This Time Series Analysis Consists of Long Run Tests including Descriptive Analysis, Unit Root Test, Shapiro Wilk Test, Cointegration Test and VECM.
This Panel Data Analysis consists of Descriptive Analysis, Unit Root Test, Cointegration Test, Pooled OLS, Test for Homoscedasticity, Random Effects and Fixed Effects Models and Hausman Test.
Analysis of Cross Sectional Data Containing Two or Three Paired Numerical Variables. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Two or Three Unpaired Numerical Variables. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Paired Nominal and a Numerical Variable. Third Variable can be also be Provided which should give the information about Pairing of Samples. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Unpaired Nominal and a Numerical Variable. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Two Paired Nominal Variables. It consists of Descriptive Statistics and McNemar Test.
Analysis of Cross Sectional Data Containing Two Unpaired Nominal Variables. It consists of Descriptive Statistics and McNemar Test.
Analysis of Cross Sectional Data Containing Two Ordinal Variables. It consists of Descriptive Statistics and Spearman Rank Test for Correlation.
Test for Normality
Test for Equality of Variances
Paired t tests are used to test if the means of two paired measurements, such as pretest/posttest scores, are significantly different.
This test compares the averages/means of two independent or unrelated groups to determine if there is a significant difference between the two.
Mann-Whitney U Test AKA Mann Whitney Wilcoxon Test or the Wilcoxon Rank Sum Test, is the Non-Parametric Alternative Test to the Independent Sample T-Test.
Wilcoxon Signed Rank Test is a Non Parametric Alternative to the Dependent (Paired ) T-Test.
An ANOVA test is a type of statistical test used to determine if there is a statistically significant difference between two or more categorical groups by testing for differences of means using variance.
The Kruskal Wallis test is the non parametric alternative to the One Way ANOVA.
Repeated measures ANOVA is the equivalent of the one-way ANOVA, but for related, not independent groups, and is the extension of the dependent t-test.
The Friedman test is the non-parametric alternative to the one-way ANOVA with repeated measures.
Pearson's correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables.
Spearman's Rho is a Non-Parametric Test used to Measure the Strength of Association Between two Ranked Variables
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Descriptive Statistics about the Time Series Data
Augmented Dickey Fuller Test to check if the given Time-Series is Stationary or Not.
It will convert the Non-Stationary Time Series into Stationary by Logarithmic Transformation and Differencing.
Normality Test of the Given Time Series using the Shapiro Wilk Test
It will Test for the Cointegrated Equation using the Trace and Max Eigen Statistics.
It will Plot the Time Series against the Datetime Variable.
Descriptive Statistics about the Time Series Data
Augmented Dickey Fuller Test to check if the given Time Series of the Panel Data is Stationary or Not.
It will convert the Non-Stationary Time Series of the Panel Data into Stationary by Logarithmic Transformation and Differencing.
It will Test for the Cointegrated Equation using the Trace and Max Eigen Statistics.
It will Perform Pooled OLS (Ordinary Least Squares) Regression on the given Panel Data
It will plot the Line Chart for each Time Series of the Panel Data against the Date Time Variable.
This Includes Breusch Pagan Test and White Test.
Durbin Watson Test for Auto-Correlation for the Panel Data.
Random and Fixed Effects Models for Panel Data.
This Analysis Contains Descriptive Analysis of the Time Series Data along with Unit Root Test
This Analysis Contains Unit Root Test to check for Stationarity and Differencing/Log Transformation to convert Non-Stationary Time Series to Stationary.
This Analysis Contains Long Run Tests - Cointegration Test (Trace and Max Eigen Statistics) and VECM.
This Analysis Contains Pooled OLS, Random Effects, Fixed Effects etc.
This Analysis contains Unit Root Test, Differencing, Log Transformation, Cointegration Test and VECM.
Analysis of Cross Sectional Data Containing Two or Three Paired Numerical Variables. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Two or Three Unpaired Numerical Variables. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Paired Nominal and a Numerical Variable. Third Variable can be also be Provided which should give the information about Pairing of Samples. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Unpaired Nominal and a Numerical Variable. It consists of Shapiro Wilk Test, Levene Test, Correlation Test etc.
Analysis of Cross Sectional Data Containing Two Paired Nominal Variables. It consists of Descriptive Statistics and McNemar Test.
Analysis of Cross Sectional Data Containing Two Unpaired Nominal Variables. It consists of Descriptive Statistics and McNemar Test.
Analysis of Cross Sectional Data Containing Two Ordinal Variables. It consists of Descriptive Statistics and Spearman Rank Test for Correlation.
his Time Series Analysis Consists of Long Run Tests (Cointegration Test and VECM) as well as Short Run Tests (Granger Causality, Instantaneous Causality and Impulse Response Analysis).