Used Car Price Estimation: Moving from Linear Regression towards a New S-Curve Model

A simple linear regression is commonly used as a practical predictive model on a used car price. It is a useful model which carry smaller prediction errors around its central mean. Practically, real data will hardly produce a linear relationship. A non-linear model has been observed to better forecast any price appreciation and manage prediction errors in real-life phenomena. In this paper, an S-curve model shall be proposed as an alternative non-linear model in estimating the price of used cars. A dynamic S-shaped Membership Function (SMF) is used as a basis to build an S-curve pricing model in this research study. Real used car price data has been collected from a popular website. Comparisons against linear regression and cubic regression are made. An S-curve model has produced smaller error than linear regression while its residual is closer to a cubic regression. Overall, an S-curve model is anticipated to provide a better and more practical estimate on used car prices in Malaysia.

Structural Equation Modeling using Stata

In this tutorial, you will learn how to fit structural equation models (SEM) using Stata software. SEMs can be fit in Stata using the sem command for standard linear SEMs, the gsem command for generalized linear SEMs, or by drawing their path diagrams in the SEM Builder. After a brief introduction to Stata, the sem command will be demonstrated through a confirmatory factor analysis model, mediation model, group analysis, and a growth curve model, and the gsem command will be demonstrated through a random-slope model and a logistic ordinal regression. Materials and datasets are provided online, allowing anyone with Stata to follow along.

Depressive Cognitions Impact On A Latent Growth Curve Model Of Depressive Symptoms In Caregiving Grandmothers

Grandmothers caring for grandchildren have elevated levels of depressive symptoms compared to grandmothers who do not provide care. While the CES-D measures the somatic, positive and negative affect, and interpersonal strain symptoms experienced with depression, the Depressive CognitionScale © captures the change in cognitive thinking that often precedes depression. Depressive symptoms, on the other hand, are state like in nature and describe depressive symptoms that have happened recently. While depressive cognitions, according to Beck’s theory of depression, are the first negative thought processes to appear, these typically lead to other, more serious symptoms of depression. Specifically, depressive cognitions reflect negative thinking patterns and not depression. Data were collected on 343 participants in a longitudinal nationwide online research study of caregiving grandmothers. A latent growth curve model was used to track the trajectory of depressive symptoms at four time points (baseline, 2 weeks, 12 weeks, and 24 weeks). As depressive cognitions are the precursor to the development of depressive symptoms, a latent growth curve model was tested to gain an understanding of how depressive cognitions impacts the trajectory of depressive symptoms over time. The model fit the data well (Chi Square=21.025; df=9; p=.013; TLI=.976; CFI=.985; RMSEA=.063). Baseline depressive cognitions had a strong impact on the intercept (Standardized Beta=.76, p<.001) and the slope of depressive symptoms (Standardized Beta=-.67, p<.001). The continued impact of depressive cognitions over 24 weeks indicates the need for potential interventions to further address depressive cognitions as a way to decrease depressive symptoms in grandmother caregivers.