Calculation Of Probability Research Articles

Hubungan Kompetensi Kepribadian Guru Dengan Perkembangan Moral Pada Anak TK B

The aims of this study were (1) to determine the relationship between teacher personality competence and the moral abilities of TK B in Desa Biru, (2) to obtain an overview of the relationship between teacher personality competence and the development of moral abilities in group B early childhood in Biru Village. This research was conducted in all kindergarten group B children in Biru Village, Poleang Timur District, Bombana Regency. The sources of data collection in this study were teachers and school principals. Data collection techniques in this study were questionnaires and observation. The results of hypothesis testing show that the value obtained from the probability calculation results is 0.034 which is smaller than the probability test value of 5% (sig = 0.05) or 0.034 <0.05. This means that there is a significant relationship between the teacher's personality competence and children's moral development in Poleang Timur District, Bombana Regency.

Assessment of Fishing Vessel Vulnerability to Pure Loss of Stability Using a Self-Developed Program

A significant proportion of maritime incidents occurring in coastal waters are attributed to small fishing vessels. To analyze the root causes of such maritime accidents, it is imperative to conduct simulations to assess the potential pure loss of stability. This paper proposes a method of using a self-developed program to assess the vulnerability, which could be used in practice for seafarers to maintain shipping safety. To assess the vulnerability of three fishing vessels, the proposed method considers different wave conditions, where the parametric behaviors of the waves relative to the ship’s position (ξG/λ) and wave steepness (H/λ) are explored. Additionally, vulnerability is computed systematically under diverse loading conditions, resulting in visual representations such as 2D and 3D plots. We conduct comparative simulations between the proposed method and the ICS-Hydro STAB. The simulation results reveal that the proposed method has superior accuracy in executing failure probability calculations. The prediction error is consistently below 5%.

Establishment and validation of prediction models for human leukocyte antigen haplotypes and human leukocyte antigen genotypes

Objective: To establish prediction models for human leukocyte antigen (HLA) haplotypes and HLA genotypes, and verify the prediction accuracy. Methods: The prediction models were established based on the characteristic of HLA haplotype inheritance and linkage disequilibrium (LD), as well as the invention patents and software copyrights obtained. The models include algorithm and reference databases such as HLA A-C-B-DRB1-DQB1 high-resolution haplotypes database, B-C and DRB1-DQB1 LD database, G group alleles table, and NMDP Code alleles table. The prediction algorithm involves data processing, comparison with reference data, filtering results, probability calculation and ranking, confidence degree estimation, and output of prediction results. The accuracy of the predictions was verified by comparing them with the correct results, and the relationship between prediction accuracy and the probability distribution and confidence degree of the predicted results was analyzed. Results: The HLA haplotypes and genotypes prediction models were established. The prediction algorithm included the prediction of A-C-B-DRB1-DQB1 haplotypes according to HLA-A, B, DRB1, C, DQB1 genotypes, the prediction of C and DQB1 high-resolution results according to A, B and DRB1 high-resolution results, and the prediction of A, B, DRB1, C and DQB1 high resolution results according to the A, B and DRB1 intermediate or low resolution results. Validation results of "Predicting A-C-B-DRB1-DQB1 haplotypes basing on HLA-A, B, DRB1, C, DQB1 genotypes" model: for 787 data, the accuracy was 94.0% (740/787) with 740 correct predictions, 34 incorrect predictions, and 13 instances with no predicted results. For 847 data, the accuracy was 100% (847/847). The 2 411 and 2 594 haplotype combinations predicted from 787 and 847 data were grouped according to confidence degree, the accuracy was 100% (48/48, 114/114) for a confidence degree of 1, 96.2% (303/315) and 97.8% (409/418) for a confidence degree of 2 respectively. Validation results of "Predicting A, B, DRB1 and C, DQB1 high-resolution genotypes basing on HLA-A, B, DRB1 high, intermediate, or low resolution genotypes" model: when predicting C and DQB1 high resolution genotypes basing on A, B, and DRB1 high resolution genotypes, 89.3% (1 459/1 634) of the predictions were correct. The accuracy for the top 2 predicted probability (GPP) ranking was 79.2% (1 156/1 459), and for the top 10, it was 95.0% (1 386/1 459). Furthermore, when GPP≥90% and GPP 50%-90%, the prediction accuracy was 81.3% (209/257) and 72.8% (447/614) respectively. The accuracy of predicting C and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 high resolution genotypes from the China Marrow Donor Program was 87.0% (20/23). The accuracy of predicting A, B, DRB1, C, and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 intermediate or low-resolution genotypes was 70.0% (7/10) and 52.5% (21/40) respectively. When predicting whether the patient is likely to have a HLA 10/10 matched donor, the accuracy of the top 2 GPP combinations with a proportion of ≥50% was 85.7% (6/7). Conclusions: When using A, B, DRB1, C, DQB1 genotypes to predict A-C-B-DRB1-DQB1 haplotype combinations, the results with a confidence degree of 1 and 2 are reliable. When predicting C and DQB1 genotypes according to A, B and DRB1 genotypes, the top 10 results ranked by GPP are reliable, and the top 2 results with GPP≥50% are more reliable.

