Statistical inference: Hypothesis Testing and t-testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Statistical inference: Hypothesis Testing and t-testsEugene Yan Ziyou
This deck was used in the IDA facilitation of the John Hopkins' Data Science Specialization course for Statistical Inference. It covers the topics in week 3 (hypothesis testing and t tests).
The data and R script for the lab session can be found here: https://github.com/eugeneyan/Statistical-Inference
Probability distribution is a way to shape the sample data to make predictions and draw conclusions about an entire population because most improvement projects and scientific research studies are conducted with sample data rather than with data from an entire population. Probability distribution helps finding all the possible values a random variable can take between the minimum and maximum possible values
inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
Random Variable
Discrete Probability Distribution
continuous Probability Distribution
Probability Mass Function
Probability Density Function
Expected value
variance
Binomial Distribution
poisson distribution
normal distribution
Probability distribution is a way to shape the sample data to make predictions and draw conclusions about an entire population because most improvement projects and scientific research studies are conducted with sample data rather than with data from an entire population. Probability distribution helps finding all the possible values a random variable can take between the minimum and maximum possible values
inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 8: Hypothesis Testing
8.3: Testing a Claim About a Mean
Random Variable
Discrete Probability Distribution
continuous Probability Distribution
Probability Mass Function
Probability Density Function
Expected value
variance
Binomial Distribution
poisson distribution
normal distribution
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.3 Two Means, Two Dependent Samples, Matched Pairs
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MATT JONES: As its name implies, the independent samples t-test has the
assumption of the independence of observations. But that's not always the case.
Sometimes we take multiple observations of the same unit of analysis, such as a
person, over time. In this case, we'll use a paired sample t-test, sometimes
referred to as the dependent sample t-test. Let's go to SPSS to see how we do
this.
To perform the paired sample t-test in SPSS, we once again go to Analyze,
Compare Means, and down to the Paired Sample T-test. SPSS doesn't require
much information here;; only the pair of variables of which we would like to test.
We have a simulated data set here for statistical anxiety of students. Students
were provided with an instrument that measures their anxiety around statistical
topics on a number of different constructs-- teachers, interpretation, asking for
help, worth, and self-conceptualization.
They were given the test at the beginning of the class and at the conclusion of a
class. Hence, why in the value labels we see pre-test and post-test. As a teacher,
I might have some interest in determining whether students felt more comfortable
with me or had lowering anxiety over time. This is perfect for a paired sample t-
test. To perform this paired sample t-test, we'll go to Analyze, Compare Means,
the Paired Sample T-test.
SPSS doesn't ask for much information;; only the pair of variables of which I
would like to test. In this case, teacher pre-test and teacher post-test. So this is a
classic before and after. The first piece of output I obtain from the paired sample
t-test are some descriptive statistics, specifically around the pairwise comparison
I'm looking at, which is the teacher subscale pre-test and post-test.
I see that there is mean on the pre-test of 17.32 and on the post-test, an 18.44.
So it appears, at least from a descriptive sense, that there is a higher mean on
the post-test than the pre-test. On the instrument, higher scores on an item or the
subscale indicate higher levels of anxiety for that specific attitude. Except for this
specific subscale, fear of statistics teachers, where higher scores actually
indicate lower levels of anxiety.
So if post scores are higher than pre scores, that means on average, students
feel lower levels of anxiety and more positive attitude about their statistics
teacher. I can see here, at least from a descriptive sense, that that appears to be
the case. But from the sample, I am performing a test of statistical significance.
Next to the mean, I'm provided with the sample size 25-- 25 observations pre-test
and 25 observations post-test, all the same person-- the standard deviation for
the pre-test and the post-test, and the standard error of the mean. ...
The Problem Statement By Dr. Marilyn Simon Find this a.docxoscars29
The Problem Statement
By Dr. Marilyn Simon
Find this and many other dissertation guides and resources at
www.dissertationrecipes.com
The problem statement is one of the most important components of your study. After
reading the problem statement, the reader will know why you are doing (or did) this study
and be convinced of its importance. In 180 -250 words you need to convince the reader
that this study MUST be done (or HAD to be done). Society or one of its institutions has
some pressing problem that needs (needed) closer examination. YOUR study will answer
(answered) some part of this serious problem in a unique and clever way.
