Lecture Business statistics in practice (7/e): Chapter 1 - Bowerman, O'Connell, Murphree

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Chapter 1 - An introduction to business statistics. After mastering the material in this chapter, you will be able to: Define a variable, describe the difference between a quantitative variable and a qualitative variable, describe the difference between crosssectional data and time series data, construct and interpret a time series (runs) plot,...

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Nội dung Text: Lecture Business statistics in practice (7/e): Chapter 1 - Bowerman, O'Connell, Murphree

Chapter 1 An Introduction to Business Statistics McGrawHill/Irwin Copyright © 2014 by The McGrawHill Companies, Inc. All rights reserved.
An Introduction to Business Statistics 1.1 Data 1.2 Data Sources 1.3 Populations and Samples 1.4 Three Case Studies that Illustrate Sampling and Statistical Inference 1.5 Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional) 12
LO1-1: Explain what a variable is. 1.1 Data Data: facts and figures from which conclusions can be drawn Data set: the data that are collected for a particular study ◦Elements: may be people, objects, events, or other entries Variable: any characteristic of an element 13
LO1-2: Describe the difference between a quantitative variable Data and a qualitative variable. Continued Measurement: A way to assign a value of a variable to the element Quantitative: the possible measurements of the values of a variable are numbers that represent quantities Qualitative: the possible measurements fall into several categories 14
LO1-3: Describe the difference between cross-sectional data CrossSectional Data and time series data. Crosssectional data: Data collected at the same or approximately the same point in time Time series data: data collected over different time periods 15
LO1-4: Construct and interpret a time series (runs) plot. Time Series Data Table 1.2 and Figure 1.1 16
LO1-5: Describe the different types of data sources: existing data 1.2 Data Sources sources, experimental studies, and observational studies.  Existing sources: data already gathered by public or private sources ◦ Internet ◦ Library ◦ Private data sources  Experimental and observational studies: data we collect ourselves for a specific purpose ◦ Response variable: variable of interest ◦ Factors: other variables related to response variable 17
LO1-6: Describe the difference between a population and a 1.3 Populations and Samples sample. Population The set of all elements about which we wish to draw conclusions (people, objects or events) Census An examination of the entire population of measurements Sample A selected subset of the units of a population 18
LO1-7: Distinguish between descriptive statistics and statistical inference. Descriptive Statistics and Statistical Inference Descriptive statistics: the science of describing the important aspects of a set of measurements Statistical inference: the science of using a sample of measurements to make generalizations about the important aspects of a population of measurements 19
LO1-8: Explain the importance of random sampling. 1.4 Three Case Studies That Illustrate Sampling and Statistical Inference 1. Estimating Cell Phone Costs 2. The Marketing Research Case: Rating a New Bottle Design 3. The Car Mileage Case: Estimating Mileage 110
LO1-9: Identify ratio, interval, ordinal, and 1.5 Ratio, Interval, Ordinal, and Nominative nominative scales of measurement (optional). Scales of Measurement (Optional)  Quantitative variables ◦ Ratio variable: a quantitative variable measured on a scale such that ratios of its value are meaningful and there is an inherently defined zero value ◦ Interval variable: a quantitative variable where ratios are not meaningful and there is no defined zero  Qualitative variables (categorical) ◦ Ordinal variable: a qualitative variable for which there is a meaningful ranking of the categories ◦ Nominative variable: a qualitative variable for which there is no meaningful ranking of the categories 111