Inferantial statistic presentation about two sample t test
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Social Science Statistics STA2122.501 ● ONLINE Project 3
This document provides instructions and materials for a project comparing social values and attitudes between countries using data from the World Values Survey. Students are asked to select two variables - one for grouping countries and one for a social value - perform descriptive statistics and a t-test, and write a report relating their findings to a UN Sustainable Development Goal. Options for analyses comparing views of competition between Japan and the US, views of domestic violence between Sweden and the US, and views of technology between China and the US are provided.
Social Science Statistics STA2122.501 ● ONLINE Project 3.docx
This document provides instructions and materials for a project comparing social values and attitudes between countries using data from the World Values Survey. Students are asked to select two variables - one for a dichotomous grouping (e.g. country) and one for a social value outcome. They then conduct background research on the variable and its relationship to UN Sustainable Development Goals, state a hypothesis comparing the two countries, perform statistical analyses in SPSS including descriptive statistics and an independent samples t-test, make a decision about their hypothesis, and reflect on what the results say about the social issue and further research needed. The document includes a grading rubric, example analyses, and appendices with the variable codebook and SPSS analysis instructions
Episode 12 : Research Methodology ( Part 2 )
1. The document discusses various techniques for quantitative data analysis, including statistical methods like correlation, t-tests, analysis of variance, regression analysis, and econometric modeling.
2. Both descriptive and inferential statistics are covered, with descriptive statistics used to describe or make abstractions about a population based on a sample, while inferential statistics allow inferences to be made about a population based on a sample.
3. Several principles of data analysis are provided, including the need to analyze rather than just narrate data, link the analysis back to research objectives and questions, and ensure findings are supported by data.
ZoonarThinkstockchapter 5The t-TestLearning Objec.docx
Zoonar/Thinkstock
chapter 5
The t-Test
Learning Objectives
After reading this chapter, you will be able to. . .
1. explain the advantage of the one-sample t-test over the z-test.
2. compare the one-sample t-test to the independent t-test.
3. distinguish between one-sample and one-tailed t-tests.
4. explain hypothesis testing in statistical analysis.
5. determine practical significance.
6. construct a confidence interval for the difference between the means.
7. discuss research applications for the t-tests.
8. present results of a t-test and draw conclusions based on hypotheses.
9. interpret t-test results and report in APA format.
10. discuss nonparametric Mann-Whitney U-test compared to the t-test.
CN
CO_LO
CO_TX
CO_NL
CT
CO_CRD
suk85842_05_c05.indd 137 10/23/13 1:17 PM
CHAPTER 5Section 5.1 Estimating the Standard Error of the Mean
The z-test (Chapter 4) is more than just an expansion of the z score to groups because it introduces statistical significance. Those who work with quantitative data need to
be able to distinguish between outcomes that probably occurred by chance and those that
are likely to emerge each time the data is gathered and analyzed. When data indicates
that a group of clients, each grieving over the loss of a loved one, becomes more positive
and peaceful with time, the therapist needs to know whether this would have happened
anyway with the passage of time, or whether it has something to do with the treatment
the therapist provided. The z-test answers such questions.
The z-test has important limitations. The greatest difficulty is the need to have a value
for the population standard error of the mean, sM. It is not the type of information that
tends to come up as a matter of course, and it can be fairly difficult to calculate when the
researcher does not have access to population data.
The second limitation is that the z-test allows only one type of comparison: a sample to a
population. What if two therapists want to compare their respective groups of grieving cli-
ents to see if one type of therapy for grief counseling is better than the other? The z-test pro-
vides nowhere to go with such questions, which is where William Sealy Gosset comes in.
Gosset worked for Guinness Brewing during the early part of the 20th century. Part of
his responsibility was quality control, and he studied ways to make sure that day-to-
day brewing remained consistent with Guinness’s standards. A man of remarkable ability,
Gosset devised procedures for quantifying product quality and then testing the consis-
tency of the quality over time. To this end, he developed t-tests.
Gosset had some sense of how important the t-tests were and wanted to publish so that
others could benefit. The roadblock was a no-publishing policy at Guinness, a policy that
was begun after one of Gosset’s predecessors published what the Guinness people consid-
ered trade secrets. Although he felt that Guinness wo ...
