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1 statistics teacher in La Sebala Du Mornag

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1 statistics teacher in La Sebala Du Mornag

Juan - Guayaquil, Ecuador£18
£18Statistical Analysis for the Digital Age: Exploring Descriptive and Inferential Stats with Microsoft Excel
Trusted teacher: Class Description: In today's digital age, statistical analysis plays a crucial role in making informed decisions for businesses and organizations. This comprehensive statistics class, "Statistical Analysis for the Digital Age: Exploring Descriptive and Inferential Stats with Microsoft Excel," is designed to provide you with the knowledge and skills needed to navigate the world of data using Microsoft Excel. From the basics of descriptive statistics to the intricacies of inferential statistics, this course will take you on a journey through the fundamental concepts and techniques used in statistical analysis. You will learn how to collect, organize, and interpret data using the powerful capabilities of Microsoft Excel, including its worksheets, Data Analysis Tool, and the PhStat2 add-in. To enhance your learning experience, this course will focus exclusively on utilizing Microsoft Excel. Through practical exercises and real-world examples, you will develop proficiency in Microsoft Excel's built-in features and functionalities for statistical analysis. You will learn how to effectively use Excel's worksheets, leverage the Data Analysis Tool, and utilize the PhStat2 add-in to perform various statistical analyses. By the end of this course, you will have a solid foundation in statistical analysis using Microsoft Excel. You will be equipped with the skills to confidently navigate data, perform meaningful analyses, and make data-driven decisions that drive success in today's digital landscape. Key Topics Covered: Chapter 1: Introduction to Statistics • Definition of statistics • Role of statistics in data analysis and decision-making • Differentiating descriptive and inferential statistics Chapter 2: Types of Statistics • Descriptive statistics: Summarizing and describing data • Inferential statistics: Making inferences and drawing conclusions about populations based on sample data Chapter 3: Types of Variables • Categorical variables: Nominal and ordinal scales • Continuous variables: Interval and ratio scales Chapter 4: Descriptive Statistics: Measures of Central Tendency • Mean, median, and mode • Choosing appropriate measures based on data characteristics Chapter 5: Descriptive Statistics: Measures of Variation • Range, variance, and standard deviation • Interpreting variation in data Chapter 6: Descriptive Statistics: Measures of Shape • Skewness and kurtosis • Understanding the distributional characteristics of data Chapter 7: Data Visualization: Choosing the Right Chart • Histograms: Displaying the distribution of continuous data • Pie charts: Representing proportions or percentages • Column and Bar charts: Comparing categories or groups • Line charts: Visualizing trends or time-series data • Guidelines for selecting appropriate charts based on data types and analysis objectives Chapter 8: Probability and Counting • Sample Space • Events • Counting Sample Points • Probability of an Event • Additive Rules • Conditional Probability • Independence and the Product Rule • Bayes’ Rule Chapter 9: Random Variables and Probability Distributions • Concept of a Random Variable • Discrete Probability Distributions • Continuous Probability Distributions • Joint Probability Distributions Chapter 10: Mathematical Expectation • Mean of a Random Variable • Variance and Covariance of Random Variables • Means and Variances of Linear Combinations of Random Variables Chapter 11: Some Discrete Probability Distributions • Introduction and Motivation • Binomial and Multinomial