Explain why selecting a regression equation using stepwise regression is considered superior to selecting one using forward selection.

Q: What is a difference – or differences -- between the AIC and BIC measures used in stepwise…

A: AIC and BIC are widely used in model selection criteria. AIC means Akaike’s Information Criteria and…

Q: regression analysis should limited in an applied business setting

A: Regression analysis method  can be used to establish  relationship between different variables by…

Q: In comparing two multiple regression models, one with variables that are a subset of the other,…

A: R-squared:The coefficient of determination (R2) is defined as the proportion of variation in the…

Q: Explain how to determine the variance ination factor (VIF) for a predictor variable x1 in a…

A: Variance inflation factor:Variance inflation factor of a variable is the ratio of the variance of…

Q: Explain the Two stage Least Square estimator in the general IV regression model?

A: Regression analysis of twostage minus squares (2SLS) is a statistical technique used in the analysis…

Q: Let’s assume a particular sample of Math classes contains several student GPAs from a minimum of 3.3…

A: A sample of Math classes contains student GPA's from a minimum of 3.3 and a max of 4.0. The…

Q: e used to make p

A: Regression: The statistical technique that expresses the relationship between two or more variables…

Q: Using the linear regression line from the sample, y = a + b x + e, if b = 0 then R 2 = 0.…

A: Solution: From the given information, the linear regression line is y=a+ bx+e.

Q: What is regression? why are there in general two regression lines? under what conditions properties…

A: Regression is a statistical method used in finance, investing, and otherdisciplines which attempts…

Q: How to get Bo, B₁, B2, B3, B4 of a Multiple Linear Regression with 4 regressors. What formula should…

A: From the given information, the formula for 4 regressors for a multiple linear regression has to be…

Q: One of the ways to address the problem of multicollinearity is to do ridge regression. Give a short…

A: Please find the explanation below. Thank you.

Q: Define Time-lagged regression analysis?

A: Introduction The time series data is consists of a sequence of data values indexed in time order.

Q: Which one is more flexible between PCR and ordinary linear regression? Why?

A: The ordinary linear regression model is a statistical method of analysis to estimate the…

Q: Define the ADL and GLS Estimators of Regression.

A:

Q: Explain which characteristic of the STA leads to a consideration of a logistic model as opposed to a…

A: In a standardized test, the outcome of interest is typically whether a student passes or fails the…

Q: Explain Multivariate regression coefficients?

A: Independent and dependent variables in regression: In a simple regression, the variable of interest,…

Q: You will not obtain a high R-squared value in a multiple regression if all the partial slope…

A: We need to use the concept of R square and regression.

Q: A researcher is investigating possible explanations for deaths in traffic accidents. He examined…

A: The formula of R -squared is,where,SSR denotes the sum of regression squaresSSE denotes the error…

Q: In simple linear regression analysis, it is desired to test whether the regression coefficient is…

A: Solution: Given information: Test statistic t = 19 S.E(β1^)= 8    standard error of the regression…

Q: Explain the IV Regression Assumptions?

A: Introduction: A multiple regression analysis is often found to contain endogenous predictor…

Q: Assume that you want to predict Company Performance based on the linear effects of Trust and…

A: Company performance is the dependent variable and Trust among employees and Commitment of employees…

Q: Show the difference between the "complete model" and "reduced model of regression analysis?

A:

Q: What are the assumptions of the classical regression model(CRM)?

A: Assumption 1: Linear Parameter and correct model specification Assumption 1 requires that the…

Q: Plot the linear regression line  y = 3 + 0.5x without using excel with proper labelling

A: Introduction:  linear regression is a linear approach for modelling the relationship between a…

Q: What is the difference between fitting longitudinal body weight with a non-linear model such as…

A: We are asked about the difference between fitting longitudinal body weight with a non-linear model…

Q: The issue of multicollinearity impacted the 'vadity and trustworthiness' of a regression model.…

A: Multicollinearity refers to the situation when the independent variables of the regression are…

Q: We estimate a simple regression Grade-B,+B,Effort + u where the red line represents Qur estimate for…

A: Given regression modelGrade=β0+β1Effort+u

Q: What is measured by the standard error of estimaate for a regression equation?

A: Standard Error of estimate(SEE) is given by, SEE=(Y-Y^)2NY: Actual ValueY^: predicted valuesN:…

Q: What are the Assumptions of Extended Least Squares in the Multiple Regression Model?

A:

Q: What is the difference between a Multiple Regression model and a Multivariate Regression model?…

A: Regression

Q: The experimenters conducted a regression analysis on the results and made the output from the…

A: Given information: Equation: Relative performance estimate = 12.10 (theories of intelligence score)…

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.