- 1. Introduction to Statistics53m
- 2. Describing Data with Tables and Graphs2h 1m
- 3. Describing Data Numerically1h 48m
- 4. Probability2h 26m
- 5. Binomial Distribution & Discrete Random Variables2h 55m
- 6. Normal Distribution & Continuous Random Variables1h 48m
- 7. Sampling Distributions & Confidence Intervals: Mean2h 8m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 20m
- 9. Hypothesis Testing for One Sample2h 23m
- 10. Hypothesis Testing for Two Samples3h 25m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 30m
- 14. ANOVA1h 4m
Struggling with Statistics for Business?
Join thousands of students who trust us to help them ace their exams!Watch the first videoAn 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.
r=0.23 suggests weak positive linear correlation; fail to reject H0(p=0) since not enough evidence to support nonzero linear correlation between inflation and unemployment.
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.
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.
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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