2. Using our sample data, construct a 95 confidence interval for th.pdf
A 95% confidence interval for the mean salary difference between genders in the sample data is calculated as $4.77 to $25.35. A two-sample t-test indicates the means are not equal, as the p-value is 0.005. Using a two-sample t-test is better than two one-sample tests when comparing samples, as it directly tests if a difference exists between the sample means rather than just providing individual confidence intervals.
T test for two independent samples and induction
Recruitment and selection is important to find people who are a good fit for the organization to reduce costs from high turnover. A good recruitment system should be efficient, effective at finding suitable candidates, and fair by being non-discriminatory. Employers should do human resource planning to forecast needs and match available supply to demand to help with recruitment and development.
Episode 18 : Research Methodology ( Part 8 )
Episode 18 : Research Methodology ( Part 8 )
Approach to de-synthesizing data, informational, and/or factual elements to answer research questions
Method of putting together facts and figures
to solve research problem
Systematic process of utilizing data to address research questions
Breaking down research issues through utilizing controlled data and factual information
SAJJAD KHUDHUR ABBAS
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
IHP 525 Milestone Five (Final) TemplateMOST OF THIS TEMPLATE S.docx
IHP 525 Milestone Five (Final) Template
MOST OF THIS TEMPLATE SHOULD BE COPIED AND PASTED FROM PRIOR MILESTONES IF YOU RECEIVED FULL CREDIT FOR THOSE ELEMENTS.
DO NOT DELETE ANYTHING IN THIS TEMPLATE.
Student Name:
State the question you will pursue. (This should be copied and pasted from the list of questions and should be the same question submitted in week 7 unless you have changed your question.)
Question of interest (Copy and Paste question here):
Restate this question in your own words:
Directions for following table:
· Fill out the table below for EACH variable of interest.
· Include ONLY the variables that are relevant to your question of interest. (If it’s not mentioned in your question directly, it’s not relevant.)
· Each variable should take up ONE row.
Variable name (one variable per row)
Note: gender is a variable, and 0 and 1 are values of the variable gender. Gender =0 and gender =1 are NOT two separate variables.
Variable type (categorical, ordinal, or quantitative, etc.)
Descriptive statistics
Include:
· the statistic names (the mean, median, range, and standard deviation, at minimum)
· final calculations (e.g., mean = 10)
· an explanation/definition of each statistic used (what each statistic SAYS about the data)
*Do not subdivide the data for the variable in each row based on any other variable. For example, do NOT find the mean length of stay separately for males and females.
**The above statistics can be found for binary variables. (For example, gender is coded as binary; therefore, the above descriptive statistics can be found for the variable gender.)
Key features
· Histogram symmetric?
· Histogram bell shaped?
· Any outliers?
· Skew?
· Unimodal?
· Any other special features?
DO NOT list or discuss descriptive statistics in this space. Use the table above, as directed.
Analyze the limitations of the data set you were provided and how those limitations might affect your findings.
Limit your response to the data relevant to your question of interest. (For example, only using two variables is NOT a limitation of the data in your question of interest. It may be a limitation of the study or question of interest, but it is NOT a limitation of the data you have been provided for your question of interest.)
Limitations:
Provide ONE graph that is useful in explaining your results.
You may copy and paste this from another program, take a screen shot, etc.
LABEL EVERYTHING!!!
Explain why you chose this graph above any others to explain the situation.
What test/analysis technique did you perform?
(It is highly recommended that you perform ONLY ONE test or technique. Some examples include a t-test, regression, etc.)
There is a hypothesis test associated with your test/technique (even if you are not doing a t-test).
What is your null hypothesis?
What is your alternative hypothesis?
Provide all relevant calculations for your hypothesis test/ statistical technique.
Make sure your final ans.
