کتاب سرخ‌کن بزرگ با هوای فشرده

Introduction 1. Descriptive statistics and inferential statistics 1.1. Subareas of statistics 1.2. Descriptive statistics 1.3. Inferential statistics 2. Level of measurement 2.1. Nominal variables 2.2. Ordinal variables 2.3. Categorical variables 2.4. Metric variables 2.5. Ratio scale and interval scale 3. Sampling 3.1. Full or total survey vs. random sample 3.2. Population and sample 3.3. Types of sampling? 3.4. Probability selection 3.5. Deliberate selection 3.6. Arbitrary selection 3.7. Sample selection in online surveys 3.8. Sample description in bachelor or master thesis 4. Location parameter 4.1. Mean value (arithmetic mean) 4.2. Geometric mean and quadratic mean 4.3. Median 4.4. Mean and median in comparison 4.5. Mode (modal value) 4.6. Advantage and disadvantage of the mean, median and mode 5. Dispersion parameter 5.1. Standard deviation 5.2. Variance 5.3. Difference between variance and standard deviation 5.4. Range 5.5 Quartile 5.6. Interquartil range 5.7. Example dispersion parameter 6. Frequency table 6.1. Absolute and relative frequencies 6.2. Valid percent 6.3. Frequency table in statistics 6.4. Example frequency table 6.5. Frequency table in APA style 7. Contingency table (Crosstab) 7.1. Crosstabs in statistics 7.2. Interpretation of crosstabs 7.3. Example crosstab 7.4. Testing a crosstab for significance 8. Charts 8.1. Bar chart 8.2. Bar chart for frequencies 8.3. Grouped bar charts 8.4. Bar chart for mean values 8.5. Error bar 8.6. Example bar chart 8.7. Histogram 8.8. Histogram example 8.9. Bar chart vs. Histogram 8.10. Scatter plot 8.11. Line charts 8.12. Boxplot 8.13. Bland-Altman plot 8.14. Create charts online with DATAtab 9. Inferential Statistics 9.1. Hypotheses 9.2. Null and alternative hypothesis 9.3. Difference and correlation hypotheses 9.4. Directional and undirectional hypotheses 9.5. Hypothesis Testing 9.5.1. Hypothesis testing and the null hypothesis 9.5.2. The uncertainty in hypothesis testing 9.5.3. Level of significance or probability of error 9.5.4. Example significance level and p-value 9.5.5. Types of errors 9.5.6. Significance vs effect size 9.5.7. Choosing the appropiate hypothesis test 9.5.8. Examples for hypothesis tests 9.6. The p-value 9.6.1. Defining the p-value 9.6.2. Using the p-value 9.6.3. Significance level 9.6.4. One-tailed p-values 9.6.5. Calculate p-value 9.6.6. Statistical tests and the p-value 9.6.7. Specify the p-value 10. Checking assumptions of statistical tests 10.1. Levene test of variance homogeneity 10.2. Levene test example 10.3. Interpreting the Levene Test 10.4. Normality test 10.4.1. Statistical test for normal distribution 10.4.2. Disadvantage of analytical tests for normal distribution 10.4.3. Graphical test for normal distribution 10.5. Multicollinearity test 10.5.1. How to avoid multicollinearity? 10.5.2. Multicollinearity test 10.5.3. Tolerance value 10.5.4. VIF Multicollinearity 11. Statistical tests for differences 11.1. One sample t-test 11.1.1. Basics of the one sample t-test 11.1.2. Examples of a t-test for one sample 11.1.3. Assumptions of the one-sample t-test 11.1.4. Hypotheses for the one-sample t-test 11.1.5. Calculation of the one-sample t-test 11.1.6. One sample t-test with example 11.1.7. APA format | One sample t-test 11.2. T-test for independent samples (unpaired t-test) 11.2.1 Using and independent t-test 11.2.2. Purpose of the independent/unpaired t-test 11.2.3. Examples for the unpaired t-test 11.2.4. Research question and hypotheses for the unpaired t-test 11.2.5. Assumptions unpaired/independent t-test 11.2.6. Calculate t-test for independent samples 11.2.7. Confidence interval for the true mean difference 11.2.8. One-sided and two-sided unpaired t-test 11.2.9. Effectsize unpaired t-test 11.2.10. Example t-test for independent samples 11.2.11. Interpretation t-test for independent samples 11.2.12. Report a t-test for independent samples 11.3. Paired-samples t-test 11.3.1. Why do you need the dependent t-test? 11.3.2. What is the advantage of a dependent t-test over an independent t-test? 11.3.3. Examples of the t-test for paired samples 11.3.4. Research question and hypotheses of the paired t-test 11.3.5. Assumptions paired t-test 11.3.6. Calculating a paired t-test 11.3.7. Example t-test for dependence samples with DATAtab 11.3.8. Interpretation of a t-test for dependent