MIKKAH MAE C. MANGAYAN- FS103.inferential-statisticspptx

Basic Inferential statistics with notation and other information regarding the introduction of story.

1. FS 103 Basic and Inferential Statistics EULOGIO “AMANG” RODRIGUEZ INSTITUTE OF SCIENCE AND TECHNOLOGY GRADUATE SCHOOL Nagtahan, Sampaloc, Manila SATURDAY (10:00 am – 1:00 pm) NOEL JOSE B. MALANUM MAED-AS

2. TABLE OF CONTENT Introduction to Regression Analysis Understanding of Regression Analysis . Presentation, Interpretation of Slopes and Predictions. 1 1.1 1.2 1.3 1.4 1.5 Correlation vs Regression Regression Models Linear Regression Analysis

3. Understand the concept of regression analysis in educational research. Identify different types regression models   Perform basic data summarization and visualization . Objectives:

4. Definition: Regression analysis is often used to model or analyze data. Most survey analysts use it to understand the relationship between the variables, which can be further utilized to predict the precise outcome. For Example – Suppose a soft drink company wants to expand its manufacturing unit to a newer location. Before moving forward, the company wants to analyze its revenue generation model and the various factors that might impact it. Hence, the company conducts an  online survey  with a specific questionnaire. After using regression analysis, it becomes easier for the company to analyze the survey results and understand the relationship between different variables like electricity and revenue – here, revenue is the dependent variable. What is Regression Analysis 1. Introduction of Regression Analysis

5. • Regression analysis identify the exact relationships between variables, and to see how changing one variable affects the system as a whole, so it shouldn’t be hard to see the connection between it and cause and effect analysis. • Regression is the measure of the average relationship between two or more variables. What is Regression Analysis 1.1 Understanding th e Regression Analysis .

6. • Degree & Nature of Relationship  Correlation is a measure of degree of relationship between X & Y.  Regression studies the nature of relationship between the variables so that one may be able to predict the value of one variable on the basis of another. Correlation vs. Regression 1.2 .Correlation vs Regression

7. • • Cause & Effect Relationship  Correlation does not assume cause and effect relationship between two variables.  Regression clearly expresses the cause and effect relationship between two variables. The independent variable is the cause and dependent variable is effect. Correlation vs. Regression

8. • Degree & Nature of Relationship  Correlation is a measure of degree of relationship between X & Y.  Regression studies the nature of relationship between the variables so that one may be able to predict the value of one variable on the basis of another. • Cause & Effect Relationship  Correlation does not assume cause and effect relationship between two variables.  Regression clearly expresses the cause and effect relationship between two variables. The independent variable is the cause and dependent variable is effect. Correlation vs. Regression

9. 1.3 Regression Models.

10. 1.3 Regression Types of Relationship.

11. 1.3 Regression Types of Relationship.

12. Regression Analysis Regression analysis is used to:  Predict the value of a dependent variable based on the value of at least one independent variable.  Explain the impact of changes in an independent variable on the dependent variable. Dependent variable: The variable we wish to predict or explain . Independent variable: The variable used to predict or explain the dependent variable .

13. Simple Linear Regression Model Only one independent variable, X • Relationship between X and Y is described by a linear function • Changes in Y are assumed to be related to changes in X 1.4 Linear Regression Analysis.

14. Simple Linear Regression Model 1.4 Linear Regression Analysis.

15. Simple Linear Regression Model 1.4 Linear Regression Analysis.

16. Interpretation of the Slope and Intercept 1.5 Presentation, Interpretation of Slopes and Predictions

17. Interpretation of the Slope and Intercept 1.5 Presentation, Interpretation of Slopes and Predictions

18. Interpretation of the Slope and Intercept 1.5 Presentation, Interpretation of Slopes and Predictions

19. Interpretation of the Slope and Intercept 1.5 Presentation, Interpretation of Slopes and Predictions

20. Interpretation of the Slope and Intercept 1.5 Presentation, Interpretation of Slopes and Predictions

21. 1.5 Presentation, Interpretation of Slopes and Predictions Interpretation of the Slope and Intercept

22. What are the differences between Quantitative VS Qualitative Data? Quantitative data  is numbers-based, countable, or measurable.  Qualitative data  is interpretation-based, descriptive, and relating to language. Quantitative data  tells us how many, how much, or how often in calculations.  Qualitative data  can help us to understand why, how, or what happened  behind certain behaviors. Quantitative data  is fixed and universal.  Qualitative data  is subjective and unique. Quantitative research  methods are measuring and counting.  Qualitative research   methods are interviewing and observing. Quantitative data  is analyzed using statistical analysis.  Qualitative data  is analyzed by grouping the data into categories and themes.

23. Levels of Measurement 1.3 Levels of measurement: nominal, ordinal, interval, and ratio.

24. Descriptive Statistics 1.4 Descriptive statistics: measures of central tendency and dispersion. Statistics that summarize or describe features of a data set, such as its central tendency or dispersion.

25. Measures of Central Tendency & Dispersion Measures of central tendency are measures that indicate the approximate center of a distribution . Measures of dispersion are m easures that describe the spread of the data.

26. Variance

27. . Measures of Central Tendency : Mean

28. . Measures of Central Tendency: Median

29. . Measures of Central Tendency: Mode

30. . Measures of Dispersion: Range

31. . Measures of Dispersion: Variance and Standard Deviation

32. .

33. What is Data Visualization? 1.5 Data visualization techniques: histograms, bar charts, and pie charts. It used to analyze visually the behavior of the different variables in a dataset, such as a relationship between data points in a variable or the distribution. Data visualization techniques are visual elements (like a histograms, bar chart, pie chart, etc.) that are used to represent information and data.

34. What is Histogram? A  histogram  is a graphical representation of a grouped frequency distribution with continuous classes. It is an area diagram and can be defined as a set of rectangles with bases along with the intervals between class boundaries and with areas proportional to frequencies in the corresponding classes. In other words, a histogram is a diagram involving rectangles whose area is proportional to the frequency of a variable and width is equal to the class interval.

35. What is Pie chart? A Pie charts are attractive data visualization types. At a high-level, they’re easy to read and used for representing relative sizes. A Pie Chart is a circular graph that uses “pie slices” to display relative sizes of data. A pie chart is a perfect choice for visualizing percentages because it shows each element as part of a whole. The entire pie represents 100 percent of a whole. The pie slices represent portions of the whole. .

36. What is Bar chart? A bar chart (also called bar graph) is a chart that represents data using bars of different heights. The bars can be two types – vertical or horizontal. It doesn’t matter which type you use. The bar chart can easily compare the data for each variable at each moment in time. .

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