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Слайды и текст к этой презентации:
№1 слайд
Содержание слайда: Chapter 9: Correlation and Regression
9.1 Correlation
9.2 Linear Regression
9.3 Measures of Regression and Prediction Interval
№2 слайд
Содержание слайда: Correlation
Correlation
A relationship between two variables.
The data can be represented by ordered pairs (x, y)
x is the independent (or explanatory) variable
y is the dependent (or response) variable
№3 слайд
Содержание слайда: Types of Correlation
№4 слайд
Содержание слайда: Example: Constructing a Scatter Plot
A marketing manager conducted a study to determine whether there is a linear relationship between money spent on advertising and company sales. The data are shown in the table. Display the data in a scatter plot and determine whether there appears to be a positive or negative linear correlation or no linear correlation.
№5 слайд
Содержание слайда: Constructing a Scatter Plot Using Technology
Enter the x-values into list L1 and the y-values into list L2.
Use Stat Plot to construct the scatter plot.
№6 слайд
Содержание слайда: Correlation Coefficient
Correlation coefficient
A measure of the strength and the direction of a linear relationship between two variables.
r represents the sample correlation coefficient.
ρ (rho) represents the population correlation coefficient
№7 слайд
Содержание слайда: Linear Correlation
№8 слайд
Содержание слайда: Calculating a Correlation Coefficient
№9 слайд
Содержание слайда: Example: Finding the Correlation Coefficient
Calculate the correlation coefficient for the advertising expenditures and company sales data. What can you conclude?
№10 слайд
Содержание слайда: Finding the Correlation Coefficient
Example Continued…
№11 слайд
Содержание слайда: Using a Table to Test a Population Correlation Coefficient ρ
Once the sample correlation coefficient r has been calculated, we need to determine whether there is enough evidence to decide that the population correlation coefficient ρ is significant at a specified level of significance.
Use Table 11 in Appendix B.
If |r| is greater than the critical value, there is enough evidence to decide that the correlation coefficient ρ is significant.
№12 слайд
Содержание слайда: Hypothesis Testing for a Population Correlation Coefficient ρ
A hypothesis test (one or two tailed) can also be used to determine whether the sample correlation coefficient r provides enough evidence to conclude that the population correlation coefficient ρ is significant at a specified level of significance.
№13 слайд
Содержание слайда: Using the t-Test for ρ
№14 слайд
Содержание слайда: Example: t-Test for a Correlation Coefficient
For the advertising data, we previously calculated r ≈ 0.9129. Test the significance of this correlation coefficient. Use α = 0.05.
№15 слайд
Содержание слайда: Correlation and Causation
The fact that two variables are strongly correlated does not in itself imply a cause-and-effect relationship between the variables.
If there is a significant correlation between two variables, you should consider the following possibilities:
Is there a direct cause-and-effect relationship between the variables?
Does x cause y?
№16 слайд
Содержание слайда: 9.2 Objectives
Find the equation of a regression line
Predict y-values using a regression equation
№17 слайд
Содержание слайда: Residuals & Equation of Line of Regression
Residual
The difference between the observed y-value and the predicted y-value for a given x-value on the line.
№18 слайд
Содержание слайда: Finding Equation for Line of Regression
№19 слайд
Содержание слайда: Solution: Finding the Equation of a Regression Line
To sketch the regression line, use any two x-values within the range of the data and calculate the corresponding y-values from the regression line.
№20 слайд
Содержание слайда: Example: Predicting y-Values Using Regression Equations
The regression equation for the advertising expenses (in thousands of dollars) and company sales (in thousands of dollars) data is ŷ = 50.729x + 104.061. Use this equation to predict the expected company sales for the advertising expenses below:
1.5 thousand dollars :
1.8 thousand dollars
3. 2.5 thousand dollars
№21 слайд
Содержание слайда: 9.3 Measures of Regression and Prediction Intervals
(Objectives)
Interpret the three types of variation about a regression line
Find and interpret the coefficient of determination
Find and interpret the standard error of the estimate for a regression line
Construct and interpret a prediction interval for y
№22 слайд
Содержание слайда: Variation About a Regression Line
Total variation =
The sum of the squares of the differences between the y-value of each ordered pair and the mean of y.
Explained variation
The sum of the squares of the differences between each predicted y-value and the mean of y.
№23 слайд
Содержание слайда: The Standard Error of Estimate
Standard error of estimate
The standard deviation (se )of the observed yi -values about the predicted ŷ-value for a given xi -value.
The closer the observed y-values are to the predicted y-values, the smaller the standard error of estimate will be.
№24 слайд
Содержание слайда: Prediction Intervals
Two variables have a bivariate normal distribution if for any fixed value of x, the corresponding values of y are normally distributed and for any fixed values of y, the corresponding x-values are normally distributed.