Distributions • Hypergeometric Distribution • Negative Binomial and Geometric Distributions • Poisson Distribution and the Poisson Process Chapter 12: Some Continuous Probability Distributions • Continuous Uniform Distribution • Normal Distribution • Areas under the Normal Curve • Applications of the Normal Distribution • Normal Approximation to the Binomial • Gamma and Exponential Distributions • Chi-Squared Distribution Chapter 13: Fundamental Sampling Distributions and Data Descriptions • Random Sampling • Some Important Statistics • Sampling Distributions • Sampling Distribution of Means and the Central Limit Theorem • Sampling Distribution of S2 • t-Distribution • F-Distribution • Quantile and Probability Plots Chapter 14: One- and Two-Sample Estimation Problems • Statistical Inference • Classical Methods of Estimation • Single Sample: Estimating the Mean • Standard Error of a Point Estimate • Prediction Intervals • Tolerance Limits • Two Samples: Estimating the Difference between Two Means • Paired Observations • Single Sample: Estimating a Proportion • Two Samples: Estimating the Difference between Two Proportions • Single Sample: Estimating the Variance • Two Samples: Estimating the Ratio of Two Variances • Maximum Likelihood Estimation Chapter 15: One- and Two-Sample Tests of Hypotheses • Statistical Hypotheses: General Concepts • Testing a Statistical Hypothesis • The Use of P-Values for Decision Making in Testing Hypotheses • Single Sample: Tests Concerning a Single Mean • Two Samples: Tests on Two Means • Choice of Sample Size for Testing Means • Graphical Methods for Comparing Means • One Sample: Test on a Single Proportion • Two Samples: Tests on Two Proportions • One- and Two-Sample Tests Concerning Variances • Goodness-of-Fit Test • Test for Independence (Categorical Data) Chapter 16: Analysis of Variance (ANOVA) • Comparing means across multiple groups • One-way and two-way ANOVA Chapter 17: Chi-Square Test • Testing relationships between categorical variables • Assessing independence and goodness-of-fit Chapter 18: Simple Linear Regression and Correlation • Introduction to Linear Regression • The Simple Linear Regression Model • Least Squares and the Fitted Model • Properties of the Least Squares Estimators • Inferences Concerning the Regression Coefficients • Prediction • Choice of a Regression Model • Analysis-of-Variance Approach • Test for Linearity of Regression: Data with Repeated Observations • Data Plots and Transformations • Correlation Chapter 19: Multiple Linear Regression and Certain Nonlinear Regression Models • Estimating the Coefficients • Linear Regression Model Using Matrices • Properties of the Least Squares Estimators • Inferences in Multiple Linear Regression • Choice of a Fitted Model through Hypothesis Testing Throughout the course, you will engage in practical exercises, real-world examples, and data analysis tasks to reinforce your understanding of statistical concepts and techniques. You will also have the opportunity to apply these skills using statistical software tools to gain hands-on experience with data analysis. By the end of this course, you will have a solid grasp of both descriptive and inferential statistics, enabling you to confidently explore, analyze, and interpret data in various contexts. Whether you are a student, professional, or an individual seeking to enhance your data analysis skills, this course will empower you to make informed decisions based on statistical insights. Join us on this statistical journey and unlock the foundations of statistical analysis. Enroll now in the "Statistical Foundations: Exploring Descriptive and Inferential Analysis" course to develop your statistical proficiency and leverage the power of data-driven decision-making, including the use of charts for effective data visualization and interpretation.
