In simple linear regression using the least squares method, which plot will produce a straight line if the model assumptions are satisfied?
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
12. Regression
Linear Regression & Least Squares Method
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
In the linear regression equation , what is the value of the slope?
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Verified step by step guidance1
Identify the general form of a simple linear regression equation, which is given by \(y = b_0 + b_1 x\), where \(b_0\) is the intercept and \(b_1\) is the slope.
In the given equation, performance = 9.32 + 0.52 × aptitude score, recognize that 9.32 corresponds to the intercept \(b_0\) and 0.52 corresponds to the slope \(b_1\).
Understand that the slope \(b_1\) represents the change in the dependent variable (performance) for each one-unit increase in the independent variable (aptitude score).
Therefore, the value of the slope is the coefficient multiplying the aptitude score, which is 0.52 in this equation.
Conclude that the slope of the regression line is 0.52, indicating how performance changes with aptitude score.
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