Recommended
Social Science Statistics STA2122.501 ● ONLINE Project 3
This document provides instructions and materials for a project comparing social values and attitudes between countries using data from the World Values Survey. Students are asked to select two variables - one for grouping countries and one for a social value - perform descriptive statistics and a t-test, and write a report relating their findings to a UN Sustainable Development Goal. Options for analyses comparing views of competition between Japan and the US, views of domestic violence between Sweden and the US, and views of technology between China and the US are provided.
Social Science Statistics STA2122.501 ● ONLINE Project 3.docx
This document provides instructions and materials for a project comparing social values and attitudes between countries using data from the World Values Survey. Students are asked to select two variables - one for a dichotomous grouping (e.g. country) and one for a social value outcome. They then conduct background research on the variable and its relationship to UN Sustainable Development Goals, state a hypothesis comparing the two countries, perform statistical analyses in SPSS including descriptive statistics and an independent samples t-test, make a decision about their hypothesis, and reflect on what the results say about the social issue and further research needed. The document includes a grading rubric, example analyses, and appendices with the variable codebook and SPSS analysis instructions
Episode 12 : Research Methodology ( Part 2 )
1. The document discusses various techniques for quantitative data analysis, including statistical methods like correlation, t-tests, analysis of variance, regression analysis, and econometric modeling.
2. Both descriptive and inferential statistics are covered, with descriptive statistics used to describe or make abstractions about a population based on a sample, while inferential statistics allow inferences to be made about a population based on a sample.
3. Several principles of data analysis are provided, including the need to analyze rather than just narrate data, link the analysis back to research objectives and questions, and ensure findings are supported by data.
ZoonarThinkstockchapter 5The t-TestLearning Objec.docx
Zoonar/Thinkstock
chapter 5
The t-Test
Learning Objectives
After reading this chapter, you will be able to. . .
1. explain the advantage of the one-sample t-test over the z-test.
2. compare the one-sample t-test to the independent t-test.
3. distinguish between one-sample and one-tailed t-tests.
4. explain hypothesis testing in statistical analysis.
5. determine practical significance.
6. construct a confidence interval for the difference between the means.
7. discuss research applications for the t-tests.
8. present results of a t-test and draw conclusions based on hypotheses.
9. interpret t-test results and report in APA format.
10. discuss nonparametric Mann-Whitney U-test compared to the t-test.
CN
CO_LO
CO_TX
CO_NL
CT
CO_CRD
suk85842_05_c05.indd 137 10/23/13 1:17 PM
CHAPTER 5Section 5.1 Estimating the Standard Error of the Mean
The z-test (Chapter 4) is more than just an expansion of the z score to groups because it introduces statistical significance. Those who work with quantitative data need to
be able to distinguish between outcomes that probably occurred by chance and those that
are likely to emerge each time the data is gathered and analyzed. When data indicates
that a group of clients, each grieving over the loss of a loved one, becomes more positive
and peaceful with time, the therapist needs to know whether this would have happened
anyway with the passage of time, or whether it has something to do with the treatment
the therapist provided. The z-test answers such questions.
The z-test has important limitations. The greatest difficulty is the need to have a value
for the population standard error of the mean, sM. It is not the type of information that
tends to come up as a matter of course, and it can be fairly difficult to calculate when the
researcher does not have access to population data.
The second limitation is that the z-test allows only one type of comparison: a sample to a
population. What if two therapists want to compare their respective groups of grieving cli-
ents to see if one type of therapy for grief counseling is better than the other? The z-test pro-
vides nowhere to go with such questions, which is where William Sealy Gosset comes in.
Gosset worked for Guinness Brewing during the early part of the 20th century. Part of
his responsibility was quality control, and he studied ways to make sure that day-to-
day brewing remained consistent with Guinness’s standards. A man of remarkable ability,
Gosset devised procedures for quantifying product quality and then testing the consis-
tency of the quality over time. To this end, he developed t-tests.
Gosset had some sense of how important the t-tests were and wanted to publish so that
others could benefit. The roadblock was a no-publishing policy at Guinness, a policy that
was begun after one of Gosset’s predecessors published what the Guinness people consid-
ered trade secrets. Although he felt that Guinness wo ...