Math · Statistics · Algebra
William - Accra, Ghana£22
£22Research Methodology Toolkit for Biologists: The Golden Solution to Every Novice Scientist's Dilemma
Trusted teacher: Are you eager to master the foundational principles of research methodology and unlock the tools for solving complex research challenges? This dynamic and practical course is your gateway to becoming a confident and skilled researcher. Packed with engaging lessons, real-world applications, and hands-on activities, you will acquire essential skills to design, execute, and publish impactful research. Whether you are a beginner or looking to enhance your expertise, this course will empower you to confidently tackle research projects and turn your findings into publications that make a difference. Join me and take your research capabilities to the next level! SYLLABUS Module 1: Foundations of Biological Research 🔵 Lesson 1.1: Understanding the Research Process in Biology ◘ Definition and scope of biological research ◘ Types of biological research (basic, applied, translational) 🔵 Lesson 1.2: Identifying Research Questions in Biology ◘ Characteristics of impactful biological research questions ◘ Refining questions for molecular biology, ecology, genomics, etc. 🔵 Lesson 1.3: Conducting a Literature Review in Biology ◘ Identifying relevant biological journals and databases (e.g., PubMed, Web of Science) ◘ Critical analysis of biological papers Module 2: Designing Your Biological Research 🔵 Lesson 2.1: Research Design for Biologists ◘ Experimental vs. observational studies in biology ◘ Designing robust controls and replicates 🔵 Lesson 2.2: Hypothesis Formulation in Biology ◘ Writing testable biological hypotheses ◘ Defining null and alternative hypotheses 🔵 Lesson 2.3: Sampling in Biological Studies ◘ Strategies for collecting biological samples (field and lab-based) ◘ Addressing sample size in population studies and molecular analyses Module 3: Biological Data Collection Techniques 🔵 Lesson 3.1: Experimental Techniques in Biology ◘ Common lab methods (e.g., PCR, Western blotting, microscopy) ◘ Good lab practices (GLP) for reproducibility 🔵 Lesson 3.2: Fieldwork for Biologists ◘ Designing ecological surveys and biodiversity studies ◘ Tools for field sampling (e.g., GPS, quadrats, transects) 🔵 Lesson 3.3: Handling Biological Specimens ◘ Sample preservation techniques for DNA, RNA, and proteins ◘ Best practices for labeling and storage Module 4: Biological Data Analysis and Interpretation 🔵 Lesson 4.1: Introduction to Statistical Analysis for Biologists ◘ Biostatistics fundamentals (e.g., t-tests, ANOVA, regression) ◘ Using R, Python, or SPSS for biological data 🔵 Lesson 4.2: Analyzing Genomic and Proteomic Data ◘ Tools like BLAST, MEGA, and Galaxy for sequence analysis ◘ Basics of bioinformatics workflows 🔵 Lesson 4.3: Interpreting Biological Results ◘ Connecting results to biological hypotheses ◘ Identifying and discussing limitations in biological research Module 5: Writing and Publishing in Biological Sciences 🔵 Lesson 5.1: Structuring a Biological Research Paper ◘ IMRAD format tailored for biological journals ◘ Writing clear and concise methods and results 🔵 Lesson 5.2: Referencing for Biologists ◘ Citation styles in biological sciences (e.g., Vancouver, APA) ◘ Using referencing tools specific to biology (e.g., EndNote, Zotero) 🔵 Lesson 5.3: Publishing in Biological Journals ◘ Identifying target journals (e.g., Nature, Cell, Microbial Genomics) ◘ Addressing reviewer comments Module 6: Ethics and Best Practices in Biological Research 🔵 Lesson 6.1: Ethical Considerations in Biology ◘ Handling live organisms and human samples ◘ Regulatory approvals (e.g., IACUC, IRB) 🔵 Lesson 6.2: Managing Biological Data ◘ FAIR principles (Findable, Accessible, Interoperable, Reusable) for biological data ◘ Data repositories for biology (e.g., NCBI, Dryad) 🔵 Lesson 6.3: Collaboration in Biology ◘ Building interdisciplinary teams (ecologists, geneticists, bioinformaticians) ◘ Leveraging platforms like ResearchGate for biologists Module 7: Practical Toolkit and Case Studies in Biology 🔵 Lesson 7.1: Tools for Efficient Biological Research ◘ Lab-specific tools (e.g., electronic lab notebooks, ELNs like LabArchives) ◘ Visualization tools (e.g., GraphPad Prism, BioRender) 🔵 Lesson 7.2: Case Studies in Biological Research ◘ Genomic studies on antimicrobial resistance pathogens ◘ Population studies in biodiversity hotspots ◘ Analyzing molecular mechanisms in model organisms
Writing · Statistics · Biology
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(1 review)
