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Statistics lessons in Geiro

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Susovan - Paris, FranceC$83
C$83Mathematics - ALL levels taught by a PhD in mathematics with five years of teaching/tutoring experience and also experience in industry
Trusted teacher: I'm in-person and online tutor in mathematics at ALL levels, based in Paris, France. I received my PhD(doctorate) in in mathematics in the US (Rutgers University, New Brunswick, NJ), and held my postdoctoral positions in reputed institutions in France (Ecole Polytechnique, INRIA) and USA (University of California, Los Angeles). After my postdoctoral research positions, I've also worked as a data scientist in several companies in Paris, France. I love to share my passion for mathematics, statistics and machine learning with my students, by means of tutoring, seminars, talks etc. Besides mathematics, I'm also keen about about cooking, and happy to provide tips if needed. For people who needs lessons in French, I can actively try, as my French is intermediate (B2). I'm also certainly able to provide credentials and certificates upon request. I've extensive teaching and tutoring experience in precalculus, algebra, differential equations as well as many other subjects. I also studied and had great scores in GRE, subject GRE and other competitive exams. I'm well equipped with tutoring remotely. Please note that I do not travel to students and prefer to give the course online - I can promise you that it's even better, since you'll be able to record the call and also have the screenshot of the boards. But if you insist on having it in person, you've to come to me, I won't travel.
English · Math · Statistics
Keivan - Pointe-Calumet, QC, CanadaC$72
C$72All MATH ENGR MECH Courses (Mechanical Engineering)
Dr. Keivan is a McGill University graduate with the following degrees: Master of Mechanical Engineering (McGill) Bachelor of Mechanical Engineering (McGill) Doctor of Medicine M.D (Iran) Dr. Keivan has more than 15 years’ experience in teaching many MATH, ENGR, and MECH courses for University students. He has been teaching assistant for many courses at Concordia University and McGill University in Montreal, with excellent course evaluation by students. The most featured courses are undergraduate and graduate mechanical engineering courses, and probability and statistics. Both in person and online classes are offered. For more information, you can contact Dr. Keivan at (514)4762075 Concordia Courses: COMM 215: Business Statistics COMP 233: Probability & Statistics ECON 325: Mathematics for Economists I ECON 326: Mathematics for Economists II ELEC 275: Principles of Electrical Engineering ENGR 213: Applied Ordinary Differential Equations ENGR 233: Applied Advanced Calculus ENGR 242: Statics ENGR 243: Dynamics ENGR 244: Mechanics of Material ENGR 251: Thermodynamics I ENGR 264: Signals and Systems I ENGR 273: Basic Circuit Analysis ENGR 301: Management Principals and Economics ENGR 311: Calculus and Partial Differential Equations ENGR 351: Thermodynamics II ENGR 361: Fluid Mechanics I ENGR 371: Probability and Statistics ENGR 391: Numerical Methods INDU 371: Random Processes INTE 296: Discover Statistics MATH 201: Elementary Functions MATH 202: College Algebra MATH 203: Differential and Integral Calculus I MATH 204: Vectors and Matrices MATH 205: Differential and Integral Calculus II MATH 206: Algebra and Functions MATH 208: Fundamental Mathematics I MATH 209: Fundamental Mathematics II MATH 251: Linear Algebra I MATH 252: Linear Algebra II MATH 264: Advanced Calculus I MATH 265: Advanced Calculus II MECH 211: Mechanical Engineering Drawing MECH 215: Programming for Mechanical and Industrial MECH 221: Material Science MECH 313: Machine Drawing and Design MECH 361: Fluid Mechanics II MECH 368: Electronics for Mechanical Engineers MECH 370: Modeling and Analysis of Dynamic Systems MECH 371: Fundamentals of Control Systems MECH 375: Mechanical Vibrations MECH 6121: Aerodynamics PHYS 204: Mechanics PHYS 205: Electricity and Magnetism PHYS 206: Waves and Optics PSYC 315: Statistical Analysis I PSYC 316: Statistical Analysis II SOCI 212: Statistics I SOCI 213: Statistics II STAT 249: Probability I STAT 250: Statistics STAT 360: Linear Models McGill Courses: CIVE 205: Statics CIVE 206: Dynamics CIVE 207: Solid Mechanics CIVE 290: Thermodynamics and Heat Transfer CIVE 302: Probabilistic Systems CIVE 320: Numerical