BUSINESS STATISTICS Chapter 1 (Page 26)

1.1 What is Business Statistics (Page26) Business Statistics – is a collection of tools and techniques that are used to convert data into meaningful information in a business environment. Ex: stock prices, crime rates, government budgets, number of production, unemployment rates, interest rates

(Page 26) Descriptive Statistics – describes data using charts, graphs and numerical measures. Example: Bar Charts, Histogram, Line Graph, Pie Graph, Scatter Diagram, Tables showing numerical measures

Bar Chart

HISTOGRAM

LINE GRAPH

Pie Chart

SCATTER DIAGRAM

FREQUENCY DISTRIBUTION numerical measure DVD Movies Frequency Relative Frequency 0 – –

Inferential Statistics – draw inferences or conclusions from a set of data which include estimation and hypothesis testing. (Page 29) Statistical Inference Procedures – procedures that allow a decision maker to reach a conclusion about a set of data.

(Page 29) Estimation. Knowing about all the data in a large data set but it is impractical to work with all the data, estimation is used to know what the larger data set looks like. Example: TV Ratings (Page 29) Hypothesis Testing. A method of making decisions using experiments. Example: Preference between Mc Donalds and Burger King

1.2 Procedures for Collecting Data (Page 31) Data Collection Methods Data Collection Methods 1. Experiment – a process that produces a single outcome whose result cannot be predicted with certainty. Experimental Design – a plan for performing an experiment in which the variable of interest is defined. Experimental Design – a plan for performing an experiment in which the variable of interest is defined.

2. Telephone Surveys – inexpensive and efficient data collection tool. (Page 33) Closed-End Questions – questions that require the respondent to select from a short list of defined choices. Demographic Questions – questions relating to the respondents’ characteristics, backgrounds and attributes.

Major steps for a Telephone Survey (Page 32) Major steps for a Telephone Survey (Page 32) 1. Define the issue 2. Define the population of interest 3. Develop survey questions 4. Pretest the survey 5. Determine the sample size and sampling method 6. Select sample and make calls

(Page 33) 3. Written Questionnaires and Surveys – frequently used method to collect opinions and factual data from people. (Page 34) Open-end Questions – questions that allow respondents the freedom to respond with any value, words, or statements of their own choosing.

Written survey Steps (Page 33) Written survey Steps (Page 33) 1. Define the issue 2. Define the population of interest 3. Design the survey instrument 4. Pretest the survey 5. Determine the sample size and sampling method 6. Select sample and send surveys

(Page 34 – 35) Potential Problems Potential Problems 1.Leading Questions 2.Poorly Worded Questions 3.Inappropriate choice of Vocabulary

(Page 35) 4. Direct Observation and Personal Interview – requires that the process from which the data are being collected is physically observed. Structured Interview – interviews in which the questions are scripted. Unstructured Interview – interviews that begin with one or more broadly stated questions, with further questions being based on the responses.

Other Data collection Methods (Page 35-36) Other Data collection Methods (Page 35-36) Data collection methods that take advantage of new technologies. ► Bar codes ► Credit Cards ► Physical Measurement (Page 36) Bias – an effect which alters a statistical result by systematically distorting it.

Data Collection Issues (Page 36-37) Data Collection Issues (Page 36-37) 1. Interviewer Bias – interviewer can interject bias by the way she asks questions, by the tone of her voice or by the way she looked at the subject being interviewed. 2. Non-responsive Bias – people refused to answer or to be interviewed. 3. Selection Bias – bias can be interjected through the way subjects are selected for data collection. 4. Observer Bias – people tend to view the same event or item differently. 5. Measurement Error – uses different measurement tools.

(Page 37) Internal Validity – a characteristic of an experiment in which data are collected in such a way as to eliminate the effects of variables within the experimental environment that are not of interest to the researcher. External Validity – a characteristic of an experiment whose results can be generalized beyond the test environment so that the outcomes can be replicated when the experiment is repeated.

(Page 30) Exercises: 1-1, 1-2, 1-3, 1-4, 1-6 Exercises: 1-1, 1-2, 1-3, 1-4, Descriptive 1-2Bar Graph 1-3Spaces/gaps between bars 1-4Use experiment Ex. Powder laundry soap 1-6Decision maker needs to use statistical inference procedure if she wants to test inference procedure if she wants to test claims about products and services or want claims about products and services or want to make an estimate to make an estimate

(Page 37 – 38) Exercises: 1-17, 1-19, 1-21, 1-22, Written survey or telephone survey 1-19Experiment – process that generates data Experimental design – plan for performing an experiment 1-21Not reliable 1-22a. Written surveys b. Direct observation or personal interview c. Telephone surveys 1-25Selection Bias/Non responsive Bias