# All Educational Materials for Jacob William Martin

## Popular Study Guides

UVMSTAT 111Jacob William MartinSpring

## STAT 111 Study Guide - Spring 2019, Comprehensive Final Exam Notes - Null Hypothesis, Standard Deviation, Dependent And Independent Variables

OC196065522 Page
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Regardless of your field, interests, lifestyle, etc. you will most definitely have to make decisions based on data. Data set of measurements taken on a
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UVMSTAT 111Jacob William MartinFall

## STAT 111- Final Exam Guide - Comprehensive Notes for the exam ( 48 pages long!)

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Stats is all about data: collecting, describing (summarizing, visualizing), analyzing data. Data: set of measurements taken on a set of individual unit
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Frequently-seen exam questions from 2014 - 2018.
UVMEE 113AllSpring

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UVMEE 100AllFall

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UVMBCOR 102AllFall

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UVMEE 275AllFall

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UVMPHYS 128AllSpring

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UVMPHYS 128AllFall

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UCLAPSYCH 10Courtney ClarkFall

## PSYCH 10 Lecture Notes - Lecture 1: Psych

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UCLAPSYCH 10Courtney ClarkFall

## PSYCH 10 Study Guide - Midterm Guide: Operant Conditioning, Reinforcement

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UCLALIFESCI 7AMaloySpring

## LIFESCI 7A Midterm: BIOLOGY 7A PRACTICE MIDTERM #2

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UCLAPSYCH 10Courtney ClarkFall

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## Popular Class Notes

UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 14: Publication Bias, Randomized Experiment, Statistical Hypothesis Testing

OC11813274 Page
0
Smaller p-value the stronger the evidence against ho. 2 possible conclusions of formal hypothesis: p-value is small, reject the null hypothesis in favo
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 18: Statistic, Test Statistic

OC11813273 Page
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Section 7. 1 chi-square goodness-of-fit test for a single categorical variable. Multiple categories: we know how to test a proportion for a single cate
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 17: Confidence Interval, Standardized Test, 2Degrees

OC11813274 Page
0
Se = the larger the sample size, the smaller the se. If either: a) the variable has a normal distribution in the population (for any sample size, b) or
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 12: Sampling Distribution, Confidence Interval, Percentile

OC11813272 Page
0
Option 1: estimate the se of the statistic by computing sd of bootstrap distribution and then generate a 95% confidence interval by, statistic 2 x se.
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 13: Monty Hall Problem, Monty Hall, Null Hypothesis

OC11813272 Page
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There are 3 doors (door 1,2,3) behind 2 doors are 2 goats and behind the other door is a car. Monty hall examines the other doors (1,3) and always open
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 10: Statistical Inference, Point Estimation, Statistic

OC11813274 Page
0
Process of drawing conclusions about entire population based on info in a sample. Use statistic from a sample as a best (point) estimate for a populati
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 4: Interquartile Range, Standard Deviation, Minimax

OC11813274 Page
0
Section 2. 2 one quantitative variable: shape and center. Shape: symmetric, skewed (left-skewed or right-skewed, determine which direction it is skewed
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 15: Statistical Parameter, Null Hypothesis, Confidence Interval

OC11813271 Page
0
Our best guess at the distribution of sample statistics. Simulate sampling from the population by resampling from the original sample. Our best guess a
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 3: Statistical Parameter, Summary Statistics, Descriptive Statistics

OC11813272 Page
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In order to make sense of data, we need ways to summarize and visualize: type of summary statistics and visualization methods depend on type of variabl
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UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 16: Junk Food, Confidence Interval, Normal Distribution

OC11813278 Page
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The symmetric bell-shaped curve we have seen for almost all of our distribution of statistics is called a normal distribution. For random samples with
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## Most Popular

UVMSTAT 111Jacob William MartinFall

## STAT 111- Final Exam Guide - Comprehensive Notes for the exam ( 48 pages long!)

OC118132748 Page
0
Stats is all about data: collecting, describing (summarizing, visualizing), analyzing data. Data: set of measurements taken on a set of individual unit
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 14: Publication Bias, Randomized Experiment, Statistical Hypothesis Testing

