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A_Math_201_SMU_Lecture_Notes_for_Week_1_Ch._1_2.2_2.3

A_Math_201_SMU_Lecture_Notes_for_Week_1_Ch._1_2.2_2.3 -...

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Unformatted text preview: Internet Usage Key: 7|2 = 72 7 7 1 7 7 8 9 0 1 2 3 8 9 9 0 0 1 13 4 6 7 9 9 9 0 1 1 2 4 4 6 0 1 3 4 4 6 6 6 9 2 7 9 2 3 7 8 0 8 MATH 201 Lecture Notes: Week #1 (Chapter 1; 2-2, 2-3) CHAPTER 1: INTRODUCTION TO STATISTICS Note : This is the only chapter that is not taken section by section – only the end- of-chapter exercises are assigned. Terminology and definitions are emphasized. I. Data vs. Information A. Data : • What you get from observations, counts, measurements, or responses • Can be a data set -- a “pile of numbers” • Can be prose or narrative (transcribed interviews) • Can be qualitative or quantitative B. Information : Taking the data and turning it into something that is useful and meaningful. C. Statistics : The science of collecting, organizing, analyzing, and interpreting data in order to make (good, well-informed) decisions. “BIG PICTURE”: 2 branches of statistics: descriptive and inferential DATA INFORMATION KNOWLEDGE (pile of (useful and (for good decision- collected meaningful) making) “stuff”) | | | | | | V V Descriptive statistics Inferential statistics Organization, display, Use a sample to make & summarization of inferences, or draw data (Chapters 1- 6) conclusions, about a population (Ch. 7-8) II. Terminology and definitions A. Data Sets : 1. Population : collection of all outcomes, responses, measurements, or counts; 2. Parameter : measure obtained by using all the data values for a specific population; descriptive measures of a population; 3. Sample : subset of a population; 4. Statistic : measure obtained by using the data values from a sample; descriptive measures of a sample. P arameters go with p opulations (note that both start with “p”); S tatistics go with s amples (note that both start with “s”). MATH 201 LECTURE NOTES – page 2 CHAPTER 1: INTRODUCTION TO STATISTICS II. Terminology/Definitions—Continued B. Qualitative vs. Quantitative Data : 1. Qualitative data : attributes, labels, or “non-numerical” entries (numerical qualitative labels serve only as identifiers); can’t ‘number crunch” 2. Quantitative data : numerical measurements or counts, can “number crunch”; two kinds : a. Discrete : derived from a counting process; only a finite number exist; discrete data are countable ; b. Continuous : derived from a measuring process; an infinite number exists; can be any real value in a finite or infinite real-number interval . C. Levels of measurement : 1. Nominal : qualitative data only, distinct categories, no ordering , lowest level; 2. Ordinal : distinct categories, ordering, scaling: differences between data values on the scale are either meaningless or indeterminable; 3. Interval : distinct categories, ordered, scaling: equal increments, no inherent zero (ratios are meaningless -- only meaningful differences are possible); 4. Ratio : distinct categories, ordered, scaling: equal increments, inherent zero (implying that ratios and differences are meaningful)....
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