Statistics - Assignment Example

Chapter 6 &7 Answers C 2. A 3. A 4. B 5. C 6. B 7. A 8. B 9. B 10. C Question 22. r= r 10x 353 -55 x 59)/{(10x415)-552}1/2x {(10x 384)-592} = 285/33.5410 x18.9473 = 285/635.5112 = 0.45r= 0.45 indicates that there is a low positive correlation between mood and creativity.Predicted creativity would be the average creativity score for all participants that is 59/10= 5.9. Standard deviation= 1.78 The error would be + or – 1.78Regression model y=a + bx a = = (59- 0.79x55)/10 =1.56 Slope b= (Roger,73) = (10x 353-59x55)/(10x415-592) = 190905/241369 = 0.

79 and x =3Y= 1.56 +0.79 x 3Ý=3.93The error would reduce using the regression analysis by 1.97Question 23The statistics to be used in this case is correlation, if there is no correlation between his performance in colleges and STAT scores.Correlation coefficient doesn’t imply lack of relation between variables but lack of linear relationship between the variable. The fact that the r value here measures the relationship between a STAT scores independent variable and college performance dependent variable, where as a particular variable may be dependent on several independent variables like materials and time used for studying in college (Roger,86).

Question 14.With a perfect correlation r2 is equal to 1 indicating that the proportion of explained variation is 100%. This implies that the variable have a positive linear relationship and if plotted on scatter plot graph the regression line plotted would pass through all point, thus total variation would be explained (Roger,85).Most of the variables been investigated usually do not have a strong linear relationship thus the correlation is usually range from 9% to 25%.Question 18.In the first case r=+0.

2 thus, r2= 0.04 that is only of 4% of the variation can be explained, while in the second instance r= -0.4 thus r2 =0.16 that is 16% of the variations can be explained thus r= -0.4 is more scientifically important since it explains 12% more of the total variation (Roger,89) Works CitedRoger, Kirk. Statistics: An Introduction. Belmont: Cengage Learning, 2007.