PRM Logistic Regression

In linear regression (LR), it is crucial to pay attention to the dummy values being assigned as 0 or 1. This is because these values represent different categories or groups in the regression analysis. By correctly identifying which dummy value corresponds to 0 and which corresponds to 1, we can ensure accurate interpretation of the regression coefficients and make meaningful comparisons between different groups.

The given statement is true. Efficiency of predictors is indeed an important consideration when using MR (Multiple Regression). The efficiency of predictors is measured by their ability to accurately predict the outcome variable. In MR, the efficiency of predictors is determined by their coefficients, which are placed in the numerator of the equation. A higher coefficient indicates a stronger predictor, thus contributing to the overall efficiency of the model. Therefore, considering the efficiency of predictors is crucial in order to build an effective and accurate MR model.

The correct answer is LR, which stands for Logistic Regression. Logistic Regression is a statistical method used to predict a dichotomous outcome (such as yes/no, success/failure) based on one or more independent variables that can be either continuous or dichotomous. It is commonly used in various fields, including medicine, social sciences, and marketing, to analyze and predict binary outcomes.

The number of predictors does matter in terms of bringing up the R squared and bringing down the F value in multiple regression. This is because adding more predictors increases the amount of variance in the dependent variable that can be explained by the independent variables, leading to a higher R squared value. Additionally, having more predictors can decrease the F value, which measures the overall significance of the regression model, indicating a better fit of the model to the data.

The Nagelkerke R squared is a recommended effect size estimate to use in logistic regression (LR) models. Logistic regression is a statistical modeling technique used to predict the probability of a binary outcome variable based on one or more predictor variables. The Nagelkerke R squared is a measure of the proportion of variance in the outcome variable that is explained by the predictor variables in the logistic regression model. Therefore, it is appropriate to use the Nagelkerke R squared as an effect size estimate in LR models.

In linear regression (LR), the dependent variable (DV) is considered a categorical variable, even though it represents a continuous probabilistic occurrence of an event occurring. This means that the DV can take on different categories or levels, but within each category, it can have a range of continuous values.

Multiple regression, MANOVA, and ANOVA use the coefficient of determination (r squared) to represent the reduction in uncertainty or the degree of variance that can be explained. They also use the sum of squares (ss) to measure the variability in the data. On the other hand, LR (Logistic Regression) uses the -2LL (negative log-likelihood) as a measure of how well the model fits the data.

The statement is true. The usefulness of a single predictor is indeed gauged by its semi-partial correlation (SR). The semi-partial correlation measures the unique contribution of a predictor variable to the dependent variable, while controlling for the effects of other predictor variables. Therefore, a higher semi-partial correlation indicates a stronger relationship between the predictor and the dependent variable, making it a more useful predictor.

In multiple regression, we first check out the descriptive statistics (1) to understand the basic characteristics of the variables involved. Then, we move on to the inferential statistics of the predictors (2) to determine the significance and strength of the relationships between the predictors and the outcome variable. Finally, we examine the inferential statistics of the equation (3) to assess the overall fit and significance of the regression model. Therefore, the correct order is 1,2,3.

In research, the significance of a finding is indeed very dependent on sample size. A larger sample size increases the statistical power and reduces the likelihood of obtaining a false positive or false negative result. On the other hand, effect size refers to the magnitude of the observed relationship or difference between variables. While effect size can provide valuable information about the practical significance of a finding, it is not necessarily related to the reliability of the result. Effect size can vary depending on the specific context and population studied, making it important to consider both sample size and effect size when interpreting research findings.

The correct answer is "experimental, looking for an effect, trying to prove relationships." This answer accurately describes the characteristics of an ANOVA design. ANOVA is an experimental design that involves manipulating independent variables to determine their effects on dependent variables. It is specifically designed to test for significant differences between groups and establish causal relationships. Correlational designs, on the other hand, do not involve experimental manipulation and are primarily focused on examining the relationship between variables without trying to prove causality.