OC11813274 Page
0
Smaller p-value the stronger the evidence against ho. 2 possible conclusions of formal hypothesis: p-value is small, reject the null hypothesis in favo
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 18: Statistic, Test Statistic

OC11813273 Page
0
Section 7. 1 chi-square goodness-of-fit test for a single categorical variable. Multiple categories: we know how to test a proportion for a single cate
View Document
UVMSTAT 111Jacob William MartinSpring

## STAT 111 Study Guide - Spring 2019, Comprehensive Final Exam Notes - Null Hypothesis, Standard Deviation, Dependent And Independent Variables

OC196065522 Page
0
Regardless of your field, interests, lifestyle, etc. you will most definitely have to make decisions based on data. Data set of measurements taken on a
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 17: Confidence Interval, Standardized Test, 2Degrees

OC11813274 Page
0
Se = the larger the sample size, the smaller the se. If either: a) the variable has a normal distribution in the population (for any sample size, b) or
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 12: Sampling Distribution, Confidence Interval, Percentile

OC11813272 Page
0
Option 1: estimate the se of the statistic by computing sd of bootstrap distribution and then generate a 95% confidence interval by, statistic 2 x se.
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 13: Monty Hall Problem, Monty Hall, Null Hypothesis

OC11813272 Page
0
There are 3 doors (door 1,2,3) behind 2 doors are 2 goats and behind the other door is a car. Monty hall examines the other doors (1,3) and always open
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 10: Statistical Inference, Point Estimation, Statistic

OC11813274 Page
0
Process of drawing conclusions about entire population based on info in a sample. Use statistic from a sample as a best (point) estimate for a populati
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 4: Interquartile Range, Standard Deviation, Minimax

OC11813274 Page
0
Section 2. 2 one quantitative variable: shape and center. Shape: symmetric, skewed (left-skewed or right-skewed, determine which direction it is skewed
View Document
UVMSTAT 111Jacob William MartinFall

## STAT 111 Lecture Notes - Lecture 15: Statistical Parameter, Null Hypothesis, Confidence Interval

OC11813271 Page
0
Our best guess at the distribution of sample statistics. Simulate sampling from the population by resampling from the original sample. Our best guess a
View Document

## Most Recent

UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture Notes - Lecture 17: Null Hypothesis, Test Statistic, Design Patterns

OC19606552 Page
0
Difference between what is observed and what is expected. Sample size: ecpcted counts for each cell > 5. If all the conditions are met, the test statis
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture 16: Lecture 16

2 Page
0
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Study Guide - Spring 2019, Comprehensive Final Exam Notes - Null Hypothesis, Standard Deviation, Dependent And Independent Variables

OC196065522 Page
0
Regardless of your field, interests, lifestyle, etc. you will most definitely have to make decisions based on data. Data set of measurements taken on a
View Document
UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture Notes - Lecture 14: Null Hypothesis

OC19606551 Page
0
Type 1 error = rejecting a true null hypothesis (false positive) Type 2 error = not rejecting a false null hypothesis (false negative) The probability
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture Notes - Lecture 13: Null Hypothesis, Statistic, Alternative Hypothesis

OC19606552 Page
0
If it is very unusual, we have statistically significant evidence against the null hypothesis. To see if a statistic provides evidence against h0, we n
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture Notes - Lecture 12: Monty Hall Problem, Null Hypothesis, Alternative Hypothesis

OC19606552 Page
0
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture Notes - Lecture 11: Confidence Interval, Standard Deviation, Sampling Distribution

OC19606552 Page
0
To create a confidence interval we need two things: 95% ci = statistic +/- 2 x se. We only need one sample for a statistic, but we need many samples to
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture 10: Lecture 10

OC19606551 Page
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An interval estimate gives a range of plausible values for a population parameter. One common form for an interval estimate is: statistic +/- margin of
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture 8: Lecture 8

OC19606551 Page
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Equatino of the line the estimated regression line is y^ = a + bx y^ is the predicted response. Prediciton the predicted response is on the regression
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UVMSTAT 111Jacob William MartinSpring

## STAT 111 Lecture Notes - Lecture 7: Scatter Plot

OC19606552 Page
0
Used for detecting patterns, trends, relationships, and extraordinary values. A positive association means that the values of one variable tend to be h
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