Methods CIVE 327: Fluid Mechanics and Hydraulics ECON 208: Microeconomics Analysis and Applications ECON 227: Economic Statistics MATH 112: Fundamentals of Mathematics MATH 122: Calculus for Management MATH 123: Linear Algebra and Probability MATH 133: Linear Algebra and Geometry MATH 139: Calculus I with Pre-calculus MATH 140: Calculus I MATH 141: Calculus II MATH 150: Calculus A MATH 203: Principles of Statistics I MATH 204: Principles of Statistics II MATH 222: Calculus III MATH 223: Linear Algebra MATH 262: Intermediate Calculus MATH 263: Ordinary Differential Equations for Engineers MATH 270: Applied Linear Algebra MATH 271: Linear Algebra and Partial Differential Equations MATH 315: Ordinary Differential Equations MATH 316: Complex Variables MATH 323: Probability MATH 324: Statistics MATH 329: Theory of Interest MECH 210 Mechanics I MECH 220 Mechanics II MECH 240 Thermodynamics I MECH 289 Design Graphics MECH 290: Design Graphic for Mechanical Engineers MECH 309: Numerical Methods in Mechanical Engineering MECH 314: Dynamics of Mechanisms MECH 315: Mechanics III MECH 361: Fluid Mechanics I MECH 341: Thermodynamics II MECH 346: Heat Transfer MECH 383: Applied Electronics and Instrumentation MECH 393: Machine Element Design MECH 412: System Dynamics and Control MECH 419: Advanced Mechanics of Systems MECH 430: Fluid Mechanics II MECH 513: Control Systems MECH 533: Subsonic Aerodynamics MECH 542: Spacecraft Dynamics MECH 562: Advanced Fluid Mechanics MECH 605: Applied Math I MECH 642: Advanced Dynamics MGCR 271: Business Statistics MGSC 372: Advanced Business Statistics PHYS 101: Introductory Physics – Mechanics PHYS 102: Introductory Physics – Electromagnetism PHYS 131: Mechanics and Waves PHYS 142: Electromagnetism and Optics PSYC 204: Introduction to Psychological Statistics PSYC 305: Statistics for Experimental Design
Mechanical engineering · Math · Statistics
Juan - Guayaquil, EcuadorC$34
C$34Statistical 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
(5 reviews)
Ahmed - Brussels, BelgiumC$102
C$102Statistics Courses By a University Professor
Trusted teacher: In order to support students who encounter difficulties in statistics, remedial sessions on descriptive statistics and inferential statistics are offered in particular on: confidence interval, statistical hypothesis test, Paired data or not? "Normal" or not? Parametric and non-parametric tests, Confidence interval and "number of subjects needed" in estimating a proportion, Also the analysis of your thesis data (linear / logistic regression, anova, t-tests, principal component analysis, repeated measures, mixed models, time series, panel analyzes, fixed and random effects models, survival analysis, SEM, ...). Do you need to be accompanied to motivate you? I offer lessons to see or review together a chapter, a theme or others ... During these remediation sessions, I offer you various activities (Q&A sessions, resolution of standard exams, revision of concepts more difficult to integrate, etc.) which help students to fill in the gaps identified in previous courses. Passionate about mathematics and quantum physics, juggles with statistics on a daily basis in my BIGDATA research work, demystifies this test by the precise approach of the subjects via an adapted teaching whose key words - study methodology, - reasoning, and especially learning to reason differently - analysis and response strategies, - targeting of the problem - implementation of the link between theory and practice
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Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.9 out of 5 based on 18 reviews.

Private tuition in math, french, english beginner to advanced (Ottignies-Louvain-la-Neuve)
Coralie
Great class, always constructive and searching the way to teach something useful to my daughter. We sincerely thank you and acknowledge your concern and dedication in providing a quality education and atmosphere
Review by BORJA
Mathematics, Economics (microeconomics, macroeconomics), Statistics, Probabilities (Copenhagen)
Constance
My daughter likes Consance very much. She is always on time and explains the problems so my daughter understands. My daughter feels it has made a difference with her math practice..
Review by EVA-LOTTA STRANDH
Statistics for Secondary School and University students (Brussels)
Akshaya
Very helpful, organized and professional so far!
Review by NIDA

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