TAILIEUCHUNG - Lecture Basic statistics for business & economics (8/e): Chapter 13 – Lind, Marchal, Wathen

Chapter 13 - Correlation and linear regression. When you have completed this chapter, you will be able to: Understand and interpret the terms dependent and independent variable; calculate and interpret the coefficient of correlation, the coefficient of determination, and the standard error of estimate; conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero. | Correlation and Linear Regression Chapter 13 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin LEARNING OBJECTIVES LO 13-1 Define the terms dependent variable and independent variable. LO 13-2 Calculate, test, and interpret the relationship between two variables using the correlation coefficient. LO 13-3 Apply regression analysis to estimate the linear relationship between two variables LO 13-4 Interpret the regression analysis. LO 13-5 Evaluate the significance of the slope of the regression equation. LO 13-6 Evaluate a regression equation to predict the dependent variable. LO 13-7 Calculate and interpret the coefficient of determination. LO 13-8 Calculate and interpret confidence and prediction intervals. 13- In this chapter we’re going to learn how to use linear regression and perform correlation analysis in studying whether two variables are dependent or independent, measure the strength of their association, carry out tests to determine whether this association or correlation is significant, and use the regression model for estimating the response variable (Y) using prediction interval and confidence interval. Regression Analysis – Introduction Recall in Chapter 4, showing the relationship between two variables with a scatter diagram was introduced. In that case we showed that as the age of the buyer increased the amount spent for the vehicle also increased. In this chapter, we carry this idea further. Numerical measures to express the strength of relationship between two variables are developed. In addition, an equation is used to express the relationship between variables, allowing us to estimate one variable on the basis of another. EXAMPLES Is there a relationship between the amount Healthtex spends per month on advertising and its sales in the month? Can we base an estimate of the cost to heat a home in January on the number of square feet in the home? Is there a relationship between the miles per .

• Quản trị kinh doanh
• Statistics for business
• Describing data
• Statistical techniques
• Terms dependent variable
• Independent variable
• Linear regression
• 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
• Ordinal level data
• 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