1-3 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 LO1-1: Explain what a variable is.
1-5 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 LO1-2: Describe the difference between a quantitative variable and a qualitative variable.
1-6 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 LO1-3: Describe the difference between cross-sectional data and time series data.
1-7 Time Series Data LO1-4: Construct and interpret a time series (runs) plot. Table 1.2 and Figure 1.1
Data Sources 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 LO1-5: Describe the different types of data sources: existing data sources, experimental studies, and observational studies.
1-9 Examples of Public Economic and Financial Data Sites Table 1.3 LO1-5
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 LO1-6: Describe the difference between a population and a sample.
1-11 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 LO1-7: Distinguish between descriptive statistics and statistical inference.
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 LO1-8: Explain the importance of random sampling.
1-13 Estimating Cell Phone Costs Considering using a company to manage their cellular resources Random sample of 100 employees on 500- minute plan Many overages and underage LO1-8
1-14 The Cell Phone Case: The Data LO1-8 Table 1.4
1-15 The Marketing Research Case: Rating a New Bottle Design Studying to see if changes should be made in the bottle design for a popular soft drink Using “mall intercept method” Sample size of 60 LO1-8
1-16 The Marketing Research Case: The Form and the Data LO1-8 Figure 1.2 and Table 1.5
1-17 Terms Process: a sequence of operations that takes inputs and turns them into outputs Finite population: a population of limited size Infinite population: a population of unlimited size LO1-8
1-18 The Care Mileage Case: Estimating Mileage Study of tax credit offered by the federal government for improving fuel economy Automaker has introduced a new model and wishes to demonstrate it qualifies for the tax credit Sample of 50 cars LO1-8
1-19 The Care Mileage Case: The Data LO1-8 Table 1.6 and Figure 1.3
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 LO1-9: Identify ratio, interval, ordinal, and nominative scales of measurement (optional).