The problem statement also explicates the paradigm (qualitative/quantitative/mixed) and the
methodology (correlation, evaluative, phenomenological, Delphi, historical, experimental,
etc.). A problem might be defined as an issue that exists in theory or practice that leads (lead)
to the need for your study. Never stray too far away from your problem as you conduct, or
discuss your research. Your dissertation will be judged on how well you solved the problem
posed, and how well you obtained your purpose.
The following template can be used to put your initial draft of your problem statement
together. This can then be converted into a lucid, scholarly, and clear problem statement
that meets all the items in the checklist that follows.
Template for initial draft of problem statement
There is a problem in ___________(societal organization). Despite _________________
(something that should be happening) ___________ is occurring (provide supporting
evidence). This problem has negatively impacted ____________(victims of problem)
because _________________. A possible cause of this problem is ___________ Perhaps
a study which investigates ___________ by a ________(paradigm/method) could remedy
the situation.
"I hear and I forget, I see and I remember, I do and I understand"
John Dewey on Experiential Learning.
http://www.dissertationrecipes.com/
PROBLEM STATEMENT
Average of ½ - ¾ page
180-250 words
√
1. General Problem/Observation identifying the need for the study, with
sufficient current evidence to support the extent of the problem. This can be
current literature or current statistics.
2. Specific “Problem” proposed for research. Evidence is provided that this
is a current problem. However the words current or today should not be in
the problem statement. A time reference can be included.
3. Introductory words describing the paradigm (quantitative, qualitative, or
mixed), the method, and research design are given and are appropriate to the
“problem.”
4. General population group affected by the problem is identified.
5. The geographic area where the problem exists is identified, if appropriate.
6. The gap in the literature is explained. .
INTRODUCTION TO HYPOTHESIS TESTING Chapters 9 and 11 D.docxmariuse18nolet
INTRODUCTION TO HYPOTHESIS
TESTING
Chapters 9 and 11
D
orit N
evo, 2013
1
INFERENTIAL STATISTICS
¢ We often use statistics to test theories.
— Theory: a prediction, or a group of predictions, about how
people, physical entities, and built devices behave.
¢ Theories begin as predictions, which are then
repeatedly tested in various settings to either
strengthen or refute them.
— Such testing often involves statistical inference, defined as
the drawing of conclusions about a population of interest
based on findings from samples obtained from that
population.
¢ We will cover:
— Hypothesis testing
— Analysis of Variance (ANOVA)
— Regression Analysis
HYPOTHESES TESTING
¢ Statistics is very much about expectations. We
aim to test specific expectations that we have
about the population’s parameters using sample
statistics. We call these expectations hypotheses.
— A hypothesis is some specific claim that we wish to
test.
¢ We study the probability of our sample’s outcome
given the hypothesized distribution of the
population
We believe that Our sample
mean is
Possible?
X~N(10,2) 12 Probably yes
X~N(10,2) 20 Probably not
HYPOTHESES
¢ We differentiate between the research hypothesis
and the null hypothesis.
— Example: A bank manager argues that, on average,
people carry $50 or more in their wallet. This claim
is the null hypothesis. The research hypothesis
contains the other side of this claim, that is – that
people carry less than $50. We can also write it as:
¢ H0: Average amount of money ≥ $50
¢ H1: Average amount of money < $50
— where H0 is used to notate the null hypothesis and H1
(sometimes denoted HA) is used to notate the
research hypothesis, commonly referred to as the
alternative hypothesis.
HYPOTHESES
¢ Researchers tell you that, on average, people have
200 or fewer friends on Facebook. However, you
believe that Facebook users, in fact, have more than
200 friends. You can set your hypotheses as:
— H0: Average number of friends ≤ 200
— H1: Average number of friends > 200
¢ A statistics professor wants to know if her section’s
grade average is different than that of other sections
of the course. The average for all other sections is
‘75’. To help the professor learn if her section’s grade
average is different than that of other sections, we
need to set up the following hypotheses:
— H0: Section’s grade average = 75
— H1: Section’s grade average ≠ 75
THREE FORMS OF HYPOTHESES
H0: Average
amount of money
≥ $50
H1: Average
amount of money
< $50
Lower-
Tail
Test
H0: Section
average = 75
H1: Section
average ≠ 75
Two-
Tail
Test H0: Average
number of
friends ≤ 200
H1: Average
number of
friends > 200
Upper-
Tail
Test
-4 -3 -2 -1 0 1 2 3 4
Z
The ‘equal’ sign (=, ≥, ≤) always goes in the null hypothesis
A FINAL NOTE ON HYPOTHESES
¢ Hypotheses must .
