1 / 18

12. Statistical Analysis

12. Statistical Analysis. SPSS: Statistical Package for the Social Sciences. The Statistics Approach. Probabilistic statements: - make probabilistic statements about a population on the basis of information available from a sample drawn.

shandi
Télécharger la présentation

12. Statistical Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 12. Statistical Analysis

  2. SPSS: Statistical Package for the Social Sciences

  3. The Statistics Approach • Probabilistic statements: - make probabilistic statements about a population on the basis of information available from a sample drawn. Example: 10% of adults play tennis, can be 95% confident that the proportion of adults that play tennis is between 9% and 11%

  4. The Statistics Approach (continued) • The normal distribution: Figure 12.1. Page 335. • Significance • The null hypothesis (H0) H0 (null hypothesis): - there is no significant difference - there is no significant difference or relationship

  5. The Statistics Approach (continued) H1 (alternative hypothesis): - there is significant difference - there is significant relationship Researcher is interested in H1; Deductive approach; The hypothesis is set up in advance of the analysis, possibly within a theoretical framework.

  6. The Statistics Approach (continued) Example: Study of leisure participation pattern; Sample is 1000 adults; The study focuses on relative popularity of golf and tennis. H0: tennis and golf participation levels are the same. H1: tennis and golf participation levels are significant different.

  7. The Statistics Approach (continued) • Dependent And Independent Variables: - the independent variable influences the dependent variable? - one variable can be dependent on a number of independent variables

  8. Linear Regression • Quantitative analysis • If the correlation between 2 variables is consistent enough, one variable can be used to predict the other

  9. Linear Regression (continued) Example 1: Days Participation = a + b Age Y = a + b X a = intercept b = slope Y = dependent variable = Days Participation X = independent variable = Age

  10. Linear Regression (continued) Example 2: Trips = a + b Income Y = a + b X Y = dependent variable = Trips X = independent variable = Income a = intercept b = slope

  11. SPSS And Regression • Interested: - value of the regression coefficient - R (r) = correlation coefficient r = 0 = no relationship between two variables r = +1 = perfect positive correlation between two variables r = - 1 = perfect negative correlation between two variables 0 < r < +1 : some positive correlation -1 < r < 0 : some negative correlation

  12. SPSS And Regression (continued) • - R2 (r2) - F Test - t Test - ANOVA (Analysis Of Variance)

  13. SPSS And Regression (continued) Example 1: Figure 12.20 Page 360 Income (independent) by holiday expenditure (dependent) Model R R Square AdjR Square Std Error Est 1 0.915 0.836 0.833 104.51

  14. SPSS And Regression (continued) Anova Model SS df MS F Sig 1 Reg 2,679,971.336 1 2,679,971.336 245.361 0.000 Res 524,283.164 48 10922.566 Total 3,204,254.500 49

  15. SPSS And Regression (continued) Coefficient Model B Stand Error t Sig 1. Constant -323.493 49.890 -6.484 0.000 Income 52.563 3.356 15.664 0.000 Holiday Expenditure = -323.493 + 52.563 Income Y = -323.493 + 52.563 X

  16. SPSS And Regression (continued) Example 2: Figure 12.23 Page 364 Model R R Square AdjR Square Std Error Est 1 0.580 0.336 0.308 1.87

  17. SPSS And Regression (continued) Anova Model SS df MS F Sig 1 Reg 83.023 2 41.512 11.907 0.000 Res 163.857 47 3.486 Total 246.880 49

  18. SPSS And Regression (continued) Coefficient Model B Stand Error t Sig 1. Constant -3.493 1.316 -2.654 0.011 Income 0.056 0.084 0.662 0.511 Age 0.227 0.076 2.969 0.005 Reg Line?

More Related