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Award Winning Private Statistics Tutoring in Washington DC
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Statistics Tutoring in Washington DC
Customized private in-home and online tutoring
Experience Statistics tutoring by highly credentialed tutors in Washington DC. Top tutors will help you learn Statistics through one-on-one tutoring in the comfort of your home, online, or any other location of your choice.
Selected Washington DC Statistics Tutors
These highly-credentialed Statistics tutors in Washington DC are uniquely qualified to help you. They have attended institutions including MIT, Stanford, UChicago, Yale, Harvard, UPenn, Notre Dame, Amherst, UC Berkeley, Northwestern, Rice, Columbia, WashU, Emory, Brown, Johns Hopkins, Vanderbilt, UNC, Michigan, UCLA, and other leading schools.
University Of Virginia - Economics
University Of Virginia - Law
Gettysburg College - Physics, Mathematics
Loyola University - Finance
University Of California, Santa Barbara - Psychology; Communication
University Of Nevada, Las Vegas - Educational Psychology
How your tutor helps you master: Statistics
IDENTIFYING STATISTICS GOALS
You will have learning objectives mapped out by our educational director based on your specific academic needs.
IDENTIFYING STRENGTHS & WEAKNESSES
Your skills and progress will be assessed by your Statistics tutor to help shape and define your lesson plan.
PERSONALIZED EDUCATION PLAN
Your tutor will create a personalized learning program, guiding you toward your Statistics objectives.
Recent Tutoring Session Reviews
We discussed statistical inference; what it means, the types of tests, hypotheses and confidence intervals. We did not work many examples but instead discussed the why and how behind the statistics. She can better differentiate between the formulas and concepts. She does well when it comes to specific tests and quizzes when she knows what is going to be covered.
We continued to work on expected value and variance of data distributions, as well as working on the normal distribution. We went over z scores, how to convert them to relevant data, and how to interpret probabilities from normally distributed data. I had her go through her homework problems with me for review. She did very well and was able to pick up on all the new topics very quickly.
The student had a Chemistry lab to work on and a Statistics test to study for. We first worked on his chemistry lab together. It was on calorimetry. We then worked on studying for his stats test. There was a practice test at the end of the chapter in his book, and we did the entire thing together. It was particularly difficult, and we spent a good amount of time working on English explanations.
We went over probability, including the definitions of union and intersection, working with Venn diagrams, the complement, conditional probability, and Bayes' Rule. We worked through problems in each set.
The student did REALLY well on her homework. She's really starting to understand areas under the curve. I assigned her some reading for next week.
We went over two classes' worth of the student's notes and a quiz she took recently. She seems pretty solid on the concepts, but appears to benefit from having someone there to help her go through the nuts and bolts of the explanations her teacher uses. Overall, a very good session.
He had been talking about sampling distributions in class, so we went through the logic process of setting those problems up. We talked about how to distinguish between a problem using means and one using proportions. He was having some trouble deciding which method to use to compute areas under the curve in various situations so we reviewed some of that.
Today, we went over propositional logic by looking at examples and doing exercises. We looked at some basic definitions, identities and the truth table. She started with lots of confusions on the topic but ended up doing most of her homework towards the end of the session. She is struggling a little with manipulating long expressions, but that's okay because it takes a while to get used to all the identities.
The student and I met up to review the material on his exam on Wednesday. We first ran through the questions on the study guide and made sure that he was familiar with each of the concepts. I feel that he has most of the theory behind t-tests down.
We also worked through an example problem for each of the three types of tests that will be on the exam. He was able to work single sample and paired-samples tests from memory, though I did help him with the independent samples test. I strongly suggested that he complete each of the practice problems given by his professor before the exam.
Covered Chapter 8: Confidence Interval for Population Mean; Sect. 8.1: Confidence Interval for Population Mean when Standard Deviation Known; Sect. 8.2: Confidence Interval for Population Mean when Standard Deviation Unknown; Sect. 8.4: Confidence Interval for Population Proportion (large sample size); Chapter 9: Hypothesis Testing; Sect. 9.3: Hypothesis Test for Population Mean & Standard Deviation Known; did flow chart of when to use t-test and z-test; wrote notes on how to compute p-value
We covered regression basics and intro probability problems. The probability problems were calculating probability using frequency tables, calculating probability using four basic formulas, and calculating conditional probabilities. Student struggling most with calculation details and how to start on problems. Once helped with starting a problem, she seemed to have a solid grasp of the concepts involved. I left her with several practice problems to go over next time and told her to email me if she has any questions.
We caught up on quiz questions, the execution of his project, and worked ahead in his textbook to cover the majority of the content in the chapter on correlation coefficients. I gave him an overview of the mathematical terminology involved in most equations, including the Greek letter sigma. I also emphasized to him the importance of using mathematical language, terms, and conventions despite his school's lack of emphasis on those skills. I also gave him an introduction to Z scores, which came up in the context of the mechanics of calculating correlation coefficients.
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