Garimidi sivasree
Math Maverick: Transforming Numbers into Your Superpower!
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Garimidi sivasree
Bachelors degree
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Each lesson is 55 min
50 lessons
20% off
/ lesson
30 lessons
15% off
/ lesson
20 lessons
10% off
/ lesson
10 lessons
5% off
/ lesson
5 lessons
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/ lesson
1 lessons
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/ lesson
Garimidi - Know your tutor
I am Dr. Siva Sree. I have completed my Ph.D. in the field of human resources. I have more than 5 years of research experience. I have more than two years of teaching experience in business administration subjects. Along with this, i also teach subjects like statistics and mathematics. I have research publications in several national and international journals. I have been currently working as an assistant professor. I teach the concepts in simple and easily understandable ways while laying emphasis on connecting theory to practice.
Garimidi graduated from JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY
Student types for classes
College
Anxiety or Stress Disorders
Class overview
My tutoring sessions are generally detailed. I help students with their assignments and test preparation. I value my students and take it on regular basis and tailor my pedagogies accordingly.
Mathematics concepts taught by Garimidi
The student and tutor worked through practice problems related to hypothesis testing and confidence intervals for proportions. They reviewed p-values, error types (Type I and Type II), and assumptions for statistical tests. The session concluded with formula-based calculations for sample size and combined mean/standard deviation.
Two Proportion Hypothesis Testing
Pooled Sample Proportion
Interpreting P-values
Confidence Intervals and Hypothesis Testing
Type I and Type II Errors
Sample Size Calculations
Combined Mean and Standard Deviation
P-value and Significance Level (α)
Confidence Intervals
Power of a Test
Type I and Type II Errors
Z-Test Statistic
Hypothesis Testing: Null and Alternate Hypotheses
The session covered normal distributions, the 68-95-99.7 rule, and standard deviation calculations. The Student practiced applying these concepts to probability problems. They will review chapter theory and examples in preparation for their exam, and potentially schedule another session to cover specific questions.
Standard Deviation of the Difference Between Two Random Variables
Z-Score Calculation in Difference of Means
68-95-99.7 Rule (Empirical Rule)
Conditions for Using a Normal Model for Proportions
Standard Deviation of a Sample Proportion
Calculating Probabilities Using the Normal Model for Proportions
The Student received instruction on hypothesis testing, including null and alternative hypotheses, significance levels, and one-tailed versus two-tailed tests. The Student then worked through a problem involving strikeout rates in baseball, calculating the test statistic and interpreting the P-value. The next session will focus on reviewing earlier chapters of the textbook.
Null Hypothesis (H₀)
Alternate Hypothesis (H₁ or Hₐ)
Significance Level (α)
P-value
Critical Region (Rejection Region)
One-Tailed vs. Two-Tailed Tests
Standard Error
The session focused on random variables, distinguishing between continuous and discrete variables, and calculating expected values and standard deviations for discrete random variables. The Student practiced applying these concepts to a word problem involving expected profit. As homework, the Student will attempt additional problems related to discrete and continuous random variables, and is expected to ask any questions in the next session.
Probability Models for Discrete Random Variables
Applying Expected Value to Decision-Making
Variance and Standard Deviation of a Discrete Random Variable
Random Variables: Discrete vs. Continuous
Expected Value (Mean) of a Discrete Random Variable
The Tutor and Student reviewed correlation, regression, correlation coefficient, and the coefficient of determination. They also covered simple linear regression and influential points on scatter plots. The student will complete the second worksheet in the next session.
Slope Interpretation
Coefficient of Determination (R²)
Regression Equation
Linear Regression
Correlation Coefficient (R)
Correlation
Scatter Plots
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