TAILIEUCHUNG - Lecture Basic statistics for business & economics (8/e): Chapter 1 – Lind, Marchal, Wathen

Chapter 1: What is statistics? When you have completed this chapter, you will be able to: List ways that statistics is used, know the differences between descriptive and inferential statistics, understand the differences between a sample and a population, explain the difference between qualitative and quantitative variables, compare the discrete and continuous variables, recognize the levels of measurement in data. | What Is Statistics? Chapter 01 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin LEARNING OBJECTIVES LO 1-1 List ways that statistics is used. LO 1-2 Know the differences between descriptive and inferential statistics. LO 1-3 Understand the differences between a sample and a population. LO 1-4 Explain the difference between qualitative and quantitative variables. LO 1-5 Compare the discrete and continuous variables. LO 1-6 Recognize the levels of measurement in data. 1- Why Study Statistics? Why Study Statistics? Numerical information is everywhere. Statistical techniques are used to make decisions that affect our daily lives. The knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you. No matter what line of work you select, you will find yourself faced with decisions where an understanding of data analysis is helpful. Some examples of the need for data collection. Research analysts for Merrill Lynch evaluate many facets of a particular stock before making a “buy” or “sell” recommendation. The marketing department at Colgate-Palmolive Co., a manufacturer of soap products, has the responsibility of making recommendations regarding the potential profitability of a newly developed group of face soaps having fruit smells. The United States government is concerned with the present condition of our economy and with predicting future economic trends. Managers must make decisions about the quality of their product or service. LO1 List ways statistics is used. 1- What Is Meant by Statistics? In the more common usage, statistics refers to numerical information Examples: the average starting salary of college graduates, the number of deaths due to alcoholism last year, the change in the Dow Jones Industrial Average from yesterday to today, and the number of home runs hit by the Chicago Cubs during the 2007 season. We often present .

• Quản trị kinh doanh
• Statistics for business
• Describing data
• Statistical techniques
• Study statistics
• Ordinal level data
• Inferential statistics
• Lecture Business statistics
• Bài giảng Thống kê doanh nghiệp
• Business statistics
• Descriptive statistics tabular
• Descriptive statistics graphical presentations
• Summarizing quantitative data
• Essentials of modern business statistics
• Modern business statistics
• Descriptive statistics
• Graphical presentations
• numerical measures
• Exploratory data analysis
• Interval estimation
• Hypothesis tests
• Simple linear regression
• Basic statistics
• Types of statistics
• Quantitative variables
• Business statistics in practice
• Data sources
• Statistical inference
• Illustrate sampling
• Graphical methods
• Tabular methods
• Graphically summarizing qualitative data
• Numerical methods
• Describing central tendency
• Measures of variation
• Sample spaces
• Elementary probability rules
• Compute conditional probabilities
• Discrete random variables
• Discrete probability distributions
• Binomial distribution
• Continuous random variables
• Continuous probability distributions
• Uniform distribution
• Sampling distributions
• Random sampling
• Stratified random
• Confidence intervals
• Sample size determination
• Hypothesis testing
• Alternative hypotheses
• Sampling distribution
• Equal variance
• Statistical inferences
• Population variances
• Experimental design
• Analysis of variance
• Randomized block design
• Chi square tests
• Chi square goodness of fit tests
• Multinomial experiment
• Simple linear regression analysis
• Simple linear regression model
• Model assumptions
• Multiple regression
• Least squares point estimate
• Multiple regression model
• Times series forecasting
• Index numbers
• Forecast error comparisons
• Statistical process control
• Causes of variation
• Control charts
• Nonparametric methods
• Hypothesis test
• Decision theory
• Utility theory
• Posterior probabilities