목차
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 21m
- 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 - ExcelBonus23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - ExcelBonus28m
- Confidence Intervals for Population Means - ExcelBonus25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - ExcelBonus42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - ExcelBonus27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - ExcelBonus28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - ExcelBonus12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - ExcelBonus9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - ExcelBonus21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - ExcelBonus12m
- Two Variances and F Distribution29m
- Two Variances - Graphing CalculatorBonus16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - ExcelBonus8m
- Finding Residuals and Creating Residual Plots - ExcelBonus11m
- Inferences for Slope31m
- Enabling Data Analysis ToolpakBonus1m
- Regression Readout of the Data Analysis Toolpak - ExcelBonus21m
- Prediction Intervals13m
- Prediction Intervals - ExcelBonus19m
- Multiple Regression - ExcelBonus29m
- Quadratic Regression15m
- Quadratic Regression - ExcelBonus10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 29m
11. Correlation
Hypothesis Tests for Correlation Coefficient Using TI-84
객관식
An economist wonders if the inflation rate is linearly correlated with the unemployment rate and is looking to use the results of their analysis for further study. They take a random sample of recent months and record the unemployment rate and inflation rate. They find and run a hypothesis test, getting a of . Interpret the value of and results of the test.
A
suggests weak positive linear correlation; fail to reject since not enough evidence to support nonzero linear correlation between inflation and unemployment.
B
r=0.23 suggests weak positive linear correlation; reject H0(p=0) since there is enough evidence to support nonzero linear correlation between inflation and unemployment.
C
r=0.23 suggests strong positive linear correlation; fail to reject H0(p=0) since not enough evidence to support nonzero linear correlation between inflation and unemployment.
D
r=0.23 suggests strong positive linear correlation; reject H0(p=0) since there is enough evidence to support nonzero linear correlation between inflation and unemployment.
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검증된 단계별 안내1
Understand the correlation coefficient as a measure of the strength and direction of a linear relationship between two variables. Here, indicates a weak positive linear correlation between inflation rate and unemployment rate.
Set up the hypothesis test for the population correlation coefficient : the null hypothesis (no linear correlation), and the alternative hypothesis (there is a linear correlation).
Use the given P-value of 0.35 to determine the statistical significance. The P-value represents the probability of observing a correlation as extreme as 0.23 (or more) if the null hypothesis is true.
Compare the P-value to the significance level (commonly 0.05). Since 0.35 is greater than 0.05, there is not enough evidence to reject the null hypothesis, meaning we fail to conclude a significant linear correlation exists.
Interpret the results: the weak positive correlation is not statistically significant based on the P-value, so we conclude there is insufficient evidence to support a nonzero linear correlation between inflation and unemployment rates.
관련 영상
관련 실천
객관식
In a TI-84 correlation test (LinRegTTest), what does it mean for the correlation between and to be statistically significant at (e.g., )?
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