jagomart
digital resources
picture1_Chi Square Test Ppt 69213 | Anova Item Download 2022-08-29 10-46-15


 173x       Filetype PPTX       File size 0.05 MB       Source: punjabiuniversity.ac.in


File: Chi Square Test Ppt 69213 | Anova Item Download 2022-08-29 10-46-15
dependent variable metric non metric discriminant independent metric regression analysis variable s binary logistic regression non metric hypothesis chi square test testing if the independent variable which is non metric ...

icon picture PPTX Filetype Power Point PPTX | Posted on 29 Aug 2022 | 3 years ago
Partial capture of text on file.
                                           Dependent Variable
                                                 Metric      Non-Metric
                                                           • Discriminant 
                 Independent       Metric      Regression      Analysis
                  Variable(s)                              •Binary/Logistic 
                                                              regression
                                Non-Metric     Hypothesis   Chi-square Test
                                                 testing
  • If the independent variable (which is non-
   metric) has two categories, we will use t-test
  • And if the independent variable has more than 
   two categories we will use F-test (ANOVA)
                    ANOVA
   • ANOVA uses F statistics which is the ratio of variances 
     between groups and variances with-in groups (error 
     variance)
   • If group means do not differ significantly, one can 
     believe that all group means come from same 
     population and do not differ
   • Larger the F statistics  Larger is the difference between 
     groups as compared to with-in group differences
   • F Statistics < 1  Indicates no significant difference in 
     the group means and thus H  is correct.
                         o
             Assumptions
   Normality:
   • Ho  Data are normally distributed
   • Steps to check overall normality
    –Analyze  Non parametric tests  Legacy dialogs  One sample K S test
    – p-value of K S Test > 0.05  Data are normally distributed
    –p-value of K S Test < 0.05  Use Non-parametric test
   • Steps to check category-wise normality
    –Analyze  Descriptive  Explore  Plots  Tick Normality plots with stats
   • If your sample size for different categories is comparable, and any 
    one or two categories are not normally distributed, even then, F & 
    t are very robust tests
    - Andy Field
                     Assumptions
    Homogeneity of Variance:
    • We assume that each sample comes from a population 
      with same variance. And thus, variance across samples is 
      homogeneous.
    • H   Variances across groups is equal or Homogeneous
        o
    • Steps to check overall Variance
       –Analyze  Descriptive statistics  Descriptives  Options  Tick 
        Variance 
    • Steps to check category-wise Variance
       –Analyze  Compare Means  Means  Options  Tick Variance
The words contained in this file might help you see if this file matches what you are looking for:

...Dependent variable metric non discriminant independent regression analysis s binary logistic hypothesis chi square test testing if the which is has two categories we will use t and more than f anova uses statistics ratio of variances between groups with in error variance group means do not differ significantly one can believe that all come from same population larger difference as compared to differences indicates no significant thus h correct o assumptions normality ho data are normally distributed steps check overall analyze parametric tests legacy dialogs sample k p value category wise descriptive explore plots tick stats your size for different comparable any or even then very robust andy field homogeneity assume each comes a across samples homogeneous equal descriptives options compare...

no reviews yet
Please Login to review.