Dr. Mohamed - Dubai, United Arab Emirates£21
£21Lessons in applied statistics, mathematics, operations research, and economics for all levels
Trusted teacher: I am honored to be a university professor specialized in teaching statistics, mathematics, operations research, and economics. Thanks to my extensive experience, which extended for 15 years, I became a professional in monitoring students’ level and contributing to their academic success and achieving their cognitive goals. I truly find true happiness in my work as a university professor by guiding and helping students in achieving their academic realization and raising their level of scientific awareness. Therefore, I have a strong belief in the importance of this noble mission in the field of education. Typically, sessions go like this: 1️⃣ The first sessions focus mainly on assessing the student’s level to determine the areas that need development. 2️⃣ Next, we prepare a plan to fill the gaps, including determining the number of hours needed, the materials to focus on, and providing a set of training and improvement exercises. 3️⃣ We are keen to constantly review the student’s curriculum to stay informed of the latest developments and ensure continuous coordination. 4️⃣ Next, we provide exams similar to those that could be asked in class. 5️⃣ Finally, we prepare a monthly report upon request to notify parents of the student’s progress and improvement points. I am honored to be a specialized university professor with 15 years of experience in teaching statistics, mathematics, and operations research. Thanks to my extensive experience, I have become a professional in monitoring students' progress and contributing to their academic success. Indeed, I find true happiness in my work as a university professor through guiding and assisting students in achieving their academic awareness and raising their level of scientific knowledge. Therefore, I have a strong belief in the importance of this noble mission in the field of education. Usually, sessions follow the following structure: 1️⃣ The initial sessions primarily focus on assessing the student’s level to identify the areas that need improvement. 2️⃣ Following that, we create a plan to address the identified areas, including determining the required number of hours, the specific subjects that need emphasis, and providing a range of practice and enhancement exercises. 3️⃣ We ensure ongoing alignment with the student’s curriculum to stay updated on the latest developments and maintain continuous coordination. 4️⃣ Subsequently, we administer similar exams to those that may be given in the classroom. 5️⃣ Finally, we prepare a monthly report upon request to inform parents about the student’s progress and areas of improvement.
Math · Statistics · Economics for students
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Alonso - Chile£24
£24Advice, jobs or online classes in statistics, inference, econometrics, etc.
Trusted teacher: Statistical engineer with more than 7 years of experience in teaching, advisory and consulting offers assistance in all kinds of issues related to statistics or data modeling. The assistance mode is 100% online and the software to be used can be R (expert level), powerBI, gretl, sql, stata, spss and excel. Professionally, working for a multinational consulting firm, I have developed statistical models for several of the main financial institutions in Latin America, corresponding to the banking, retail, factoring, leasing, and insurance sectors, among others. On the other hand, as an independent consultant I have developed projects for all types of areas, such as the pharmaceutical industry, mining, medicine, architecture, forestry, etc. In addition, I have worked for two universities teaching mathematics and statistics subjects. Finally, it is important to highlight that quality, responsibility and respect are guaranteed in each class or required work. Accompanied by an effective teaching method, based on theoretical and practical knowledge, including real examples of applicability in the workplace. Regarding the value of the service, this will be agreed upon jointly and will depend on the advice or class to be carried out. Best regards Other sources: Statistics, Probability, Inference, Econometrics, Linear models, Nonparametric methods, Sampling, Data analysis, Time series, Experimental design, Multivariate analysis, Data mining, Quantitative methods, Biostatistics, Geostatistics, Credit risk, Risk Operational, Business Intelligence, Machine Learning, R Programming, Rstudio, R project.
