Lab 10 Activity

Lab 10 Activity FOR THE FIRST BULLET POINT: Report the results for each analysis in the correct format.  For example: r (18) = .35, p = .024 FOR THE SECOND BULLET POINT: Next, write up a few sentences describing the results for each analysis that you did.  Report the value of each statistic, degrees of freedom, and p-value and indicate whether this is significant. Additionally, for t-tests and ANOVA report the means for each level of the independent variable. The powerpoints for Lab 9 and Lab 10 provide information on how to format the results! 1. T-tests looking at differences between men and women on 1) anxiety, and 2) exam scores. ·         For anxiety: t(16)=2.656, p= .559                         For exam scores:  t(16)= -2.235, p=.951     ·     An independent samples t-test was conducted to compare the differences between men and women on anxiety and exam scores. For men, there was a significant difference for anxiety t(16)=2.656, p= .559 (M=3.3889,SD=1.26930) as they held more anxiety than their women counterparts  t(16)=2.656, p= .559, (M=5.111, SD=1.47432). For men and women there was not a significant difference in exam scores. For men,  t(16)= -2.235, p=.951 , (M=83.5556, SD=2.46143). For women, t(16)= -2.235, p=.951 , (M=75.4444, SD=2.66725). 2. An ANOVA on exam scores among the three coffee groups. You will need to conduct Tukey posthoc tests as part of this analysis. ·F(2,15)=8.209, p=.004           · A one-way between-groups ANOVA was conducted comparing exam score among three coffee groups. The overall omnibus test was significant F(2,15)=8.209, p<.004. Post-hoc comparisons using Tukey’s HSD indicated that Group 1 ( M = 75.3333, SD = 4.67618) ( p < .004). Group 2  ( M = 88, SD =6.48074)(p<.004). Group 3 ( M = 75.1667, SD = 7.41395) (p<.004) .           3. Pearson correlation between anxiety and exam score. ·          r(18)= 1, p= .029 • We calculated a Pearson product-moment correlation to examine the relation between anxiety and exam score.. We found a correlation between anxiety and exam scores.   r (18) = 1, p = .029 . ·          

4. Simple regression analysis predicting exam score from anxiety. ·           2 = .264 • R 2 = 26.4 • The level of anxiety does not explain a significant amount of variance in exam scores reported., R 2 = 26.4 b = slope, t (df) = t-statistic, p = p-value • b = -2.745, t (17) =-2.396 ., p = .015. • The level of anxiety is a statistically significant predictor of exam score. , b = -2.745, t (17) = .-2.396, p = .015 ·           Information needed to fill out homework: *replace numbers with your data found* Reporting Correlation Results: r (N) = r , p = p-value • r (41) = .13, p = .422 • We calculated a Pearson product-moment correlation to examine the relation between the number of hours watching television a week and the number of movies seen in the past month. We found no correlation between the hours watching television and the number of movies seen last month, r (41) = .13, p = .422 . Reporting Regression Results; R 2 = R-squared value • R 2 = 1.7%. • The number of movies one has seen in the past month does not explain a significant amount of variance in the amount of TV one watches, R 2 = 1.7%. b = slope, t (df) = t-statistic, p = p-value • b = 0.22, t (40) = .81, p = .422. • The number of movies one has seen in the past month is not a statistically significant predictor of television viewing, b = 0.22, t (40) = .81, p = .422. Reporting Independent t-test: • t (df) = t-value, p = XXX