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1

Parametric test of means

Statistical tests used to compare means assuming a specific distribution of the data.

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2

Non-parametric test of medians

Statistical tests used to compare medians without assuming a specific distribution of the data.

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3

Pearson Correlation Coefficient

Measures the statistical relationship between two continuous variables.

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4

Linear Regression

Predictive analysis to determine the relationship between predictor variables and an outcome variable.

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5

Spearman Correlation Coefficient (rho)

Nonparametric measure of association between two variables on an ordinal scale.

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6

Kendall Correlation Coefficient (tau)

Nonparametric measure of association between two variables on an ordinal scale.

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7

Mann-Whitney U test

Compares differences between two independent groups with ordinal or continuous data.

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8

Wilcoxon matched-pairs signed-ranks test

Nonparametric test to compare two sets of scores from the same participants.

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9

Kruskal-Wallis test

Nonparametric test to compare more than two independent groups on a continuous or ordinal dependent variable.

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10

McNemar chi-square test

Determines differences on a dichotomous dependent variable between two related groups.

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11

T-test

Compares means of two groups; can be one-sample, two-sample, or paired.

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12

ANOVA (F-test)

Compares statistical models fitted to data to identify the best fit for the population.

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13

Chi-square test

Used for testing relationships between categorical variables.

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14

Simple Linear Regression

Which variables are significant predictors of the outcome variable, and in what way do they indicated by the magnitude and sign of the beta estimates-impact the outcome variable?

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15

Multiple Linear Regression

1 dependent variable (interval or ratio), 2+ independent variables (interval or ratio or dichotomous)

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16

Logistic Regression

1 dependent variable (dichotomous), 2+ independent variable(s) (interval or ratio or dichotomous)

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17

Ordinal Regression

1 dependent variable (ordinal), 1+ independent variable(s) (nominal or dichotomous)

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18

Multinomial Regression

1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio or dichotomous)

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19

Discriminant Analysis

1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio)

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20

Mann-Whitney U test

Appropriate test if the first variable is ordinal and the second variable is dichotomous.

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21

Bivariate Analysis

It is used to find patterns, relationships, and associations between two variables. This can aid in making predictions and inferences.

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22

Correlation Coefficient

Measures the strength and direction of the relationship between two continuous variables

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23

Scatter Plots

Visual representation showing the relationship between two continuous variables.

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24

Simple Linear Regression

Models the linear relationship between two continuous variables.

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25

Steps in Bivariate Analysis

Define the variable, select the appropriate technique, visualize data, compute statistical measure, and interpret the results.

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26

Correlation

Indicates the degree to which two variables move in relation to each other.

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27

Regression Analysis

A statistical method to model and analyze the relationships between variables.

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28

Linearity

Assumption that the relationship between variables is linear, which can be assessed using scatter plots or correlation coefficients.

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29

Homoscedasticity

The variance of residuals should be constant across all levels of the independent variable.

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30

Independence

This is crucial for valid inferential statistics.

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31

Confidence intervals

Provide a range within which the true value of the parameter lies with a certain level of confidence.

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32

p-Value

Indicates the probability that the observed relationship is due to chance.

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33

Applications of Bivariate Analysis

Business and Economics, Healthcare, and Social Sciences.

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34

Outliers

It can significantly affect the results of bivariate analysis.

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35

Challenges and consideration in using Bivariate Analysis

Outliers, Non-linearity, and confounding variables

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36

Ethical Considerations in using Bivariate Analysis

Transparency, Bias and Fairness

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37

Cross tabulation (Contingency tables)

Displays the frequency distribution of variables.

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38

Normality

Assumption that the data is normally distributed.

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39

Healthcare

Studying the relationship between patient age and recovery time, or smoking and lung cancer incidence.

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40

Social Sciences

Investigating the relationship between education level and income, or social media use and mental health.

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