2. Using our sample data, construct a 95 confidence interval for th.pdf
A 95% confidence interval for the mean salary difference between genders in the sample data is calculated as $4.77 to $25.35. A two-sample t-test indicates the means are not equal, as the p-value is 0.005. Using a two-sample t-test is better than two one-sample tests when comparing samples, as it directly tests if a difference exists between the sample means rather than just providing individual confidence intervals.
T test for two independent samples and induction
Recruitment and selection is important to find people who are a good fit for the organization to reduce costs from high turnover. A good recruitment system should be efficient, effective at finding suitable candidates, and fair by being non-discriminatory. Employers should do human resource planning to forecast needs and match available supply to demand to help with recruitment and development.
Episode 18 : Research Methodology ( Part 8 )
Episode 18 : Research Methodology ( Part 8 )
Approach to de-synthesizing data, informational, and/or factual elements to answer research questions
Method of putting together facts and figures
to solve research problem
Systematic process of utilizing data to address research questions
Breaking down research issues through utilizing controlled data and factual information
SAJJAD KHUDHUR ABBAS
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
IHP 525 Milestone Five (Final) TemplateMOST OF THIS TEMPLATE S.docx
IHP 525 Milestone Five (Final) Template
MOST OF THIS TEMPLATE SHOULD BE COPIED AND PASTED FROM PRIOR MILESTONES IF YOU RECEIVED FULL CREDIT FOR THOSE ELEMENTS.
DO NOT DELETE ANYTHING IN THIS TEMPLATE.
Student Name:
State the question you will pursue. (This should be copied and pasted from the list of questions and should be the same question submitted in week 7 unless you have changed your question.)
Question of interest (Copy and Paste question here):
Restate this question in your own words:
Directions for following table:
· Fill out the table below for EACH variable of interest.
· Include ONLY the variables that are relevant to your question of interest. (If it’s not mentioned in your question directly, it’s not relevant.)
· Each variable should take up ONE row.
Variable name (one variable per row)
Note: gender is a variable, and 0 and 1 are values of the variable gender. Gender =0 and gender =1 are NOT two separate variables.
Variable type (categorical, ordinal, or quantitative, etc.)
Descriptive statistics
Include:
· the statistic names (the mean, median, range, and standard deviation, at minimum)
· final calculations (e.g., mean = 10)
· an explanation/definition of each statistic used (what each statistic SAYS about the data)
*Do not subdivide the data for the variable in each row based on any other variable. For example, do NOT find the mean length of stay separately for males and females.
**The above statistics can be found for binary variables. (For example, gender is coded as binary; therefore, the above descriptive statistics can be found for the variable gender.)
Key features
· Histogram symmetric?
· Histogram bell shaped?
· Any outliers?
· Skew?
· Unimodal?
· Any other special features?
DO NOT list or discuss descriptive statistics in this space. Use the table above, as directed.
Analyze the limitations of the data set you were provided and how those limitations might affect your findings.
Limit your response to the data relevant to your question of interest. (For example, only using two variables is NOT a limitation of the data in your question of interest. It may be a limitation of the study or question of interest, but it is NOT a limitation of the data you have been provided for your question of interest.)
Limitations:
Provide ONE graph that is useful in explaining your results.
You may copy and paste this from another program, take a screen shot, etc.
LABEL EVERYTHING!!!
Explain why you chose this graph above any others to explain the situation.
What test/analysis technique did you perform?
(It is highly recommended that you perform ONLY ONE test or technique. Some examples include a t-test, regression, etc.)
There is a hypothesis test associated with your test/technique (even if you are not doing a t-test).
What is your null hypothesis?
What is your alternative hypothesis?
Provide all relevant calculations for your hypothesis test/ statistical technique.
Make sure your final ans.
T-Test
The document discusses the t-test, which is a statistical method used to determine if there is a significant difference between the means of two groups. It can be used to compare the means of two independent groups, related groups, or a group's mean to a hypothesized population mean. There are assumptions that must be met for a t-test, including independent observations, normal distribution of data, and homogeneity of variances. The t-test calculates a t-score or t-value which is compared to a critical value to determine if the null hypothesis can be rejected.
