TAILIEUCHUNG - Lecture Business statistics in practice (7/e): Chapter 1 - Bowerman, O'Connell, Murphree

Chapter 1 - An introduction to business statistics. After mastering the material in this chapter, you will be able to: Define a variable, describe the difference between a quantitative variable and a qualitative variable, describe the difference between crosssectional data and time series data, construct and interpret a time series (runs) plot,. | Chapter 1 An Introduction to Business Statistics Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin An Introduction to Business Statistics Data Data Sources Populations and Samples Three Case Studies that Illustrate Sampling and Statistical Inference Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional) 1- 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- 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

• Quản trị kinh doanh
• Business Statistics
• Business statistics in practice
• Descriptive statistics
• Data sources
• Statistical inference
• Illustrate sampling
• Lecture Business statistics
• Bài giảng Thống kê doanh nghiệp
• Descriptive statistics tabular
• Descriptive statistics graphical presentations
• Summarizing quantitative data
• Essentials of modern business statistics
• Modern business statistics
• Graphical presentations
• numerical measures
• Exploratory data analysis
• Interval estimation
• Hypothesis tests
• Simple linear regression
• Basic statistics
• Statistics for business
• Describing data
• Types of statistics
• Quantitative variables
• Ordinal level data
• 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