Chapter 1 Why Study Statistics? BUS172: Introduction to Statistics Chapter 1 Why Study Statistics?

Dealing with Uncertainty Everyday decisions are based on incomplete information Decision makers cannot be certain of the future behavior of factors that affect the outcome Why Study Statistics? Numerical information is everywhere Statistical techniques are used to make many decisions Business decisions are made in an uncertain environment. An understanding of statistical methods will help you make these decisions more effectively. We make many decisions often based on incomplete information. More importantly, business decisions are made in an uncertain environment. Therefore, it is crucial that we understand how to collect, analyze and interpret data. In other words, we need to make sense of data. Sometimes computer software such as Excel or SPSS help us to quickly process, summarize and analyze data.

Importance of Statistics To draw conclusions about the wider population. Statistics is the study of how to make decisions about a population when our information has been obtained from a sample. Some uncertainty will always remain. Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc.

Key Definitions A population is the collection of all items of interest that interest an investigator. N represents the population size For e.g. All students in your university A sample is an observed subset of the population n represents the sample size For example: students majoring in Finance. A parameter is a specific characteristic of a population A statistic is a specific characteristic of a sample Population is the complete set of all items. It is represented by “N”. For example: All students in your university; All families living in Dhaka. Sample is an observed subset of the population. For example: Students majoring in Finance; Families living in Banani.

Population vs. Sample Population Sample a b c d b c ef gh i jk l m n o p q rs t u v w x y z b c g i n o r u y A parameter is a specific characteristic of a population A statistic is a specific characteristic of a sample. Values calculated using population data are called parameters Values computed from sample data are called statistics

Example: Sample vs. Population Suppose you want to find out the average age of voters in Bangladesh. The population size is so large that we might take only a random sample, perhaps 500 registered voters and calculate their average age. Since the average age is based on sample data, it is called a “statistic” If the average age of the whole population was calculated, then the average age would be called a “parameter”. Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc.

Random Sampling Simple random sampling is a procedure in which each member of the population is chosen strictly by chance, each member of the population is equally likely to be chosen, and every possible sample of n objects is equally likely to be chosen The resulting sample is called a random sample

Descriptive and Inferential Statistics Two branches of statistics: Descriptive statistics Collecting, summarizing, and processing data to transform data into information Inferential statistics provide the bases for predictions, forecasts, and estimates that are used to transform information into knowledge

Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Summarize data e.g., Sample mean =

Inferential Statistics Estimation e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing e.g., Test the claim that the population mean weight is 120 pounds Inference is the process of drawing conclusions or making decisions about a population based on sample results

Exercise 1.8 Determine if descriptive statistics or inferential statistics should be used to obtain the following information: A graph that shows the number of defective bottles produced during the day shift over one week’s time. An estimate of the percentage of employees who arrive to work late. An indication of the relationship between years of employee experience and pay scale. Answer: Descriptive – to describe information about a one week sample. Inferential – to estimate the true percentage of all employees who arrive late. Inferential – to predict the relationshiop between years of experience and pay scale.