The t-test is used to determine if two numbers are statistically different. There are three main types of t-tests: one-sample, two-sample, and paired. The two-sample t-test examines differences between two independent groups and is calculated using a formula that considers the averages, sample sizes, and standard deviations of each group. A degrees of freedom value and critical value must also be determined. If the absolute value of the calculated t-statistic is greater than the critical value, then the difference between the groups is considered statistically significant.
This presentation educates you about T-Test, Key takeways, Assumptions for Performing a t-test, Types of t-tests, One sample t-test, Independent two-sample t-test and Paired sample t-test.
For more topics Stay tuned with Learnbay
This presentation educates you about T-Test, Key takeways, Assumptions for Performing a t-test, Types of t-tests, One sample t-test, Independent two-sample t-test and Paired sample t-test.
For more topics Stay tuned with Learnbay
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
Through this ppt you could learn what is Wilcoxon Signed Ranked Test. This will teach you the condition and criteria where it can be run and the way to use the test.
T test, Student’s t Test, Key Takeaways, Uses of t-test / Application , Type of t-test, Type of t-test Cont.., One-tailed or two-tailed t-test, Which t-test to Use, t-test Formula, The t-score, Understanding P-values, Degrees of Freedom, How is the t-distribution table used, Example, Example Cont.., Different t-test Formulae, Different t-test Formulae Cont.., Reference.
Assignment 4Chapter 1010.1. In a t test for a single samp.docxssuser562afc1
Assignment 4
Chapter 10
10.1. In a t test for a single sample, the sample's mean is compared to the population .
10.2. When we use a paired-samples t test to compare the pretest and posttest scores for a group of 45 people, the degrees of freedom (df) are _____.
10.3. If we conduct a t test for independent samples, and n1 = 32 and n2 = 35, the degrees of freedom (df) are _____.
10.4. A researcher wants to study the effect of college education on people's earning by comparing the annual salaries of a randomly-selected group of 100 college graduates to the annual salaries of 100 randomly-selected group of people whose highest level of education is high school. To compare the mean annual salaries of the two groups, the researcher should use a t test for ______.
10.5. A training coordinator wants to determine the effectiveness of a program that makes extensive use of educational technology when training new employees. She compares the scores of her new employees who completed the training on a nationally-normed test to the mean score of all those in the country who took the same test. The appropriate statistical test the training coordinator should use for her analysis is the t
test for ______.
10.6. As part of the process to develop two parallel forms of a questionnaire, the persons creating the questionnaire may administer both forms to a group of students, and then use a t test for ______ samples to compare
the mean scores on the two forms.
Circle the correct answer:
10.7. A difference of 4 points between two homogeneous groups is likely to be more/less statistically significant than the same difference (of 4 points) between two heterogeneous groups, when all four groups are taking completing the same survey and have approximately the same number of subjects.
a.
10.8. A difference of 3 points on a 100-item test taken by two groups is likely to be more/less statistically significant than a difference of 3 points on a 30-item test taken by the same two groups.
10.9 When a t test for paired samples is used to compare the pretest and the posttest means, the number of pretest scores is thesame as/different thanthe number of post-test scores.
10.10. When we want to compare whether females' scores on the GMAT are different from males' scores, we should use a t test for paired samples/independentsamples.
10.11 In studies where the alternative (research) hypothesis is directional, the critical values for a one tailedtest/two-tailed testshould be used to determine the level of significance (i.e., the p value).
10.12 When the alternative hypothesis is: HA: u1=u2, the critical values for onetailed test/two-tailedtest should be used to determine the level of statistical significance.
10.13. In a study conducted to compare the test scores of experimental and control groups, a 50-item test is administered to both groups at the end of the study. The mean of the experimental group on the test is 1 point h ...
Assessment 3 – Hypothesis, Effect Size, Power, and t Tests.docxcargillfilberto
Assessment 3 – Hypothesis, Effect Size, Power, and
t
Tests
Complete the following problems within this Word document. Do not submit other files. Show your work for problem sets that require calculations. Ensure that your answer to each problem is clearly visible. You may want to highlight your answer or use a different type color to set it apart.