Estimation of Mediastinal Toxicities after Radiotherapy for Hodgkin Lymphoma-A Normal Tissue Complication Analysis of the HD16/17 Trial by the German Hodgkin Study Group.

Hodgkin lymphoma is a hematologic malignancy with excellent outcomes even in advanced stages. Consequently, the importance of treatment-associated toxicity increases. However, the exact estimation of individualized rates is difficult due to different disease extents, treatment strategies and techniques. The following analysis aims at a pre-treatment estimation of relevant mediastinal toxicities. Normal tissue complication probability calculations were used to evaluate the toxicity rates for the heart, lungs and female breast of patients undergoing radiotherapy for early-stage Hodgkin lymphoma. Overall, 45 Patients of the HD16 and HD17 trials by the German Hodgkin study group were included and risks were calculated using the Lyman-Kutcher-Burman model. The median values for pericarditis, pneumonitis and fibrosis of the left or right breast were 0.0%, 0.0%, 0.7% and 0.6% in the HD16 cohort, and 0.0%, 0.1%, 1.1% and 1.0% in the HD17 cohort, respectively. Correspondingly, none of the included patients displayed any of the evaluated toxicities during clinical follow-up. The use of higher doses (30 Gy) in the HD17 cohort led to an increase in toxicity compared to the HD16 cohort (20 Gy). No significant influence of the planning target volume size or the radiation technique could be found in this study. Both the clinically observed and calculated toxicity rates corroborate the overall low-risk profile of radiotherapy for Hodgkin lymphoma. Further treatment individualization will be attempted in the future.

Testing the predictive ability of a karst subsidence susceptibility map made thirty years ago in the central Ebro Basin (Zaragoza, Spain)

Karst subsidence has caused damage in numerous buildings and infrastructures in the surroundings of Zaragoza city since the early 1970s, when rapid urbanization of the area begun. A detailed survey carried out in 1991 allowed recognizing and mapping damages in an industrial district, and elaborating a susceptibility or spatial hazard map based on (i) the observed presence of active alluvial dolines, and (ii) spatial distribution of factors controlling their development, according to a hazard model based on both spatial analysis and laboratory experimental simulation. The predictive ability of such spatial hazard map is now tested, thirty years later, by a new detailed field survey carried out in 2021 covering almost the same area. The results allow mapping of the total present-day damage and, using GIS tools, also a residual map of areas newly damaged since 1991. After comparing those maps with the hazard zonation proposed in 1991, we conclude that: (i) the predictive ability of the latter is good in overall; (ii) the areas of highest subsidence hazard have a probability in the range of 19–38% of suffering damage over a 30-year period, while this probability systematically decreases up to about 7% for the lowest hazard areas; (iii) the average rate of occurrence of new damage within the overall study zone has remained quite uniform around 0.5% in area per year. Incorporating such calculations of temporal probability of occurrence associated to each hazard category allows the original susceptibility map to approach a hazard s.s. map, providing annual probabilities of occurrence between ~ 0.6 to 1.3% and ~ 0.3% for the distinct hazard zones.

