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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
William - Accra, GhanaC$40
C$40Research 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
(5 reviews)
Ahmed - Brussels, BelgiumC$101
C$101Statistics 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
Statistics
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Juan - ChileC$28
C$28Statistics and Probability classes at all levels
Trusted teacher: I have a degree in Statistics and have more than 10 years of experience as a teacher. I have experience in Civil Aviation, Banking, Technology and the Education Sector. I have worked on Statistical Information Systems implementation projects. What can I teach you? Consulting in data analysis and processing for assignments, theses, academic tests, exams, and homework. Topics: *Descriptive statistics: Mean, Mode, Variance, Standard Deviation, Statistical Graphs *Probabilities: Probability calculation, Continuous and discrete probability distributions (Normal, Binomial, Poisson, Gamma, Uniform, etc.) *Inferential statistics: Point estimates and confidence intervals, Hypothesis testing *Database management: SQL *Data analysis *Linear and non-linear models *Supervised and unsupervised learning (Machine Learning): Logistic regression, Kmeans, Clustering, Neural Networks, Decision trees) *Data mining *Text mining *Sampling: Selection of samples *Design of experiments What programs do I use? *R *Python *SPSS *Minitab *Orange *RapidMiner *SQL *Power BI *Canva Where do I teach my classes? Online classes via Zoom Online classes via Teams Online classes via Google Meets Languages in which I can give you classes: *Spanish *English *Portuguese My methodology: *Practical, simple and easy to learn classes. *I adapt my classes to the student's learning style, the guides and materials available to the student. *I use digital whiteboards to teach my classes. *Interactive classes. *Theoretical and practical classes. *Management of subject content, extensive experience in exams. *I can give you tips on the topics and problem solving. In addition to statistics, I can also help you with: *Mathematics and Calculus (Derivatives, Integrals, Limit Calculations, etc.) *Econometrics *Macroeconomy *Microeconomics *Algebra *Operations research *Linear programming *English *Document translation
Statistics · Math
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Only reviews of students are published and they are guaranteed by Apprentus. Rated 4.9 out of 5 based on 22 reviews.

Programming and Data Analysis, Matlab, Python, Fortran, SPSS
Nikolaos
Nikolas is a very depentable tutor with an abudant amount of knowledge in programming. He was very helpful in preparing me for my programming exam and I higly recommend him!
Review by KYRIAKOS
Mathematics, Statistics, Python and R Programming Tutoring (Leuven)
Luana
Luana is a good teacher, she is patient and brings practical examples to understand statistical issues.
Review by GREICE
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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