Chapter 5 t-test
This chapter introduces the t-test, a statistical tool used to test for significant differences between the means of two groups. It discusses the independent t-test for comparing means of two independent samples, and the dependent or paired t-test for comparing means of the same sample tested twice. The chapter covers the assumptions of the t-test, how to formulate hypotheses, compute t-values, and interpret results based on critical values. Key aspects include the formula for computing the t-statistic and standard error, conducting significance tests using t-distribution tables, and checking assumptions such as normality and homogeneity of variance.
122416, 11(23 AMModule 8 Mastery Exercise SchoologyPa.docx
12/24/16, 11(23 AMModule 8: Mastery Exercise | Schoology
Page 1 of 3https://app.schoology.com/assignment/885059160/assessment
Statistics and SPSS: WINTER16-B-8-MIS445-1
Module 8: Mastery Exercise
Question 1 (1 point)
In a body weight loss trial, the calculated F-value was 5.91 and the tabulated F (0.95, 3, 16) = 3.2; what should be the conclusion?
a Since F-calculated, 5.91 is bigger than F-tabulated, 3.2, therefore, reject the null hypothesis that dietary treatments were
similar in reducing body weight.
b Accept the null hypothesis of no dietary treatments effects.
c Nothing can be calculated.
Question 2 (1 point)
Some of the assumptions, for the data used in ANOVA are _________.
a data follows a normal distribution
b population means have similar variance (or standard deviation)
c samples are randomly selected and independent of one another
d all of the above
Question 3 (1 point)
Researchers wish to examine the effectiveness of a new weight-loss pill. A total of 200 obese adults are randomly assigned to one of
four conditions: weight-loss pill alone, weight-loss pill with a low-fat diet, placebo pill alone, or placebo pill with a low-fat diet. The
weight loss after six months of treatment is recorded in pounds for each subject. To analyze this data, you would use __________.
a a z-test
b a t-test
c an ANOVA F test
d a Chi-square test
Question 4 (1 point)
A medical research team is interested in determining whether a new drug has an effect on creatine kinase (CK), which is often assayed in
blood tests as an indicator of myocardial infarction. A random selection of 20 patients from a pool of possible subjects is selected, and
each subject is given the medication. The subjects’ CK levels are observed initially, after three (3) weeks, and again after six (6) weeks.
The purpose is to study the CK levels over time. Here is a summary of the findings:
Time (weeks) Mean CK level (U/L) Standard devia9on (U/L)
0 121 20.37
3 106 16.09
6 100 10.21
In this example, we notice that ____________.
a the data shows very strong evidence of a violation of the assumption that the three populations have the same standard
deviation
Questions 1-10 of 10 | Page 1 of 1
https://app.schoology.com/course/885058852
12/24/16, 11(23 AMModule 8: Mastery Exercise | Schoology
Page 2 of 3https://app.schoology.com/assignment/885059160/assessment
b ANOVA cannot be used on this data because the sample sizes are much too small
c the assumption that the data is independent for the three time points is unreasonable because the same subjects were
observed each time
d there is no reason not to use ANOVA in this situation
Question 5 (1 point)
The degree of freedom for the total number of observations in ANOVA will be ________.
a total number of observations less one
b total number of observations less two
c total number of observations plus one
d total number of observations plus two
Question 6 (1 point)
How much corn should be plante ...
The following calendar-year information is taken from the December.docx
The following calendar-year information is taken from the December 31, 2011, adjusted trial balance and other records of Azalea Company.
1. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Materials used.
b. Factory overhead.
c. Total manufacturing costs.
d. Total cost of goods in process.
e. Cost of goods manufactured.
2. Check your cost of goods manufactured with the instructor. If it is correct, proceed to part (3).
3. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Net sales.
b. Cost of goods sold.
c. Gross profit.
d. Total operating expenses.
e. Net income or loss before taxes.
CALCULATE T TEST
Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
12
2
~
NN
DM
MM
tt
S
+-
-
=
Where
22
1122
1212
(1)(1)
11
2
DM
NsNs
S
NNNN
éùéù
-+-
=+
êúêú
+-
ëûëû
Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
(
)
(
)
22
(101)9330.95(101)6912.95
11
101021010
DM
S
éù
-+-
éù
=+
êú
êú
+-
ëû
êú
ëû
= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
0
.
