Quantitative Analysis For Management | Quantitative Analysis Explained For Begin

2. Meet Alex, he has established a new Automotive business venture.

3. The business is operational at low scale, hence Alex is in charge of all financial and managerial decisions. • Financial Decisions • Managerial Decisions

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5. Alex takes his business decisions by following market trends and his gut. Instinct / Gut Market Trend

6. Recently, Alex made a decision to introduce a luxury automobile, despite the fact that his company is still in its early stages.

7. Due to that, Alex went through a substantial loss. And decided to get in touch with a business intelligence consultant.

8. This is Victor. He is an experienced business intelligence consultant, who is going to help Alex.

9. Victor met Alex and understood that Alex’s decision making process is completely qualitative.

10. Victor started explaining Alex about where his decisions went wrong and how he can make better decisions by utilizing quantitative analysis.

11. Alex began questioning Victor since he was unfamiliar with the phrase quantitative analysis and its application in making decisions.

12. What Is Quantitative Analysis?

13. What Is Quantitative Analysis? Quantitative analysis is a technique that uses mathematical and statistical modeling, measurement, and research to understand patterns in any activity.

14. What Is Quantitative Analysis? This method necessitates extensive research at the beginning. The researched data is then transformed into a numerical format.

15. What Is Quantitative Analysis? The numerical data then goes through computer analysis using advanced statistical and mathematical modeling.

16. What Is Quantitative Analysis? End result generated through this analysis is completely objectiveand unemotional.

17. What Is Quantitative Analysis? We live in an era where huge amount of data is generated every second. And it’s difficult to distinguish between which data is useful or useless.

18. What Is Quantitative Analysis? Traditional analysis models are broken now. That is why, strong statistical and mathematical modelling is the way going forward.

19. How Quantitative Analysis Works? What Is Quantitative Analysis?

20. How Quantitative Analysis Works? Let’s understand this with the help of an example.

21. How Quantitative Analysis Works? Convert gathered data into numerical format Assessing customer satisfaction with Alex’s Luxury automobile Gather data of Car owners across the globe

22. How Quantitative Analysis Works? Convert gathered data into numerical format Car Owner Data Sample

23. How Quantitative Analysis Works? Satisfied with Service Yes = 1 No = 2 Convert gathered data into numerical format Car Owner Data Sample

24. How Quantitative Analysis Works? Statistical modeling is then applied to data depending on the anticipated kind of outcome.

25. How Quantitative Analysis Works? Quantitative analysis uses two forms of statistical modelling. Descriptive Statistics Inferential Statistics

26. How Quantitative Analysis Works? In statistics, a population refers to the total group of items, people, or organizations that you're analyzing. 1. Population

27. How Quantitative Analysis Works? In statistics, a population refers to the total group of items, people, or organizations that you're analyzing. 1. Population In the case of our customer satisfaction analysis problem, all car owners throughout the world are considered a population.

28. How Quantitative Analysis Works? A sample is a subset of the entire population. In the customer satisfaction analysis use case, the sample will be data of only 100 car owners. 1. Sample Sample Population

29. How Quantitative Analysis Works? Descriptive statistics: This form of statistics focuses on describing the contents of sample. Inferential statistics: This form of statistics aims to make predictions about the whole population.

30. Descriptive Statistics Mean Median Mode Standard Deviation Skewness

31. Descriptive Statistics Mean A mathematical average of a set of numbers is called a mean.

32. Descriptive Statistics Mode is the most commonly repeated datain provided data set. Mode

33. Descriptive Statistics Median is the midpointof range of numbers. Median

34. Descriptive Statistics This metrics representsdispersion of data pointsavailable in the data set. Standard Deviation

35. Descriptive Statistics Skewness represents howsymmetricala range of numbers is. Skewness

36. Descriptive Statistics Descriptive Statistics Data Set Mean = ?

37. Descriptive Statistics Descriptive Statistics Data Set Mean = 60.14 Median = ?

38. Descriptive Statistics Descriptive Statistics Data Set Mean = 60.14 Median = 55 Mode = ?

39. Descriptive Statistics Descriptive Statistics Data Set Mean = 60.14 Median = 55 Mode = 70 Standard Deviation = ? SD

40. Descriptive Statistics Descriptive Statistics Data Set Mean = 60.14 Median = 55 Mode = 70 Standard Deviation = 8.23 Skewness = ? =3 * (Mean – Median) / Standard Deviation.

41. Descriptive Statistics Data Visualization Mode Mean Median Non-Parametric Distribution

42. Descriptive Statistics Importance Helps identify errors and anomalies in the data. Helps you decide which inferential model to use. Helps visualizing micro details of the data.

43. Inferential Statistics Inferential statistics aims to make predictions about the whole population. And there are two types of predictions it can make. Difference Between Variable Relations

44. Methods of Inferential Statistics T - Test Regression ANOVA Correlation Analysis

45. Methods of Inferential Statistics T - Test T – Test compares mean between two groups of data to assess whether they are different to a statistically significant extent.

46. Methods of Inferential Statistics ANOVA is similar to T-Test except that it compares multiple groups of data. ANOVA

47. Methods of Inferential Statistics Correlation Analysis This statistical model assesses the relationship between variables.

48. Methods of Inferential Statistics Correlation Analysis

49. Methods of Inferential Statistics Regression assesses the relationship between variables by understanding the cause and effect between variables. Regression

50. Methods of Inferential Statistics Regression