MODY Probability Calculator Is Suitable for MODY Screening in China: A Population-based Study.

Selecting appropriate individuals for genetic testing is essential due to the optimal treatment for maturity-onset diabetes of the young (MODY). However, how to effectively screen for MODY in China remains unclear. To validate the performance of current screening strategies in selecting patients with MODY based on a nationwide type 2 diabetes cohort. A panel of 14 MODY genes was analyzed from 1911 type 2 diabetes patients who were ages 15 to 35 years. Variants were evaluated according to the American College of Medical Genetics and Genomics guidelines. Based on this cohort, we simulated the 2 most frequently used screening strategies, including the traditional MODY criteria and the MODY probability calculator (MPC), to assess their ability to select patients with MODY. From a total of 1911 participants, 42 participants harbored pathogenic/likely pathogenic variants. The performance of the traditional criteria was sensitivity: 19.0%, specificity: 72.9%, positive predictive value (PPV): 1.6%, and missing rate: 81.0%. The optimal cut-off for MPC was 40.7%. Based on this cut-off value, the performance was sensitivity: 54.8%, specificity: 81.0%, PPV: 6.1%, and missing rate: 45.2%. Moreover, hemoglobin A1c, insulin treatment, and family history of diabetes have poor discrimination between MODY and young-onset type 2 diabetes. The MPC is better than traditional criteria in terms of both sensitivity and PPV. To ensure more MODY patients benefit from optimal treatment, we therefore suggest that routine genetic testing be performed on all type 2 diabetes patients who are between the ages of 15 and35 years and have MPC probability value over 40.7%.

Theoretical evaluation of the impact of diverse treatment conditions by calculation of the tumor control probability (TCP) of simulated cervical cancer Hyperthermia-Radiotherapy (HT-RT) treatments in-silico

Introduction Hyperthermia (HT) induces various cellular biological processes, such as repair impairment and direct HT cell killing. In this context, in-silico biophysical models that translate deviations in the treatment conditions into clinical outcome variations may be used to study the extent of such processes and their influence on combined hyperthermia plus radiotherapy (HT + RT) treatments under varying conditions. Methods An extended linear-quadratic model calibrated for SiHa and HeLa cell lines (cervical cancer) was used to theoretically study the impact of varying HT treatment conditions on radiosensitization and direct HT cell killing effect. Simulated patients were generated to compute the Tumor Control Probability (TCP) under different HT conditions (number of HT sessions, temperature and time interval), which were randomly selected within margins based on reported patient data. Results Under the studied conditions, model-based simulations suggested a treatment improvement with a total CEM43 thermal dose of approximately 10 min. Additionally, for a given thermal dose, TCP increased with the number of HT sessions. Furthermore, in the simulations, we showed that the TCP dependence on the temperature/time interval is more correlated with the mean value than with the minimum/maximum value and that comparing the treatment outcome with the mean temperature can be an excellent strategy for studying the time interval effect. Conclusion The use of thermoradiobiological models allows us to theoretically study the impact of varying thermal conditions on HT + RT treatment outcomes. This approach can be used to optimize HT treatments, design clinical trials, and interpret patient data.

