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All Educational Materials for STAT-3090 at Clemson University

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STAT 2300 Test 3 VA - F18

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31 Jan 2019
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STAT 2300 Test 2 VA - S18 - Grading Guidelines

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CLEMSON USpring

STAT 2300 Test 3 VA - S18

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31 Jan 2019
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STAT 2300 Test 2 VA - F18

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31 Jan 2019
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STAT 2300 Test 1 VA - F18

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STAT 2300 Test 3 VA - F18 - Grading Guidelines

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31 Jan 2019
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STAT 2300 Test 1 VA - F18 - Grading Guidelines

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31 Jan 2019
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CLEMSON USpring

STAT 2300 Test 1 VA - S18 - Grading Guidelines

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31 Jan 2019
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CLEMSON USpring

STAT 2300 Test 2 VA - S18

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31 Jan 2019
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CLEMSON USpring

STAT 2300 Test 1 VA - S18

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31 Jan 2019
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CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 3: Pie Chart, Bar Chart, Frequency Distribution

8 Page
2 Oct 2017
From information about baseball to information about income, the most basic task when working with data is to summarize a great deal of information. Gr
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 2: Flu Season, Tai Chi, Confounding

9 Page
13 Feb 2018
Chapter 2 data, reality and problem solving. For years the tobacco industry argued that the only way to answer this question was to conduct a controlle
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Binomial Distribution, Random Variable, Free Throw

4 Page
13 Feb 2018
Chapter 6 - discrete probability distributions: information about the future. In this section we explore a specific type of random variable called a bi
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Poisson Distribution, Binomial Distribution, Landfall

4 Page
13 Feb 2018
6. 6 objectives: recognize the properties of a poisson experiment, calculate probabilities using the poisson formula, calculate and interpret the expec
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CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 4: Xi Xi, Frequency Distribution, Weighted Arithmetic Mean

11 Page
2 Oct 2017
Number of observations = x / i. Typically, we compare the center of distributions, the variability of the distribution, the shape of each distribution
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CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 4: Percentile, Central Tendency, Box Plot

8 Page
2 Oct 2017
Form an ordered array by placing the data in order from smallest to largest. In the previous sections, we determined measures of central tendency, whic
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CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Random Variable, Sample Space, Probability Distribution

7 Page
13 Feb 2018
Chapter 6 - discrete probability distributions: information about the future. The ideas of randomness and uncertainty were introduced in the last chapt
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CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 5: Free Throw, Sample Space, Statistical Inference

9 Page
13 Feb 2018
Businesses use probability as the basis for decision making in a world full of risk and uncertainty. Probability can be viewed as the foundation for st
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CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 2: Flu Season, Design Of Experiments, Dependent And Independent Variables

10 Page
2 Oct 2017
Chapter 2 data, reality and problem solving. Does cigarette smoking cause cancer? to answer this question was to conduct a controlled experiment in whi
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CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 1-3: Bar Chart, Time Series, The Bell Curve

13 Page
29 Jan 2018
In these cases, input conditions will be varied between two or more groups and the numerical outputs contrasted. For example, we manufacture one batch
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CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 13.1-13.5, 13.8: Mathematical Model, Standard Deviation, Dependent And Independent Variables

53 Page
4 Feb 2018
Is a representation of a phenomenon using mathematical principles: makes use of variables modeling unknown or known quantities and relationships betwee
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CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 9-10: Central Limit Theorem, Sampling Distribution, Null Hypothesis

30 Page
29 Jan 2018
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CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 6-8: Cluster Sampling, 3 Women, Sample Size Determination

27 Page
29 Jan 2018
Pg 282: a random variable is the outcome of a random process. Random variables can only assume one value at a time, and a chance process always impacts
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CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 10.2: Type I And Type Ii Errors, Test Statistic, Sampling Distribution

9 Page
4 Feb 2018
Type i error and type ii error: if the value of the test statistic falls in the rr, we reject h. We conclude that the sample evidence is insufficient t
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CLEMSON USTAT-3090kayla javiarFall

STAT 3090- Final Exam Guide - Comprehensive Notes for the exam ( 35 pages long!)

35 Page
28 Mar 2018
Chapter 2 data, reality and problem solving. For years the tobacco industry argued that the only way to answer this question was to conduct a controlle
View Document
CLEMSON USTAT-3090Sharon EvansFall

STAT 3090- Midterm Exam Guide - Comprehensive Notes for the exam ( 39 pages long!)

