Least Squares Regression Line
2 hr 22 min 19 Examples
- Introduction to Video: Least-Squares Regression
- Identify Explanatory and Response Variables and How to determine the Correlation Coefficient (Example #1)
- Find the correlation coefficient using both formula methods (Example #2)
- Find the correlation coefficient and create a scatterplot (Example #3)
- Would you expect a positive, negative or no association for the pairs of variables (Example #4)
- Consider the scatterplot and determine the linear association (Example #5)
- How to find the Least Squares Regression Line using raw data or summary statistics
- Find the regression line (Examples #6-7)
- What are residuals, outliers and influential points? With Example #8
- Use the data to create a scatterplot and find the correlation coefficient, LSRL, residuals and residual plot (Example #9)
- Find the regression line and use it to predict a value (Examples #10-11)
- Using technology find the regression line, correlation coefficient, coefficient of determination and use the LSRL to predict a future value (Example #12-13)
- Using the regression line interpret the slope and r-squared value and find the residual (Example #14)
- Using output data determine the regression line (Example #15)
- Determine if the observation in a regression outlier and has influence on the regression analysis (Example #16)
- Explain what is wrong with the way regression is used in each scenario (Example #17)
- Construct a scatterplot and compute the regression line and determine correlation and coefficient of determination (Example #18)
- Find the regression line and use it to predict future values (Example #19)
Log Transformation
31 min 3 Examples
- Introduction to Video: Transformation to Achieve Linearity
- Why and How do we transform data to achieve linearity?
- Given a data set find the regression line, r-squared value, and residual plot (Example #1)
- Use the Power transformation to find the transformed regression line, r-squared value and residual plot (Example #1a)
- Use the Exponential transformation to find the transformed regression line, r-squared value and residual plot (Example #1b)
- Use the Square Root transformation to find the transformed regression line, r-squared value and residual plot (Example #1c)
- Use the Logarithmic and Hyperbolic transformations to find the transformed regression line, r-squared value and residual plot (Example #1d and 1e)
- Use regression analysis to determine the best answer (Example 2)
- Transform using the square root or logarithmic method and use the transformed data to predict a future value (Example #3)
Linear Regression T Test
1 hr 5 Examples
- Introduction to Video: Significance Test for Slope
- Overview and Formulas for conducting Hypothesis testing for Least Squares Regression
- Estimate the regression line, conduct a confidence interval and test the hypothesis for the given data (Examples #1-2)
- Using the data set find the regression line, predict a future value, conduct a confidence interval and test the hypothesis (Examples #3)
- Test the claim using computer output data (Example #4)
- Write the regression line, test the claim, and conduct a confidence interval using computer output data (Example #5)
Chapter Test
1 hr 10 min 7 Practice Problems
- Using raw data create a scatterplot, find the LSRL, estimate a future value, construct a confidence interval and hypothesis test (Problem #1)
- Given summary statistics find and interpret the regression line (Problem #2)
- Using computer output data identify and interpret the regression equation, predict a future value and find the residual (Problem #3)
- Create a residual plot using raw data (Problem #4)
- Apply a transformation to achieve linearity (Problem #5)
- Given summary data estimate the slope with 90% confidence (Problem #6)
- Using output data find the regression line, test the claim and provide a confidence interval (Problem #7)