Multiple Factors Coupling Probability Calculation Model of Transmission Line Ice-Shedding

After a transmission line is covered by ice in winter, ice-shedding and vibration occurs under special meteorological and external dynamic conditions, which leads to intense transmission line shaking. Transmission line ice-shedding and vibration often cause line flashover trips and outages. In January 2018, three 500 kV transmission lines, namely, the 500 kV Guanli line, the 500 kV Dushan line, and the 500 kV Guanqiao line, tripped and cut off due to ice-shedding and vibration in Anhui province, seriously threatening the safe operation of a large power grid. Current studies mainly focus on analyzing the influence factors and characteristics of line ice-shedding and investigating suppression measures, but they only analyze the correlation between each influencing factor and icing or shedding, and do not consider the coupling effects between multiple factors. In this paper, the key influencing factors and the probability distribution of transmission line ice-shedding were analyzed, and a multiple-factor coupling fault probability calculation model of line ice-shedding based on Copula function was proposed. The fault probability was calculated directly by considering multiple influence factors at the same time, which effectively overcame the error caused by multi-factor transformation in fuzzy membership degree and other methods. It provided an important decision-making basis for preventing and controlling transmission line ice-shedding faults.

Online tools to predict individualised survival for primary oesophageal cancer patients with and without pathological complete response after neoadjuvant therapy followed by oesophagectomy: development and external validation of two independent nomograms

ObjectiveThis study aimed to develop and validate robust predictive models for patients with oesophageal cancer who achieved a pathological complete response (pCR) and those who did not (non-pCR) after neoadjuvant...

Magnitude transition probability and casual dependence of local earthquakes

Even though they contribute far less to the total worldwide death toll and damage than their more powerful counterparts, Aswan earthquakes, which have low overall seismicity and magnitudes between 3.0 and 5.6, may be the main source of seismic risk and hazard in the Aswan region. With such earthquakes’ potential to cause significant concern in several parts of the world, it is important to study of the casual dependence between the occurrences of earthquake magnitudes in Aswan area. The probabilistic dependence is different from the casual dependence. Earthquakes’ probabilistic reliance is not a prerequisite nor an adequate means of establishing a causal connection. Therefore, it is crucial to look at the accidental dependency of earthquake magnitude in the Aswan region. For this purpose, and to verify the stability of the findings, we considered the seismic data from 1980 to 2002 in Aswan area. The statistical and probabilistic methods might be used to examine the features of the dependency between earthquake magnitudes. The probability of both the absolute and conditional earthquake magnitudes, as determined by counting the consecutive observations of earthquake magnitude directly. This article investigates empirically the magnitude transition probability and casual dependence of earthquake magnitude events in Aswan area. The data for 286 consecutive earthquakes felt in the Aswan area with a magnitude of 3.0 or above are utilized in the probability calculations to examine the dependency of earthquake magnitudes on chance. The results show that the magnitudes of subsequent earthquakes in the Aswan area are causally related to one another. Additionally, the results of this study imply that the order in which earthquakes occur in the Aswan region may be governed by some regular connection.

Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations

In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source.

Predictive modelling based on statistical modeling of logistic regression for heart disease

The heart is the core driving force for the continuation of human life, and the disease of this organ is bound to be fatal. There are two main types of heart disease. Congenital diseases are caused by developmental problems in unborn children. These problems, mainly in the heart, can damage various parts of the heart. Acquired sexually transmitted diseases are diseases caused by environmental factors and their own growth and development after birth. The purpose of the project model is to predict heart disease and analyse the main types of heart disease in the population. In the whole research process, the most important thing is the establishment of the model. The algorithm principle of this model is logistic regression. Logistic regression is used to make predictions and probability calculations on the data. Through such algorithms, modelling techniques can be used to predict the impact of pathogenic factors on the probability of heart disease. In addition, prevention of heart disease can be improved with accurate and convenient model predictions that can be tailored to the population that fits the predictions. This method can improve the technical level and treatment level of the hospital, and can also reduce the harm caused by heart disease.

An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis.