samples 11.3.9. Effect size dependent t-test 11.4. Mann-Whitney U test 11.4.1. Assumptions Mann-Whitney U test 11.4.2. Hypotheses Mann-Whitney U test 11.4.3. Calculate Mann-Whitney U test 11.4.4. Calculate Mann-Whitney U test with tied ranks 11.4.5. Mann-Whitney U test Example with DATAtab 11.4.6. Interpret Mann-Whitney U test 11.4.7. Mann-Whitney U test and effect size 11.5. Wilcoxon test 11.5.1. Assumptions of the Wilcoxon test 11.5.2. Hypotheses in the Wilcoxon test 11.5.3. Wilcoxon test and test power 11.5.4. Calculate Wilcoxon test 11.5.5. Calculate Wilcoxon signed-rank test with tied ranks 11.5.6. Effect size in the Wilcoxon signed-rank test 11.5.7. Example Wilcoxon test with DATAtab 12. Frequency analysis 12.1. Binomial test 12.1.1. Hypotheses in binomial test 12.1.2. Binomial test calculation 12.1.3. Binomial test example 12.1.4. Interpretation of a Binomial Test 12.2. Chi-square test 12.2.1. Applications of the Chi-Square Test 12.2.2. Calculation of Chi-Square test 12.2.3. Chi-Square Test of Independence 12.2.4. Chi-square distribution test 12.2.5. Chi-square homogeneity test 12.2.6. Effect size for Chi-square test 12.2.7. Effect size vs. p-value 12.2.8. Example: Chi-square test with DATAtab 13. Statistical tests to test for differences in more than two groups 13.1. Analysis of Variance (ANOVA) 13.1.1. Why not calculate multiple t-tests? 13.1.2. Difference between one-way and two-way ANOVA 13.1.3. Analysis of variance with and without repeated measures 13.2. One-factor ANOVA 13.3. One-factor ANOVA example 13.4. Analysis of variance hypotheses 13.5. Assumptions of one-way analysis of variance 13.6. Welch's ANOVA 13.7. Effect size Eta squared (n2) 13.8. Two factor analysis of variance 13.9. Calculate example with DATAtab 13.10. Repeated Measures ANOVA 13.10.1. What are dependent samples? 13.10.2. Difference of analysis of variance with and without repeated measurements 13.10.3. Example of repeated measures ANOVA 13.10.4. Research question and hypotheses 13.10.5. Assumptions ANOVA with repeated measures 13.10.6. Results of the one-factor analysis of variance with repeated measures 13.10.7. Effect size for repeated measures ANOVA 13.10.8. Bonferroni Post-hoc-Test 13.10.9. Calculate ANOVA with measurement repetitions with DATAtab 13.10.10. Calculate a repeated measures ANOVA by hand 13.11. Two-way ANOVA (without repeated measures) 13.11.1. What is a factor? 13.11.2. Two factors 13.11.3. Example Two-Way ANOVA 13.11.4. Hypotheses 13.11.5. Assumptions 13.11.6. Calculation of a two-way ANOVA 13.11.7. Calculating two-way ANOVA with DATAtab 13.11.8. Interpreting two-way ANOVA 13.11.9. Interaction effect 13.12. Two-way ANOVA with measurement repetition 13.12.1. Sample with measurement repetition 13.12.2. Example two-way ANOVA with repeated measures 13.12.3. Hypotheses 13.12.4. Assumptions of the two-way analysis of variance with repeated measures 13.13. Kruskal-Wallis test 13.13.1. Examples for the Kruskal-Wallis test 13.13.2. Research question and hypotheses in the Kruskal-Wallis test 13.13.3. Assumptions of the Kruskal-Wallis test 13.13.4. Calculate Kruskal-Wallis test 13.13.5. Kruskal-Wallis test example 14. Correlation 14.1.1. Correlation and causality 14.1.2. Correlation and causality example 14.1.3. Correlation interpretation 14.1.4. Direction of correlation 14.1.5. Strenght of correlation 14.1.6. Scatter plot and correlation 14.1.7. Test correlation for significance 14.1.8. Directional and non-directional hypotheses 14.2. Pearson correlation analysis 14.2.1. Pearson Correlation assumptions 14.3. Spearman rank correlation 14.4. Point biserial correlation 14.5. Partial correlation 14.5.1. Calculation of the partial correlation 14.5.2. Partial correlation example 14.5.3. Partial correlation 2nd order 14.5.4. Example: Pearson correlation 14.5.5. Directional (one-sided) correlation hypothesis 15. Regression analysis 15.1. Basic of regression 15.1.1. Using a regression analysis 15.1.2. Types of regression analysis 15.1.3. Dummy variables and Reference category 15.1.4. Examples of regression: 15.2. Linear regression 15.2.1. Simple Linear Regression 15.2.2. Multiple Linear Regression 15.2.2.1. Multiple Regression vs. Multivariate Regression 15.2.2.2. Coefficient of determination 15.2.2.3. Adjusted R2 15.2.2.4. Standard estimation error 15.2.2.5. Standardized and unstandardized regression coefficient 15.2.2.6. Assumptions of Linear Regression 15.2.2.7. Linearity 15.2.2.8. Homoscedasticity 15.2.2.9. Normal distribution of the erro 15.2.2.10. Multicollinearity 15.2.2.11. Significance test and Regression 15.2.2.12. Example linear regression 15.2.2.13. Interpretation of the results 15.2.2.14. Presenting the results of the regression 15.2.3. Logistic regression 15.2.3.1. What is logistic regression? 