Table of contents
- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically2h 8m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables3h 28m
- 6. Normal Distribution & Continuous Random Variables2h 21m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 37m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals22m
- Confidence Intervals for Population Mean1h 26m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 20m
- 9. Hypothesis Testing for One Sample5h 15m
- Steps in Hypothesis Testing1h 13m
- Performing Hypothesis Tests: Means1h 1m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions39m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions29m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 35m
- Two Proportions1h 12m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 2m
- 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
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator15m
- 11. Correlation1h 24m
- 12. Regression3h 42m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope32m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression23m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 31m
- 14. ANOVA2h 32m
11. Correlation
Hypothesis Tests for Correlation Coefficient Using TI-85
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Join thousands of students who trust us to help them ace their exams!Watch the first videoMultiple Choice
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 -value 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
suggests weak positive linear correlation; reject since there is enough evidence to support nonzero linear correlation between inflation and unemployment.
C
suggests strong positive linear correlation; fail to reject since not enough evidence to support nonzero linear correlation between inflation and unemployment.
D
suggests strong positive linear correlation; reject since there is enough evidence to support nonzero linear correlation between inflation and unemployment.
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Verified step by step guidance1
Understand that the correlation coefficient measures the strength and direction of a linear relationship between two variables. Here, indicates a weak positive linear correlation between inflation rate and unemployment rate.
Recognize that the hypothesis test is conducted to determine if the observed correlation is statistically significant. The null hypothesis states that the population correlation coefficient is zero (no linear correlation), while the alternative hypothesis states that .
Interpret the P-value of 0.35 obtained from the hypothesis test. The P-value represents the probability of observing a correlation as extreme as 0.23 (or more) if the null hypothesis were true. A high P-value (commonly above 0.05) suggests insufficient evidence to reject the null hypothesis.
Conclude that since the P-value (0.35) is greater than the typical significance level (e.g., 0.05), we fail to reject the null hypothesis. This means there is not enough statistical evidence to support a nonzero linear correlation between inflation and unemployment rates.
Summarize the interpretation: the correlation coefficient indicates a weak positive linear relationship, but the hypothesis test shows that this observed correlation is not statistically significant, so we cannot confidently say there is a linear association between the two variables based on this sample.
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