Hypothesis, Effect Size, and Power
Problem Set 3.1: Sampling Distribution of the Mean Exercise
Criterion:
Interpret population mean and variance.
Instructions:
Read the information below and answer the questions.
Suppose a researcher wants to learn more about the mean attention span of individuals in some hypothetical population. The researcher cites that the attention span (the time in minutes attending to some task) in this population is normally distributed with the following characteristics: 20
36
. Based on the parameters given in this example, answer the following questions:
1. What is the population mean (μ)? __________________________
2. What is the population variance
? __________________________
3. Sketch the distribution of this population. Make sure you draw the shape of the distribution and label the mean plus and minus three standard deviations.
Problem Set 3.2: Effect Size and Power
Criterion:
Explain effect size and power.
Instructions:
Read each of the following three scenarios and answer the questions.
Two researchers make a test concerning the effectiveness of a drug use treatment. Researcher A determines that the effect size in the population of males is
d
= 0.36; Researcher B determines that the effect size in the population of females is
d
= 0.20. All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning the levels of marital satisfaction among military families. Researcher A collects a sample of 22 married couples (
n
= 22); Researcher B collects a sample of 40 married couples (
n
= 40). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Two researchers make a test concerning standardized exam performance among senior high school students in one of two local communities. Researcher A tests performance from the population in the northern community, where the standard deviation of test scores is 110 (
); Researcher B tests performance from the population in the southern community, where the standard deviation of test scores is 60 (
). All other things being equal, which researcher has more power to detect an effect? Explain. ______________________________________________________________________
Problem Set 3.3: Hypothesis, Direction, and Population Mean
Criterion:
Explain the relationship between hypothesis, tests, and population mean.
Instructions:
Read the following and answer the questions.
Inferential Statistics (Melrose High School Library)TomScudder1
A slideshow walkthrough on choosing the correct inferential statistics test for students in AP Research at Melrose High School Library. Includes instructions for using Stapplet to perform a Chi square, Z test, t test, ANOVA, and several other inferential statistical tests.
The slides discuss comparing two means to ascertain which mean is of greater statistical significance. In these slides we will learn about three research questions in which the t-test can be used to analyze the data and compare the means from two independent groups, two paired samples, and a sample and a population.
WEEK 6 – EXERCISES Enter your answers in the spaces pr.docxwendolynhalbert
WEEK 6 – EXERCISES
Enter your answers in the spaces provided. Save the file using your last name as the beginning of the file name (e.g., ruf_week6_exercises) and submit via “Assignments.” When appropriate,
show your work
. You can do the work by hand, scan/take a digital picture, and attach that file with your work.
1
.
A psychotherapist studied whether his clients self-disclosed more while sitting in an easy chair or lying down on a couch. All clients had previously agreed to allow the sessions to be videotaped for research purposes. The therapist randomly assigned 10 clients to each condition. The third session for each client was videotaped and an independent observer counted the clients’ disclosures. The therapist reported that “clients made more disclosures when sitting in easy chairs (
M
= 18.20) than when lying down on a couch (
M
= 14.31),
t
(18) = 2.84,
p
< .05, two-tailed.” Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
2.
A researcher compared the adjustment of adolescents who had been raised in homes that were either very structured or unstructured. Thirty adolescents from each type of family completed an adjustment inventory. The results are reported in the table below. Explain these results to a person who understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Means on Four Adjustment Scales for
Adolescents from Structured versus Unstructured Homes
Scale
Structured Homes
Unstructured Homes
t
Social Maturity
106.82
113.94
–1.07
School Adjustment
116.31
107.22
2.03*
Identity Development
89.48
94.32
1.93*
Intimacy Development
102.25
104.33
.32
______________________
*
p
< .05
3.