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance Per DC 20.docxmecklenburgstrelitzh
F ProjHOSPITAL INPATIENT P & L20162017Variance Variance %Per DC 2016Per DC 2017Total Number of Beds149149Maximum Occupancy55,74554,561Total Patient Days37,25037,926Actual Occupancy %ALOSDischarges by PayerMedicare/Medicaid4,9224,989Commercial Ins5,2415,099Private Pay/Bad Debt1,2801,162Total DischargesREVENUEGross Patient Revenue$ 161,325,872$ 135,365,715Contract Allowances, Uncollectables$ (84,696,083)$ (65,680,261) Net Patient RevenueMisc Income$ 378,530$ 303,233 NET REVENUEPatient Care Expenses Salaries $ 18,387,223$ 18,244,610Benefits $ 4,140,146$ 4,211,157Contract Labor $ 1,724,507$ 1,820,377Physician Contract Services$ 6,439,165$ 6,335,188Lab Services $ 1,589,648$ 1,575,808Radiology Services$ 2,336,043$ 2,343,920Rehabilitation Services$ 655,766$ 679,444General Supplies $ 653,941$ 689,766Medical Supplies $ 1,006,220$ 1,029,151Cost of Food $ 576,245$ 612,890Patient Transportation $ 35,324$ 36,031Total Patient Care ExpensesGeneral and Administrative ExpensesSalaries$ 8,450,134$ 8,629,126Benefits$ 2,001,199$ 1,993,174Contract Labor$ 157,925$ 161,015Purchased Services $ 1,285,925$ 1,355,602Medical Director $ 162,909$ 167,207Telephone$ 586,985$ 596,466Meals & Entertainment $ 254,517$ 289,185Travel$ 126,951$ 141,561General Supplies $ 332,069$ 337,874Postage$ 53,760$ 57,383Building Expense$ 2,685,376$ 2,950,379Equipment Rents $ 363,302$ 429,694Repairs and Maintenance $ 337,711$ 366,311Insurance$ 644,384$ 715,563Utilities $ 504,959$ 556,226Total General and Administrative ExpensesNet Operating Expenses NET PROFIT (LOSS) before Interest, Taxes and Depreciation (EBITDA)NET PROFIT (LOSS) %2017CASH FLOW 2016RELEVANT FINANCIAL RATIOS 2016What is your average Daily Revenue?Return on Assets (ROA)Return on Assets (ROA)Assume your AR Days are 55, what is your Total AR?Return on Equity (ROE)Return on Equity (ROE)What is your Average Daily Expense?Current RatioCurrent RatioAssume your AP Days are 35, what is your total AP?Debt RatioDebt RatioBALANCE SHEET 2016ASSETS Cash and EquivalentsAssume 45 days of ExpensesAssume 45 days of Expenses Accounts Receivable$ - 0$ - 0 Inventory All SuppliesAssume 55 days of suppliesAssume 55 days of suppliesTotal Current AssetsFixed Assets:xxxxxxxxxxxxxxxxxxxxxxxxxxxx Bldg and Equipment$ 14,700,779$14,700,779Total AssetsLIABILITIES AND EQUITYCurrent Liabilitiesxxxxxxxxxxxxxxxxxxxxxxxxxxxx Accounts Payable$ - 0$0Long Term Debtxxxxxxxxxxxxxxxxxxxxxxxxxxxx Bldg and Equipment$ 8,149,152$8,149,152Total LiabilitiesEquityTotal Liabilities and EquityITEMSPOINT VALUEOccupany Calcs2Hospital Cols B & C3Variance (2014-2013) $ and %2PPD 2013 - 20142Cash flow 20142Balance Sheet Calculations5Relevant Financial Ratios4Sub-Total20
35879 Topic: Discussion6
Number of Pages: 1 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
.
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and ...Daniel Katz
Why We Are Open Sourcing ContraxSuite and Some Thoughts About Legal Tech and the Modern Information Economy - By Michael Bommarito + Daniel Martin Katz from LexPredict
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Artificial Intelligence and Law - A Primer Daniel Katz
Artificial Intelligence in Law (and beyond) including Machine Learning as a Service, Quantitative Legal Prediction / Legal Analytics, Experts + Crowds + Algorithms
LexPredict - Empowering the Future of Legal Decision MakingDaniel Katz
LexPredict is an enterprise legal technology and consulting firm, specializing in the application of best-in-class processes and technologies from the technology, financial services, and logistics industries to the practice of law, compliance, insurance, and risk management.