Statistics
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(5 reviews)
Yinhao - Rotterdam, Netherlands£51
£51Mathematics, Statistics, and Econometrics up to university level | 3 diplomas
Trusted teacher: Do you need help in mathematics, statistics, probability theory, or econometrics? I'm a qualified teacher specializing in math, science, econometrics, and programming. I hold 3 university diplomas. Bachelor of Science in Finance (from a top 50 university in the world) Bachelor of Science in Econometrics and Operations Research (from the University of Groningen) Master of Science in Quantitative Finance (from a top 10 university in the world, ETH Zürich (Swiss Federal Institute of Technology in Zurich), on par with the University of Cambridge or University of Oxford) I offer help with your homework, and exam preparation in math, econometrics, and finance. Not only do I explain to you how to solve the problems in simple words, but also tell you the general way to solve problems of a kind. Therefore you'll be able to solve similar problems independently next time. Audience: Students in primary school, high school, and university (bachelor/master's level), all ages are welcome. Schedule: Flexible, upon agreement. Example: (1) Regular courses: courses on a regular basis as an auxiliary tool to catch up with the lectures in the school, or university; (2) Intensive courses: intensive courses with a long time aiming to prepare for the exam. Examples of the scope of the courses I offer (not exhaustive): Mathematics: Algebra, Arithmetic, Calculus, Probability Theory, Geometry, Trigonometry, Series and Sequences, Linear Programming, Statistics, Stochastic Processes, Stochastic Calculus, Derivative, Ordinary Differential Equations (ODEs), Partial Differential Equations (PDEs), Covariance and Correlation Matrix, etc. Econometrics: Time Series Analysis, Econometrics, Statistical Inference, etc. (any course in your Econometrics degree study) Please let me know if I can help :D
Math · Economics for students · Statistics
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(1 review)
Amine - Paris, France£37
£37Expert and experienced Professor: SPSS, R, jamovi, jasp, STATA, hypothesis testing, ANOVA, multivariate analysis, linear/log regression
Trusted teacher: During this course you will learn: ✓ SPSS ✓ R Studio ✓jamovi/jasp/Stata/xlstat ✓ Analyze data for univariate, bivariate and multivariate statistics with SPSS ✓ Simple and multiple linear regression and Logistic regression ✓ Analyze exploratory data, basic statistics and visual displays (Frequencies, exploration function, outliers) ✓ Inferential tests on correlations, counts and means (Calculation of z-Scores in SPSS, Correlation coefficients, A measure of reliability: Cohen's Kappa, Binomial tests, Goodness of fit test, Chi-square , One-sample t-test for a mean, Two-sample t-test for means) ✓ Analysis of variance (fixed and random effects, Running ANOVA in SPSS, The F-test for ANOVA, Effect size, Contrasts and post-hoc tests, Alternative post-hoc tests and comparisons, ANOVA with random effects , factorial ANOVA with fixed effects and interactions, Simple main effects, Analysis of covariance (ANCOVA), Power for analysis of variance) and Repeated Measures ANOVA (One-way repeated measures, Repeated measures in both directions: one between and one in the postman) ✓ Principal Component Analysis (PCA Example, Pearson 1901 Data, Component Scores, Principal Component Visualization, Correlation Matrix PCA) and Exploratory Factor Analysis (The Common Factor Analysis Model, The Problem of Factor Analysis exploratory, Factor analysis of CPA data, Scree Plot, Rotation of the factor solution, Cluster analysis, How to validate clusters) ___________________________________ ✓ My courses are based on exercises with the essentials of the course to remember. ✓ Working method for better understanding. ✓ Working on concrete data which allows the work to be visualized and assimilated more quickly.
Statistics · Math · Economics for students
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Our students from La Sebala Du Mornag evaluate their Statistics teacher.

To ensure the quality of our Statistics teachers, we ask our students from La Sebala Du Mornag to review them.
Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.5 out of 5 based on 16 reviews.

Uni Mathe, A-Levels, Matura etc., by a Cambridge Grad (Vienna)
Volodymyr
Very good teacher, attentive and systematic. My son understood a lot from the lesson and it was very helpful for the IB programme.
Review by IULIA
Mathematics, Statistics, and Econometrics up to university level | 3 diplomas (Rotterdam)
Yinhao
Amazing at explain complex concepts and breaking it down in simple words. It really helped me prepare well for my exams.
Review by USER
Private lessons in Maths and Statistics - English or Spanish (Solna)
Victor
Good teacher. Victor is a teacher who is able to explain your question in easy way to understand.
Review by ZEYAD

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