1 / 21

Welcome to DSCI 2710.003

Welcome to DSCI 2710.003. Ms. Gina Harden. KVANLI PAVUR KEELING. Chapter 1 A First Look at Statistics. Chapter Objectives. At the completion of this chapter, you should be able to answer: ∙ What is “statistics” and why is the study of

trevet
Télécharger la présentation

Welcome to DSCI 2710.003

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Welcome to DSCI 2710.003 Ms. Gina Harden

  2. KVANLI PAVUR KEELING Chapter 1A First Look at Statistics

  3. Chapter Objectives • At the completion of this chapter, you should be able to answer: ∙ What is “statistics” and why is the study of statistics important to a business manager? ∙ What is a “parameter” and what makes it different from a “statistic” ? ∙ What is meant by the term “descriptive statistics”? “inferential statistics”?

  4. Chapter Objectives - Continued ∙ What is a population? A sample? A census? ∙ What are the types of data? (discrete/continuous) ∙ What are the strengths of data? (nominal, ordinal, interval, ratio) ∙ What distinguishes qualitative data from quantitative data?

  5. Section 1.2 Using the Computer • The textbook discusses three software packages: Microsoft Excel, MINITAB, and SPSS • We will only be using Excel; in particular, we will use a set of Excel macros that we have written to carry out the calculations and construct various statistical graphs/charts • Read Section 1.3 (Uses of Statistics in Business)

  6. Population vs Sample Population (size is N) For example: All students at UNT All students at UNT who own a car All registered voters All production workers at Motorola You measure or count something for each of these. That is the population. Sample (size is n) This is what we look at and describe Usually selected randomly (called a simple random sample) Random samples provide a better representation of the population.

  7. Two Areas of Statistics • Descriptive Statistics • Inferential Statistics

  8. Descriptive Statistics • With descriptive statistics, you describe the sample and quit • You might construct a graph Discussed in Chapter 2 • You might derive a number or two Discussed in Chapter 3 Example: “The average age of the 100 sample values is 21.3 years.“ • The sample average is called a statistic

  9. Inferential Statistics • You use the sample results to infer something about the population • Example: “My estimate of the average age of everyone in the population is 21.3 years.” • The average age of everyone in the population is called a parameter since it describes the population.

  10. Summary • A number derived from a sample is called a statistic. • The corresponding value in the population is called a parameter. It is usually represented by a Greek symbol (e.g., μ and σ). The symbol μ is mu and is pronounced “myoo”. • A census is a sample that consists of the entire population.

  11. Sample Data • Sample data can be discrete or continuous • You obtain discrete data when you’re counting something • You obtain continuous data when you’re measuring something

  12. Discrete and Continuous Data • Example of discrete data: How many textbooks did you purchase this semester? • Possible sample: {2, 3, 5, 1, 10, 8, …} • Example of continuous data: What is your height? • Possible sample (in feet): {5.26, 6.12, 5.83, …} No decimal point Values have a decimal point

  13. Level of Measurement • “Weak” data versus “strong” data • weakest Nominal (a category) Ordinal (ranks) Interval (differences are meaningful) strongest Ratio (the word “twice” makes sense)

  14. Nominal Data • Nominal data are the weakest data and represent a category. • Examples: Gender, Ethnicity, Hair color • We only discuss proportions here. For example, in the sample we might find that 58% are female and 42% are male.

  15. Ordinal Data • Example: Where did you finish in the race last week? • Sample: {12, 3, 6, 25, …} - - that is, 12th, 3rd, 6th, 25th, … • With ordinal data, the order of the values is meaningful but the difference between values is not meaningful.

  16. Ordinal Data - Continued • Is this true? The difference between 1st place and 2nd place is the same as The difference between 30th place and 31st place • We simply can’t say. The difference between 1st and 2nd place might be a half-second and the difference between 30th and 31st place might be a half-minute - - or the other way around.

  17. Interval Data • Here, the difference between values is meaningful • Example: Temperature (⁰F) • Is this true? The difference between 60⁰ F and 65⁰ F is the same as The difference between 80⁰ F and 85⁰ F • Yes, the difference is 5⁰ F in both cases.

  18. Ratio Data • Most business data are ratio data. • With ratio data, the word “twice” makes sense and a value of zero means it doesn’t exist. • Examples: Counting something, height, length, weight, area, time • Consider weight: Is 100 lbs. twice as heavy as 50 lbs.? Or length: Is 10 feet twice as long as 5 feet? The answer is “Yes” for both.

  19. Ratio Data • Also, if something weighs 0 lbs., it doesn’t exist. --- unless you’re in a vacuum. But how often does that happen? • Or, if something is 0 feet long, it doesn’t exist. • Notice that temperature (⁰ F) is not ratio since the following statement doesn’t make sense: “It’s 0 degrees, so there is no temperature.”

  20. Qualitative and Quantitative Data • Nominal data and ordinal data are referred to as qualitative data. • Interval data and ratio data are referred to as quantitative data.

  21. Summary of Data and Levels of Measurements:

More Related