Islamiat Quiz Archives - GM Statistics

MCQs Correlation and Regression 6

This Quiz contains MCQs about Correlation and Regression Analysis, Multiple Regression Analysis, Coefficient of Determination (Explained Variation), Unexplained Variation, Model Selection Criteria, Model Assumptions, Interpretation of results, Intercept, Slope, Partial Correlation, Significance tests, OLS Assumptions,…

Short Questions: Normal and Standard Normal Distribution

The following post is about Short Questions related to Normal and Standard Normal Distribution. Q1: What is a standard normal variable? Ans: The variable $Z=\frac{X-\mu}{\sigma}$ which measures the deviations of variable $X$ from the…

Normality Test for the Error Term

In this post we will learn about Normality Test for the Error term using (1) Anderson-Darling Test and (2) Shapiro-Wilk test. Normality of error (disturbance) term is a basic assumption for many statistical procedures….

Non-Linear Regression Model

In the least square method, the regression model is established in such a way that the sum of the squares of the vertical distances of different points (residuals) from the regression line is minimized. When the relationship between the variables is not linear (one has a non-linear regression model), one may try to transform the […]

Logistic Regression Models in R

In logistic regression models, the response variable ($y$) is of categorical (binary, dichotomous) values such as 1 or 0 (TRUE/ FALSE). It measures the probability of a binary response variable based on mathematical equation relating the values of response variable with the predictor(s). The built-in function glm() can be used to perform logistic regression analysis. […]

Generalized Linear Models (GLM) in R

The generalized linear models (GLM) can be used when the distribution of the response variable is non-normal or when the response variable is transformed into linearity. The GLMs are flexible extensions of linear models that are used to fit the regression models to non-Gaussian data. The basic form of a Generalized linear model is\begin{align*}g(\mu_i) &= […]

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