending Knoll Academy. The sample is a random selection of 100 families with children attending Knoll Academy. The parameter is the average (mean) amount of money spent on school uniforms by families with children at Knoll Academy. The statistic is the average (mean) amount of money spent on school uniforms by families in the sample. The variable is the amount of money spent by one family. Let be the amount of money spent on school uniforms by one family with children attending Knoll Academy. The data are the dollar amounts spent by the families. Examples of the data are $65, $75, and $95.