Population 2 Bay Health o Hypotheses H0 \u03c31 \u03c32 H1 \u03c31 \u03c32 population variances are

Population 2 bay health o hypotheses h0 σ1 σ2 h1

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Population 2 = Bay Health o Hypotheses H0: σ1 = σ2 H1: σ1 ≠ σ2 (population variances are different) Step 3: set a value for the significance level, ∞ o Set ∞ = 0.05 Step 4: Calculate the F-test statistic o Finding F F = s1 / s2 = 0.8464 / 0.5041 = 1.679 Step 5: Determine the critical value (F-score) o Degrees of Freedom
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D1 = (n1 – 1) = (20 – 1) = 19 D2 = (n2 – 1) = (20 – 1) = 19 o F-Score Two-tail test → ∞/2 = 0.025 Area in right tail of distribution (0.025 column) D1 = 19 and D2 = 19 → 2.526 Step 6: Compare the test statistic (F) with the critical value (F-score) o 1.679 (F) < 2.526 (F-score) → do not reject H0 Step 7: State your conclusions o Fail to reject null hypothesis Do not have evidence that population variances are diff for the two hospitals CANT SAY variances are equal, but business says we probably have no reason to investigate the diff in stay time for each of the hospitals Using PHStat2 to Compare Two Population Variances o Procedures Add-ins > Two-Sample Tests (Summarized Data) > F Test for Differences in Two Variances Fill in values where needed - Sample SD - Level of significance, ∞ - Population 1 sample size and sample variance - Population 2 sample size and sample variance - Two-tail test or upper-tail test o Results Intermediate Calculations - F Test Statistic - Population 1 Sample DF - Population 2 Sample DF
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Two-Tail Test - Upper Critical Value (F-Score) - P-value - Whether or not to reject the null hypothesis
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Chapter 14: Correlation and Simple Regression Analyses Intro o Correlation & Investments Investors strive to achieve uncorrelated investments to reduce the risk of one of their stocks affecting the other OR all increasing / decreasing at one time o Techniques that find relationships b/w 2 factors Correlation Analysis - Determines strength and direction of the relationship b/w two variables - Hypothesis test – det. if strength b/w 2 variables is strong enough to be useful Simple regression - Describes the relationship b/w 2 variables using a linear equation - Predict variable 1 given specific value of variable 2, and vice versa - Hypothesis to det. if results are accurate enough to be useful o Applications in Business Realtors want to est. relationship b/w living space and eventual selling price in particular town Best Buy manager wants to know effect of dropping printer price $10 will have on demand in next week Coke wants to predict extent to which running a 30-second Super Bowl ad will improve sales 14.1 – DEPENDENT AND INDEPENDENT VARIABLES o Terms Independent Variable (X) – explains the variation (change) in another variable Dependent variable (Y)
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X → Y Does NOT work in reverse 14.2 – CORRELATION ANALYSIS o The Basics Measure strength / direction of linear relationship b/w 2 variables Calculate correlation coefficient (R) - Provides value describing relationship Hypothesis test to decide if relationship b/w 2 variables is strong enough to be considered statistically significant
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  • Fall '12
  • Donnelly
  • Normal Distribution, Null hypothesis, Hypothesis testing, Statistical hypothesis testing

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