This document provides an overview of key concepts in statistics for management, including: 1. Descriptive statistics involve collecting and describing data through tables and graphs, while inferential statistics allow making decisions about populations based on sample data using estimation and hypothesis testing. 2. There are different types of data that can come from primary sources like surveys or secondary compiled sources, and different sampling methods like simple random, systematic, and stratified sampling that select subjects with known probabilities. 3. Surveys can be conducted through interviews, observation, or experiments, but are subject to errors like non-response, sampling, and measurement errors that can impact the quality of statistical analysis.

1. Statistics for Management Introduction and Data Collection

2. Lesson Topics Statistical Thinking and Management Descriptive versus Inferential Statistics Types of Data and their Sources Types of Sampling Methods Survay methods Types of Survey Errors

3. 1. Statistical Thinking and Management Three Aspects of Quality Improvement Management Philosophy Behavioral Tools Statistical Methods

4. 2. Statistical Methods Descriptive Statistics Inferential Statistics Collecting and describing data. Making decisions based on sample data.

5. Descriptive Statistics Collect Data e.g. Survey Present Data e.g. Tables and Graphs Characterize Data e.g. Mean A Characteristic of a: P opulation is a P arameter S ample is a S tatistic.

6. Inferential Statistics Estimation Hypothesis Testing Making decisions concerning a population based on sample results.

7. 3. Types of Data

8. 3. Data Sources Primary Data Collection Secondary Data Compilation Observation Experimentation Survey Print or Electronic

9. Quota 4.Types of Sampling Methods Samples Non-Probability Samples Judgement Chunk Probability Samples Simple Random Systematic Stratified Cluster

10. Probability Samples Probability Samples Simple Random Systematic Stratified Cluster Subjects of the sample are chosen based on known probabilities.

11. Simple Random Samples Every individual or item from the target frame has an equal chance of being selected. Selection may be with replacement or without replacement . One may use table of random numbers for obtaining samples.

12. Systematic Samples Decide on sample size: n Divide population of N individuals into groups of k individuals: k = N / n Randomly select one individual from the 1st group. Select every k-th individual thereafter. N = 64 n = 8 k = 8 First Group

13. Stratified Samples Population divided into 2 or more groups according to some common characteristic. Simple random sample selected from each. The two or more samples are combined into one.

14. Cluster Samples Population divided into several “ clusters ”, each representative of the population. Simple random sample selected from each. The samples are combined into one. Population divided into 4 clusters.

15. 5. Survey methods Interview (Anket, Face to face, Telephon, Letter) Observation Experimentation Data compilation

16. 6. Types of Survey Errors Coverage Error Non Response Error Sampling Error Measurement Error Excluded from selection. Follow up on non responses. Chance differences from sample to sample . Bad Question!

17. Lesson Summary Described the use of Statistical Thinking to improve quality. Addressed the notion of Descriptive versus Inferential Statistics. Defined and described different Types of Data and Sources Listed Types of Sampling Methods. Described different Types of Survey Errors.