regression analysis

  1. N

    Unbiasedness of the Variance of the Error Term in an intercept-only Model

    Hi folks, Is there anyone that might help me with the following derivation to show that the estimated variance error term is unbiased? I have managed to do the first and the second step, however I am currently stuck at the third step since I don't know how to replace Yi by µ^. The model itself...
  2. M

    What Test Should I Use?

    Hi there, I am analysing a large data set involving a food-training study, and I am struggling with the last question. Here are the key facts: The participants were 83 people aged 18–65 with a body mass index (BMI) of at least 18.5. All participants were weighed at baseline (Week 1, prior to...
  3. M

    Is it possible to run a multiple linear regression analysis when one of the categorical predictors has more than 2 groups?

    I want to run a multiple linear regression analysis with 5 categorical or continuous predictors (independent variables) for a continuous outcome (dependent variable). Is it possible to use a multiple linear regression analysis if one of the predictors has more than 2 groups?
  4. A

    Tobit Regression

    Can I use categorical independent variables in tobit regression? If yes, how to interpret the results?
  5. W

    R^2 vs. significance of the the variables

    Hello community :) I am currently working with paneldata to see if there is correlation between sustainability and performancce in the energy and materials sector of the S&P500. I ran the regression twice, one with the logarithm of MarketValue (=MarketValue.WINS.LOG) and one without...
  6. K

    Difference between simulating the dependent variable and simulating the error terms and adding them to the fitted values values assuming normality?

    What's the statistical difference between simulating the dependent variable and simulating the error terms and adding them to the fitted values values assuming normality (gaussian GLM)? Say I'm doing a simple multiple regression on the following data (R): n <- 40 x1 <- rnorm(n, mean=3, sd=1)...
  7. S

    Adjusted OR

    How to find adjusted OR as given in Table 6 in spss? The procedure followed to get it like i found binary logistic regression is used to find crude OR using single indep. variable.
  8. Y

    Multiple or simple linear regression?

    Hi all I'm fairly new to statistics and have a project where I investigate if the difference between two variables can predict the value of a third variable. And I am unsure if I should use simple or multiple regression. The participants in my sample has answered three surveys: A) Emotions...
  9. R

    Urgent Help for Statistic

    I have a study to establish the factors that influence consumer satisfaction with different internet banking services. Part 1 is the demographic and Part 2 are the Customer Satisfaction and Attributes of Satisfaction which were measured using the 5 Point Likert Scale questionnaires. The...
  10. R

    SPSS for Customer Satisfaction

    I have data collected with 5 Likert Scale Questionnaire to establish factors influencing customer satisfaction using online banking. The factors included Credibility, Efficiency, Ease of Use, Security, Problem Handling and Product/Service Portfolio. A total of 6 independent variables. In this...
  11. C

    Need help choosing the right analysis [unsolved]

    Hi! I am using three surveys, let's call them A, B, and C. From one survey to the next, there is a 5-year interval. From these surveys, I calculate the weight change and I then check if they get a certain diagnosis afterward within 10 years. I want to see if weight increase or decrease is...
  12. StrangeCharm

    Which approach to evaluate small spatial variations in temperature?

    I am evaluating whether utility-scale solar energy plants affect the surrounding climate (initially temperature). An effect has been found in one paper using the approach described below/attached but when repeating this approach I find no effect for the same site. I want to be sure that there...
  13. C

    Standardize one predictor variable or all predictor variables to solve multi-collinearity

    I was using a fixed-effects panel model with interaction effects when I realized that the VIF values are too high for some variables. I was advised to standardize the predictor variables to mitigate multi-collinearity. My question is that can I standardize just 1 predictor variable or must I...
  14. S

    Trying to understand and correlate the meaning of OR and p value in the univariate logistic regression

    Dear members, Just a newbie to statistics. Request your help with understanding this table below I have serious doubt in the results of statistics that I have done with regard to the odds ratio and p value that I obtained. My question is that how come that when the OR is low the p value is...
  15. L

    Forecast Model

    Hi, everyone, Based on the forecast turnover, I am supposed to predict various activities (such as the number of manually entered orders, number of phone calls etc. (17 activities in total) in a company.My first thought was to do a linear regression per activity to be predicted. Means to...
  16. S

    How to Improve my Hosmer-Lemeshow Goodness of Fit Significance Value

    Hi, I'm just wondering if anyone here can help me. I'm making a model for my logistic regression. For the variables, I'm using 1 Dichotomous Dependent Variable (Financial Distresss, Y) and 2 Continuous Independent Variable (Corporate Governance Score, X and VAIC Score, Z). I'm using logistic...
  17. F

    Two dummy variables are mutually exclusive

    Hello all, I have a problem for a university assignment that two dummy variables are mutually exclusive. The task is to estimate what can increase certain diseases in dogs using regression. In the sample there are several factors (Lives in city yes/no, age, male/female etc....). Then there is a...
  18. T

    How is conditional main effect interpreted when there is interaction?

    I have a question about multiple regression. May I kindly ask how should I interpret the conditional main effect when the interaction is 0 and the coefficients have opposite signs, more specifically if one of the interaction term contains zero such as angle or time. Please see an example...
  19. M

    Negative correlation, but positive effect in multilevel regression

    Hello, I am doing a longitudinal multilevel analyses. I am researching the impact of certain restricting abortion policies of US states on the number of abortions. Furthermore I control for the characteristics of these states. One of these characteristics namely poverty rate of a state, has a...
  20. U

    Which test to use? one predictor variable and 3 dependent variables

    Hello, I'm hoping someone can help me. I am conducting research for my thesis looking at relationship between social connectedness (IV), and diabetes distress, diabetes self-management and perceived competence (DVs). All are continuous variables measured on self-report likert scale...