15.2.3.2. Logistic regression and probabilities 15.2.3.3. Calculate logistic regression 15.2.3.4. Logistic function 15.2.4. Maximum Likelihood Method 15.2.4.1. The Likelihood Function 15.2.4.2. Maximum Likelihood Estimator 15.2.5. Multinomial logistic regression 15.2.6. Interpretation of the results 15.2.7. Pseudo-R squared 15.2.8. Null Model 15.2.9. Cox and Snell R-square 15.2.10. Nagelkerkes R-square 15.2.11. McFadden's R-square 15.2.12. Chi2 Test and Logistic Regression 15.2.13. Example logistic regression 15.2.14. Calculating logistic regression with DATAtab 16. Factor analysis 16.1. What is a factor? 16.2. Example factor analysis 16.3. Research questions factor analysis 16.4. Factor load, eigenvalue, communalities 16.5. Correlation Matrix 16.6. Factor Analysis and dimensionality 16.6.1. Eigenvalue criterion (Kaiser criterion) 16.6.2. Scree-Test 16.6.3. Communalities 16.6.4. Component matrix 16.6.5. Rotation Matrix 16.6.6. Varimax Rotation 17. Cluster analysis 17.1. Example Hierarchical Cluster Analysis 17.1.1. Calculating a Hierarchical Cluster Analysis 17.1.2. Distance between two points 17.1.3. Euclidean Distance 17.1.4. Manhattan Distance 17.1.5. Maximum Distance 17.1.6. Linking methods 17.1.6.1. Single-linkage 17.1.6.2. Complete-linkage 17.1.6.3. Average-linkage 17.1.7. Example Hierarchical Cluster Analysis 17.1.7.1. Calculate hierarchical cluster analysis with DATAtab 17.2. K-means cluster analysis 17.2.1. Optimal cluster number 17.2.2. Elbow curve 17.2.3. Scaling data for k-means clustering 17.2.4. K-means clustering calculator 17.2.5. Key Features 18. What does association analysis do? 18.2. Market Basket Analysis Example 18.3. Interpreting the results of a Market basket analysis 18.3.1. Frequency 18.3.2. Support 18.3.3. Confidence 18.3.4. Lift 18.3.5. Market basket analysis and data mining 18.3.6. Critical note on the market basket analysis 19. Cronbach's Alpha 19.1. Latent variables 19.2. Assumptions for Cronbach's Alpha 19.3. Calculate Cronbach's Alpha 19.4. Example Cronbach's Alpha 19.5. Interpret Cronbach's Alpha 20. Cohen's Kappa 20.1. Cohen's Kappa Example 20.2. Inter-rater reliability 20.3. Use cases for Cohen's Kappa 20.4. Cohen's Kappa reliability and validity 20.5. Calculate Cohen's Kappa 20.6. Cohen's Kappa Interpretation 20.7. Cohen's Kappa Standard Error (SE) 20.8. Calculating Standard Error of Cohen's Kappa 20.9. Interpreting Standard Error 20.10. Calculate Cohen's Kappa with DATAtab 21. Weighted Cohen's Kappa 21.1. Reliability and validity 21.2. Calculating weighted Cohen's Kappa 21.3. Calculate expected frequency 21.4. Calculate weighting matrix 21.5. Linear and quadratic weighting 21.6. Calculate weighted Kappa 21.7. Calculating Cohen's weighted kappa with DATAtab 22. Fleiss Kappa 22.1. Fleiss Kappa Example 22.2. Fleiss Kappa with repeated measurement 22.3. Fleiss Kappa reliability and validity 22.4. Calculate Fleiss Kappa 22.5. Fleiss Kappa interpretation 22.6. Calculate Fleiss Kappa with DATAtab 23. Survival time analysis 23.1. Basics of survival time analysis 23.2. Use cases for survival time analysis 23.3. Example of survival time analysis 23.4. Censored data 23.5. Methods of survival time analysis 23.6. Kaplan-Meier Curve 23.6.1. Survival rate 23.6.2. Interpreting the Kaplan-Meier curve 23.6.3. Calculating the Kaplan-Meier curve 23.6.4. Draw Kaplan Meier curve 23.6.5. Censored data 23.6.6. Comparing different groups 23.6.7. Kaplan-Meier curve assumptions 23.6.8. Create Kaplan Meier curve with DATAtab 23.7. Log Rank Test 23.7.1. Hypotheses in the Log Rank Test 23.7.2. Assumptions for the LOg Rank Test 23.7.3. Calculate Log Rank Test 23.7.4. Calculate Log Rank Test with DATAtab 23.8. Cox regression 23.8.1. Survival time analysis 23.8.2. Censoring 23.8.3. Cox Regression Example 13.8.4. Calculate Cox Regression with DATAtab 23.8.5. Interpretation of the Cox Regression 23.8.6. Assumptions of a Cox Regression 23.8.7. Calculate survival time analysis with DATAtab References