We focus on the goals of prediction, optimization, and risk management to enable holistic organizational changes that empower legal decision-making.
These changes span people and processes, software and data, and execution and education.
Legal Analytics Course - Class 9 - Clustering Algorithms (K-Means & Hierarchical Clustering) - Professor Daniel Martin Katz + Professor Michael J Bommarito
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The French Revolution Class 9 Study Material pdf free download
Quantitative Methods for Lawyers - Class #13 - Students "t" Distribution - Professor Daniel Martin Katz
1. Quantitative
Methods
for
Lawyers Class #13
Students “t” Distribution
@ computational
computationallegalstudies.com
professor daniel martin katz danielmartinkatz.com
lexpredict.com slideshare.net/DanielKatz
3. Students “T” Distribution
v. Normal Distribution
is then distributed Standard Normal
Let X1, X2,..., Xn be drawn from N ( μ,σ )
We have learned that
But typically - we do not actually know σ
If we know σ than we can use Z Scores
4. Student “T” Distribution is preferred statistic for dealing with
continuous data
Students “T”
Distribution
Sample sizes are sometimes small, and often we do not know
the standard deviation of the population.
When either of these problems occur, statisticians rely on “t”
distribution
5. The t distributions were discovered by William S. Gosset
in 1908.
Students “T”
Distribution
Goal for Gosset: Determine the Likelihood that any
particular sample represented the true quality of the
entire product
Comparing the Mean of Population and
Mean of a Given Sample
6. Gosset was a statistician employed by the Guinness
brewing company which had stipulated that he not
publish under his own name.
He therefore wrote under the pen name “Student.”
Students “T”
Distribution
7. The t distribution should NOT be used with small
samples from populations that are NOT approximately
normal
Students “T”
Distribution
The particular form of the t distribution is determined
by its degrees of freedom
8. Students “T”
Distribution
NOTE: T-Distribution Converges to the Normal Distribution
A Student's t distribution converges to a normal distribution
when the number of degrees of freedom N becomes large
(converges to infinity).
http://www.nku.edu/~longa/stats/taryk/TDist.html
9. Students “T”
Distribution
A Student's t distribution when the N is small
Otherwise, use Normal and “Z Scores”
If the sample is small, n < 30, we use t and if
the sample is large, n ≥ 30, we use z.
What is “Small” in this context?
12. Students “T”
Distribution
Acme Corporation manufactures light bulbs. The CEO
claims that an average Acme light bulb lasts 300 days. A
researcher randomly selects 15 bulbs for testing. The
sampled bulbs last an average of 290 days, with a
standard deviation of 50 days.
If the CEO’s claim were true, what is the probability that
15 randomly selected bulbs would have an average life
of no more than 290 days?
13. Students “T”
Distribution
Acme Corporation manufactures light bulbs. The CEO
claims that an average Acme light bulb lasts 300 days. A
researcher randomly selects 15 bulbs for testing. The
sampled bulbs last an average of 290 days, with a
standard deviation of 50 days.
If the CEO’s claim were true, what is the probability that
15 randomly selected bulbs would have an average life
of no more than 290 days?
This is Single Sample T Test Problem
19. H0: There is No Difference Between the Mean Damage
Award in Bloom County and the Mean Damage Award in
the Rest of the State
Num of Obs. Mean Std. Dev.
GROUP 1
Rest of State
21 $371,621 $289,823
GROUP 2
Bloom County
25 $547,784 $703,314
20. Here is the Data Set With 2 Variables:
Award = Award Amount in Dollars
Bloom = Indicator Variable
( where 1 = award in Bloom County )
( where 0 = award in rest of the State)
There are Various Approaches
You Might Take
You can then load this into
On the Left I Manually Entered
the Data is in Excel
Then you can calculate the two mean test
21.
22. Use an online t-test calculator
http://www.graphpad.com/quickcalcs/ttest1.cfm
23. Daniel Martin Katz
@ computational
computationallegalstudies.com
lexpredict.com
danielmartinkatz.com
illinois tech - chicago kent college of law@