39 Page
5 Oct 2017
Chapter 2 data, reality and problem solving. Does cigarette smoking cause cancer? to answer this question was to conduct a controlled experiment in whi
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CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 3: Pie Chart, Bar Chart, Frequency Distribution

8 Page
2 Oct 2017
From information about baseball to information about income, the most basic task when working with data is to summarize a great deal of information. Gr
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 2: Flu Season, Tai Chi, Confounding

9 Page
13 Feb 2018
Chapter 2 data, reality and problem solving. For years the tobacco industry argued that the only way to answer this question was to conduct a controlle
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Binomial Distribution, Random Variable, Free Throw

4 Page
13 Feb 2018
Chapter 6 - discrete probability distributions: information about the future. In this section we explore a specific type of random variable called a bi
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Poisson Distribution, Binomial Distribution, Landfall

4 Page
13 Feb 2018
6. 6 objectives: recognize the properties of a poisson experiment, calculate probabilities using the poisson formula, calculate and interpret the expec
View Document
CLEMSON USTAT-3090AllSpring

STAT 3090- Final Exam Guide - Comprehensive Notes for the exam ( 134 pages long!)

134 Page
28 Mar 2018
View Document
CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 1-3: Bar Chart, Time Series, The Bell Curve

13 Page
29 Jan 2018
In these cases, input conditions will be varied between two or more groups and the numerical outputs contrasted. For example, we manufacture one batch
View Document
CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 4: Xi Xi, Frequency Distribution, Weighted Arithmetic Mean

11 Page
2 Oct 2017
Number of observations = x / i. Typically, we compare the center of distributions, the variability of the distribution, the shape of each distribution
View Document
CLEMSON USTAT-3090Sharon EvansFall

STAT-3090 Lecture Notes - Lecture 4: Percentile, Central Tendency, Box Plot

8 Page
2 Oct 2017
Form an ordered array by placing the data in order from smallest to largest. In the previous sections, we determined measures of central tendency, whic
View Document

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CLEMSON USTAT-3090kayla javiarFall

STAT 3090- Final Exam Guide - Comprehensive Notes for the exam ( 35 pages long!)

35 Page
28 Mar 2018
Chapter 2 data, reality and problem solving. For years the tobacco industry argued that the only way to answer this question was to conduct a controlle
View Document
CLEMSON USTAT-3090AllSpring

STAT 3090- Final Exam Guide - Comprehensive Notes for the exam ( 134 pages long!)

134 Page
28 Mar 2018
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Poisson Distribution, Binomial Distribution, Landfall

4 Page
13 Feb 2018
6. 6 objectives: recognize the properties of a poisson experiment, calculate probabilities using the poisson formula, calculate and interpret the expec
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Random Variable, Sample Space, Probability Distribution

7 Page
13 Feb 2018
Chapter 6 - discrete probability distributions: information about the future. The ideas of randomness and uncertainty were introduced in the last chapt
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 6: Binomial Distribution, Random Variable, Free Throw

4 Page
13 Feb 2018
Chapter 6 - discrete probability distributions: information about the future. In this section we explore a specific type of random variable called a bi
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 5: Free Throw, Sample Space, Statistical Inference

9 Page
13 Feb 2018
Businesses use probability as the basis for decision making in a world full of risk and uncertainty. Probability can be viewed as the foundation for st
View Document
CLEMSON USTAT-3090kayla javiarFall

STAT-3090 Lecture Notes - Lecture 2: Flu Season, Tai Chi, Confounding

9 Page
13 Feb 2018
Chapter 2 data, reality and problem solving. For years the tobacco industry argued that the only way to answer this question was to conduct a controlle
View Document
CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 13.1-13.5, 13.8: Mathematical Model, Standard Deviation, Dependent And Independent Variables

53 Page
4 Feb 2018
Is a representation of a phenomenon using mathematical principles: makes use of variables modeling unknown or known quantities and relationships betwee
View Document
CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 10.2: Type I And Type Ii Errors, Test Statistic, Sampling Distribution

9 Page
4 Feb 2018
Type i error and type ii error: if the value of the test statistic falls in the rr, we reject h. We conclude that the sample evidence is insufficient t
View Document
CLEMSON USTAT-3090AllSpring

STAT-3090 Chapter Notes - Chapter 9-10: Central Limit Theorem, Sampling Distribution, Null Hypothesis

30 Page
29 Jan 2018
View Document

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