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

A novel grey relational clustering model under sequential three-way decision framework

In the context of addressing clustering problems with small samples, grey relational clustering (GRC) plays a crucial role. Currently, a three-way GRC has gained popularity, particularly for its ability to handle the uncertain relationship between objects and classes. However, explicit clustering for some objects may not be achievable when employing the one-step decision, which is often a coarse-grained approach. In such cases, re-clustering these objects with a finer granularity becomes necessary. This is where the sequential three-way decision (STWD) proves to be an effective solution. Therefore, this paper is dedicated to designing a novel GRC under the STWD framework, namely GRC-STWD. Specifically, recognizing that some existing threshold calculations in GRCs can be overly subjective and that a pair-wise global threshold may not efficiently leverage available information for accurate object clustering, we introduce a novel threshold algorithm to address these limitations. Furthermore, we propose a conditional probability calculation method combining TOPSIS and grey relational degrees for clustering data lacking decision attributes or class labels. As the development of GRC within portfolio environments remains relatively nascent, we apply the proposed model to portfolio strategy construction. Finally, comparative and experimental analyses validate the model's effectiveness and feasibility. Notably, the developed model serves as a generalization of GRC, contributing to the broader advancement of GRC and STWD methodologies.

Impact of Problem-Based Learning on the Perception, Understanding, and Application of Statistical Concepts in Business Administration and Management Students

We investigate the impact of the Problem-Based Learning (PBL) approach supplemented by student-created videos on the teaching of Probability Calculation and Inference Statistics in Business Administration and Management Studies. To that aim, we analyse survey results gathered from a sample of students from the Business Administration and Management and Law-Business Administration and Management degrees at the University of Zaragoza who participated in the PBL experience, focusing on their perceptions, skill development, dynamics of collaboration, and communication with teachers and classmates. Additionally, a comparative study of their final grades is conducted, comparing them with that obtained by students who did not engage in PBL. Our findings reveal that PBL significantly enhances students’ understanding and motivation, along with their communication with teachers, particularly among repeating students, and teamwork skills, particularly among beginners. Furthermore, students participating in PBL activities tended to achieve significantly higher grades than their nonparticipating counterparts did. Moreover, our study contributes to the discourse on sustainable education by highlighting the potential of PBL as a pedagogical approach aligned with the Sustainable Development Goals. By promoting active learning, collaborative problem solving, and communication skills, PBL fosters a culture of lifelong learning and empowers students to address real-world challenges in a sustainable manner. These findings have implications for educators, policymakers, and researchers seeking to promote sustainability in higher education through effective pedagogical practices.

TCRosetta: An Integrated Analysis and Annotation Platform for T-cell Receptor Sequences.

T cells and T-cell receptors (TCRs) are essential components of the adaptive immune system. Characterization of the TCR repertoire offers a promising and highly informative source for understanding the functions of T cells in the immune response and immunotherapy. Although TCR repertoire studies have attracted much attention, there are few online servers available for TCR repertoire analysis, especially for TCR sequence annotation or advanced analyses. Therefore, we developed TCRosetta, a comprehensive online server that integrates analytical methods for TCR repertoire analysis and visualization. TCRosetta combines general feature analysis, large-scale sequence clustering, network construction, peptide-TCR binding prediction, generation probability calculation, and k-mer motif analysis for TCR sequences, making TCR data analysis as simple as possible. The TCRosetta server accepts multiple input data formats and can analyze ∼ 20,000 TCR sequences in less than 3 min. TCRosetta is the most comprehensive web server available for TCR repertoire analysis and is freely available at https://guolab.wchscu.cn/TCRosetta/.

Contingency versus inevitability: a review and reinterpretation of Stephen Jay Gould’s book “Wonderful Life—The Burgess Shale and the Nature of History”

In 1989, Stephen Jay Gould published his Wonderful Life: “High in the Canadian Rockies is a small limestone quarry formed 530 million years ago called the Burgess Shale. It holds the remains of an ancient sea where dozens of strange creatures lived a forgotten corner of evolution preserved in awesome detail. In this book Stephen Jay Gould explores what the Burgess Shale tells us about evolution and the nature of history”. Gould, based upon a reinterpretation of the famous fossils from the Burgess Shale, proposed a conclusion that was revolutionary at the time, because it was centred on the notion of contingency in the course of biological evolution. The very last paragraph displayed in particular the following sentences: “This response does not cite a single law of nature; it embodies no statement about predictable evolutionary pathways, no calculation of probabilities based on general rules of anatomy or ecology. I do not think that any ‘higher’ answer can be given, and I cannot imagine that any resolution could be more fascinating.” In this essay, I intend to criticise Gould’s conclusions and to provide a new reinterpretation of the history of life, based not on contingency but on the inevitability of evolution.