Do men with higher levels of a particular hormone show higher levels of assertiveness? Levels of this hormone were tested in 100 men. The top 10 and the bottom 10 were selected for the study. All participants took part in a laboratory simulation in which they were asked to role-play a person picking his car up from a mechanic’s shop. The simulation was videotaped and later judged by independent raters on each of four types of assertive statements made by the participant. The results are shown in the table below. Explain these results to a person who fully understands the
t
test for a single sample but knows nothing about the
t
test for independent means.
Mean Number of Assertive Statements
Type of Assertive Statement
Group
1
2
3
4
Men with High Levels
2.14
1.16
3.83
0.14
Men with Low Levels
1.21
1.32
2.33
0.38
t
3.81**
0.89
2.03*
0.58
______________________
*
p
< .05;
**
p
< 0.1
4.
A manager of a small store wanted to discourage shoplifters by putting signs around the store saying “Shoplifting is a crime!” However, he wanted to make sure this would not result in customers buying less. To test this, he displayed the signs every other W.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. The t-test is a way to determine whether two
numbers are significantly different from one
another. There are several types of t-test, and
each is calculated using a different formula.
3. THE THREE MOST COMMON TYPES OF T-TEST
1. One-sample t-test: This test looks at whether the mean (aka average) of data from one group (in this case
the overall NPS) is different from a value you specify.
Example: Your company’s goal is to have an NPS that’s significantly higher than the industry standard of 5.
Your company’s latest survey puts its NPS at 10. Is an NPS of 10 significantly higher than the industry
standard of 5?
2. Two-sample t-test: This test examines whether the means of two independent groups are significantly
different from one another.
Example: Your hypothesis is that men give your company a lower NPS than women. The average NPS from
male respondents is 9, while the average score from women is 12. Is 9 significantly different from 12?
3. Paired t-test: This test is for when you give one group of people the same survey twice. A paired t-test lets
you know if the mean changed between the first and second survey.
Example: You surveyed the same group of customers twice: once in April and a second time in May, after
they had seen an ad for your company. Did your company’s NPS change after customers saw the ad?
4. HOW TO CONDUCT A T-TEST
1. Calculate the t-statistic:
Each type of t-test has a different formula for calculating the t-statistic (You can scroll to the
bottom of the page to find all three formulas).
2. Calculate the degrees of freedom:
Degrees of freedom are the number of ways the mean could vary. In this case, the degrees of
freedom are the number of NPS ratings that you could have in a given group of respondents.
Similar to the t-statistic, the formula for degrees of freedom will vary depending on the type of
t-test you perform.
3. Determine the critical value:
The critical value is the threshold at which the difference between two numbers is considered
to be statistically significant.
4. Compare absolute value of the t-statistic to critical value:
If your t-statistic is larger than your critical value, your difference is significant. If your t-
statistic is smaller, then your two numbers are, statistically speaking, indistinguishable.
5. CALCULATE T-STATISTIC
Below is the formula for the two-sample t-test, where:
t is the t-statistic
x1 is the average NPS for men → 9
x2 is the average for women → 12
n1 is the number of men who provided a response to the NPS question → say 20 men
responded to the survey
n2 is the number of women → 23 women responded
s1 is the standard deviation of the NPS for men → say the calculated standard deviation is
12.48
s2 is the standard deviation of the NPS for women → the calculated standard deviation is
10.51
6.
7. CALCULATE THE DEGREES OF FREEDOM
This formula must be used to determine degrees of freedom in two-sample t-tests. The
formulas for other types of test are included below.
8. DETERMINE
CRITICAL VALUE
According to this table, for a two-tailed test with an
alpha level of 0.05 at 41 degrees of freedom, the
critical value is 2.02.
Note that most analysts use a two-tailed test instead
of a one-tailed test because it’s more conservative.
9. COMPARE
ABSOLUTE
VALUE OF THE
T-STATISTIC TO
CRITICAL
VALUE:
Since the absolute value of the t-statistic is 0.86,
which is not larger than the critical value of 2.02, then
you can conclude that men do not give a significantly
lower NPS ratings than women.