If the EB (equilibrium balance) is below 1, it means that the skier's weight is not evenly distributed between the two skis. In this case, it is less likely that the skier will fall because having a lower EB indicates better balance and stability.

Multiple regression (MR) is the correct answer because it involves the use of multiple variables to predict one continuous scale variable. In MR, the relationship between the dependent variable (DV) and multiple independent variables (IVs) is analyzed to determine how the IVs collectively contribute to predicting the DV. This statistical technique is commonly used in research and allows for the examination of the unique contribution of each IV while controlling for the effects of other variables.

The correct answer is 2,3,4. This means that LR (likely referring to linear regression) does not require assumptions 2, 3, and 4, which are linearity, normality, and homogeneity of variance. Linear regression assumes independence, linearity, normality, homogeneity of variance, and non-multicollinearity. However, this answer suggests that LR is flexible and does not strictly require all five assumptions.

The predictors (independent variables) in multiple regression can be continuous or dichotomous. Continuous predictors are variables that can take on any value within a certain range, such as age or income. Dichotomous predictors are variables that have only two possible values, such as gender or yes/no responses. Therefore, options a and b are correct because they represent the two types of predictors commonly used in multiple regression analysis.

The use of EB (evidence-based) is particularly pertinent in health research, whereby you are 4.3 times as likely or half as likely to do something. In health research, it is crucial to base decisions and interventions on solid evidence and scientific research. Using evidence-based approaches ensures that the information and interventions provided are reliable, effective, and supported by empirical evidence. This is especially important in the healthcare field where the well-being and safety of individuals are at stake.

An EB of .9 on 'information' indicates a strong negative correlation between being male and having a high information score. This means that males are less likely to have a high information score compared to females.

In logistic regression, the exponent of B (EB) represents the odds ratio. If the EB is 1, it means that the predictor variable is not a significant predictor of the outcome. In other words, the skier falling or not falling is equally likely regardless of the value of the predictor variable.

The correct answer is "of 1". In logistic regression, the estimated beta coefficients (EB) represent the change in the log-odds of the outcome variable for a one-unit increase in the predictor variable. In this case, the predictor variable is gender, and the dummy variables 0 and 1 are used to represent female and male, respectively. Since the question states that the EB is understood as a poor predictor, it implies that the EB is not significantly different from 0, meaning that the log-odds of the outcome variable does not change as gender changes. Therefore, the EB is "of 1", indicating no effect of gender on the outcome variable.

The correct answer explains that the observed data is the result of what can be modeled plus error. This means that the observed data includes the effects of the variables that can be modeled, as well as any random error or variability in the data.

In logistic regression (LR), the dependent variable (dv) has to be categorical, meaning it has distinct categories or groups. However, the underlying distribution of the dv can be continuous, meaning it can take on any value within a range.

Standard, statistical, and hierarchical regression are all variable selection methods used in LR. These methods help to determine which independent variables should be included in the regression model based on their statistical significance and contribution to the prediction of the dv.

Nagelkerke's statistic is a measure of effect size that can achieve a value of 1. It represents the proportion of variation in the response variable explained by the specific model, as well as the proportion of variation in the explanatory variable explained by the specific model. This statistic is commonly used in logistic regression to assess the strength of the relationship between variables.

Logistic Regression (LR) is very similar to DFA (Discriminant Function Analysis) except the predictors do not need to be normally distributed.

Standard MR, statistical regression, and hierarchical regression all use the same number of independent variables (IVs). However, they differ in terms of the predictors used. Standard MR uses all available predictors, statistical regression uses the minimum necessary predictors, and hierarchical regression uses the best subset of predictors. Therefore, the correct answer is "all, best, subset."

The unique contributions of particular predictors (X1 alone and X2 alone) are measured using semi-partial correlation. This means that the effect of each predictor is assessed while controlling for the other predictors in the model. However, joint contributions are not assigned to any particular predictor, as they represent the combined effect of all predictors in the model.