Multiple Choice
In linear regression using the least squares method, the least squares regression line minimizes the sum of the , where represents the residuals (the differences between observed and predicted values).
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Table of contents
- 1. Intro to Stats and Collecting Data
- 2. Describing Data with Tables and Graphs
- 3. Describing Data Numerically
- 4. Probability
- 5. Binomial Distribution & Discrete Random Variables
- 6. Normal Distribution and Continuous Random Variables
- 7. Sampling Distributions & Confidence Intervals: Mean
- Sampling Distribution of the Sample Mean and Central Limit Theorem
- Distribution of Sample Mean - Excel
- Introduction to Confidence Intervals
- Confidence Intervals for Population Mean
- Determining the Minimum Sample Size Required
- Finding Probabilities and T Critical Values - Excel
- Confidence Intervals for Population Means - Excel
- 8. Sampling Distributions & Confidence Intervals: Proportion
- 9. Hypothesis Testing for One Sample
- 10. Hypothesis Testing for Two Samples
- Two Proportions
- Two Proportions Hypothesis Test - Excel
- Two Means - Unknown, Unequal Variance
- Two Means - Unknown Variances Hypothesis Test - Excel
- Two Means - Unknown, Equal Variance
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel
- Two Means - Known Variance
- Two Means - Sigma Known Hypothesis Test - Excel
- Two Means - Matched Pairs (Dependent Samples)
- Matched Pairs Hypothesis Test - Excel
- 11. Correlation
- 12. Regression
- 13. Chi-Square Tests & Goodness of Fit
- 14. ANOVA
12. Regression
Linear Regression & Least Squares Method
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