A Method to Detect the Characteristics of Intermittent Generalized Synchronization Based on Calculation of Probability of the Synchronous Regime Observation

A Method to Detect the Characteristics of Intermittent Generalized Synchronization Based on Calculation of Probability of the Synchronous Regime Observation

Effect of emerging digital technologies and methodologies combined with incentives on HbA1c in patients with type 2 diabetes mellitus: study protocol for a parallel, open randomized controlled trial

BackgroundType 2 diabetes mellitus (T2DM) is a common metabolic disease that requires long-term management and treatment. Digital intervention, as an emerging medical model, has been widely used in the treatment of T2DM patients. Behavioral economics theory provides a favorable perspective for studying the effect of digital intervention because it can reveal the decision-making mechanisms behind human behavior and provide more effective interventions for digital intervention. The purpose of this trial is to evaluate the impact of behaviorally based digital intervention on T2DM patients’ HbA1c, self-monitoring of blood glucose (SMBG) testing rate, diabetes self-efficacy, and other indicators compared to conventional treatment.MethodsThis trial is a prospective randomized controlled trial conducted at the First People’s Hospital of Kunshan City from April 1, 2023, to December 31, 2024, with a follow-up period of 3 months. The specific randomization method was established and implemented through the EDC clinical trial center’s randomization system. We will measure and collect baseline data from three groups, including Group A: digital intervention + virtual incentives + conventional treatment, Group B: digital intervention + physical incentives + conventional treatment, and Group C: conventional treatment. HbA1c, weight, SMBG testing rate, diabetes self-efficacy, and diabetes-related medical expenses will be recorded at baseline, 1 month, 2 months, and 3 months for all three groups. The Shapiro-Wilk test will be used to test for normality, and Pearson correlation analysis will be used for correlation analysis. Dropouts will be analyzed separately. Analysis of variance or exact probability calculation will be used to compare demographic data and other baseline indicators.DiscussionThis study is a novel clinical trial that integrates multiple disciplines (economics and medicine) and uses digital technology to deliver the intervention. Most published studies were offline interventions based on behavioral economics theory, but very few were on online interventions for T2DM patients. This study has both novelty and social value.Trial registrationChinese Clinical Trial Registry ChiCTR2300070753. Registered on 2023/04/22.

Covariational reasoning in Bayesian situations

AbstractPrevious studies on Bayesian situations, in which probabilistic information is used to update the probability of a hypothesis, have often focused on the calculation of a posterior probability. We argue that for an in-depth understanding of Bayesian situations, it is (apart from mere calculation) also necessary to be able to evaluate the effect of changes of parameters in the Bayesian situation and the consequences, e.g., for the posterior probability. Thus, by understanding Bayes’ formula as a function, the concept of covariation is introduced as an extension of conventional Bayesian reasoning, and covariational reasoning in Bayesian situations is studied. Prospective teachers (N=173) for primary (N=112) and secondary (N=61) school from two German universities participated in the study and reasoned about covariation in Bayesian situations. In a mixed-methods approach, firstly, the elaborateness of prospective teachers’ covariational reasoning is assessed by analysing the arguments qualitatively, using an adaption of the Structure of Observed Learning Outcome (SOLO) taxonomy. Secondly, the influence of possibly supportive variables on covariational reasoning is analysed quantitatively by checking whether (i) the changed parameter in the Bayesian situation (false-positive rate, true-positive rate or base rate), (ii) the visualisation depicting the Bayesian situation (double-tree vs. unit square) or (iii) the calculation (correct or incorrect) influences the SOLO level. The results show that among these three variables, only the changed parameter seems to influence the covariational reasoning. Implications are discussed.