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Statistics Idiots Guide! Dr. Hamda Qotba, B.Med.Sc, M.D, ABCM.

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Presentation on theme: "Statistics Idiots Guide! Dr. Hamda Qotba, B.Med.Sc, M.D, ABCM."— Presentation transcript:

1 Statistics Idiots Guide! Dr. Hamda Qotba, B.Med.Sc, M.D, ABCM

2 Dr.H.Qotba2 Definition Statistics is the science of collecting, organizing, summarising, analysing, and making inference from data Descriptive stat. Includes collecting, organizing, summarising, analysing, and presenting data Inferential stat. Includes Making inferences, hypothesis testing Determining relationship, and making prediction

3 Dr.H.Qotba3 Variables Quantitative Discrete Continuous Qualitative Ordinal Categorical

4 Dr.H.Qotba4 Parametric Vs. non parametric tests Parametric: decision making method where the distribution of the sampling statistic is known Non-Parametric: decision making method which does not require knowledge of the distribution of the sampling statistic

5 Dr.H.Qotba5 t-Test Compare the means of a continuous variable into samples in order to determine whether or not the difference between the 2 expected means exceed the difference that would be expected by chance What is probability the mean will differ?

6 Dr.H.Qotba6 Requirements The observations are independent Drawn from normally distributed population Sample size 30 use normal curve z test (binomial test)

7 Dr.H.Qotba7 Types of t-Test One sample t test: test if a sample mean for a variable differs significantly from the given population with a known mean Unpaired or independent t test: test if the population means estimated by independent 2 samples differ significantly (group of male and group of female) Paired t test: test if the population means estimated by dependent samples differ significantly (mean of pre and post treatment for same set of patients

8 Dr.H.Qotba8 chi² test Used to test strength of association between qualitative variables Used for categorical data

9 Dr.H.Qotba9 Requirements Data should be in form of frequency Total number of observed must exceed 20 Expected frequency in one category or in any cell must be >5 (When 1 of the cells have <5 in observed yats correction) or if (When 1 of the cells have <5 in expected fischer exact) The group compared must be approximately the same

10 Dr.H.Qotba10 Correlation and Regression Methods to study magnitude of the association and the functional relationship between two or more variables

11 Dr.H.Qotba11 Correlation Denote strength of relationship between variables

12 Dr.H.Qotba12 Regression Method that’s indicate a mathematical relationship between a dependant and one or more independent variables Simple linear regression and multiple regression are appropriate for continuous variables like(BP, Weight) Logistic regression applicable for binary response like alive/dead

13 Dr.H.Qotba13 Measures If parametric Pearson correlation coeff. »Continuous variables »Linear relationship If nonparametric Spearman rank »Both variables are continuous Kendall’s tau »Two ordinal or one ordinal one continuous

14 Dr.H.Qotba14 ANOVA is used to uncover the main and interaction effects of categorical independent variables (called "factors") on an interval dependent variable

15 Dr.H.Qotba15 Types of ANOVA One-way ANOVA tests differences in a single interval dependent variable among two, three, or more groups formed by the categories of a single categorical independent variable.

16 Dr.H.Qotba16 Two-way ANOVA analyzes one interval dependent in terms of the categories (groups) formed by two independents, one of which may be conceived as a control variable Multivariate or n-way ANOVA. To generalize, n-way ANOVA deals with n independents. It should be noted that as the number of independents increases, the number of potential interactions proliferates

17 Dr.H.Qotba17 How to select appropriate statistical test Type of variables Quantitative (blood pres.) Qualitative (gender) Type of research question Association Comparison Risk factor Data structure Independent Paired matched

18 Dr.H.Qotba18 Body of research question Association of 2 variable(dep, indep) Spearman Correlation linear Regression Quantitative 2 out come T test 3+out come ANOVA categoricalQuantitative Log. regressionQuantitativecategorical chi-squarecategorical Test Types of variable Dependent independent

19 Dr.H.Qotba19 Comparing (difference) variables chi-square Kruskal wallis ANOVA McNemar chi-square* Wilcoxon Mann- Whitney Paired T test T test Quantitative Ordinal Categorical Number of independent variable 2 groups paired data >2groups Variable * When 1 of the cells have <5 in expected fischer exact When 1 of the cells have <5 in observed yats correction

20 Dr.H.Qotba20 Looking for Risk Factor Test Types of variables Dependent several indepen. Multiple log. Regression categorical ANOVAcategoricalquantitative Linear, log regression quantitative

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