Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 1 An Introduction to Business Statistics

1-2 An Introduction to Business Statistics 1.1Data 1.2Data Sources 1.3Populations and Samples 1.4Three Case Studies that Illustrate Sampling and Statistical Inference 1.5Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional)

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 LO 1: Explain what a variable is.

1-4 Data 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 LO 2: Describe the difference between a quantitative variable and a qualitative variable.

1-5 Cross-Sectional Data Cross-sectional data: Data collected at the same or approximately the same point in time Time series data: data collected over different time periods LO 3: Describe the difference between cross-sectional data and time series data.

1-6 Time Series Data LO 4: Construct and interpret a time series (runs) plot.

Data Sources Existing sources: data already gathered by public or private sources Internet Library US Government Data collection agency Experimental and observational studies: data we collect ourselves for a specific purpose Response variable: variable of interest Factors: other variables related to response variable LO 5: Describe the different types of data sources.

Populations and Samples PopulationThe set of all elements about which we wish to draw conclusions (people, objects or events) CensusAn examination of the entire population of measurements SampleA selected subset of the units of a population LO 6: Describe the difference between a population and a sample.

1-9 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 LO 7: Distinguish between descriptive statistics and statistical inference.

Three Case Studies That Illustrate Sampling and Statistical Inference 1. The Cell Phone Case: Estimating Cell Phone Costs 2. The Marketing Research Case: Rating a New Bottle Design 3. The Car Mileage Case: Estimating Mileage LO 8: Explain the importance of random sampling.

Ratio, Interval, Ordinal, and Nominative 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 LO 9: Identify scales of measurement (optional).