4
0
.
3
0
.
2
0
.
1
0
.
0
X
D
e
n
s
i
t
y
-
2
.
1
0
0
.
0
2
5
2
.
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0
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.
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0
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i
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i
b
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t
i
o
n
P
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o
t
T
,
d
f
=
1
8
Conclusion: Fails to reject the null hypothesis. The sample does not provide enough evidence to support the claim that there is significant difference ...
T‑tests
Commonly Used t‑tests in Medical Research
R. M. Pandey
Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
Two dependent samples (matched pairs)
This document discusses testing differences between two dependent samples using matched pairs. It provides examples of how to:
1) Calculate the differences between matched pairs and find the mean and standard deviation of the differences.
2) Use a t-test to determine if the mean difference is statistically significant and construct a 90% confidence interval for the true mean difference between two dependent samples.
3) Apply these methods to an example comparing cholesterol levels before and after a mineral supplement, testing the claim that the supplement changes cholesterol levels.
T_test_of_dependent_means (2).ppt
This document provides information about performing a paired t-test, including the steps and an example. A paired t-test is used to compare two related samples, such as pre-test and post-test scores from the same group. The steps are: formulate hypotheses, determine if the test is one-tailed or two-tailed, calculate the degrees of freedom, select the t-test, compute the test statistic, state the decision rule based on critical value, and make a conclusion. An example tests if a new diet significantly reduces weight by comparing weights before and after for 7 women.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
A study compared eating disorder symptoms between 297 Australian and 249 Indian university students using the Eating Attitudes Test and Goldfarb Fear of Fat Scale. Indian students scored higher on both tests, especially women. Statistical analysis found the differences were highly significant (p < .0001) between the groups. However, the small effect size (-0.14) suggests the actual magnitude of the difference between memory technique groups was likely small.
Spss2 comparing means_two_groups
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Innovative sample size methods for adaptive clinical trials webinar web ver...
View the video here:
https://www.statsols.com/webinar/innovative-sample-size-methods-for-adaptive-clinical-trials
Given the high failure rates and the increased costs of clinical trials, researchers need innovative design strategies to best optimize financial resources and reduce the risk to patients.
Adaptive designs are emerging as a way to reduce risk and cost associated with clinical trials. The FDA recently published guidance (Innovative Cures Act) and are actively encouraging sponsors to use Adaptive trials.
Adaptive design is a clinical trial design that allows adaptations or modifications to aspects of the trial after its initiation without undermining the validity and integrity of the trial.
In this webinar, Ronan will demonstrate nQuery's new Adaptive module focusing on Sample Size Re-Estimation & Group-Sequential Design.
In this webinar you will learn about:
The pros and cons of adaptive designs
Sample Size Re-Estimation
Group-Sequential Design
Conditional Power
Predictive Power
Prueba z en ingles
This document defines and explains how to use the Z test, a statistical test used to determine if there are significant differences between population or sample proportions or means. It outlines the assumptions, factors, and applications of the Z test, provides an example problem, and concludes that the Z test is useful for biomedical analysis due to its ability to provide concrete numbers and conclusions from variable data in samples of people or groups.
البحث العلمي المنشور بين الجيد والرديء
The document discusses a lecture titled "The difference between good and poor scientific research publication" given at the Research Center for Medical and Health Studies at King Saud University. It provides contact information for the lecturer, Dr. Abdulmohsen Al-Aqeil, an associate professor in the Department of Clinical Pharmacy at the College of Pharmacy. The document announces voting for the lecture using an online platform and thanks the audience.
Two Means, Two Dependent Samples, Matched Pairs
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http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.3 Two Means, Two Dependent Samples, Matched Pairs
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
NAD Vs MapReduce
This document discusses a method for splitting large medical data sets based on the normal distribution in a cloud computing environment. The key points are:
- Large medical and e-commerce data sets present challenges for data mining due to their size and generation velocity. Existing splitting methods like UV decomposition do not scale well for very large data sets.
- The proposed method splits large data sets into smaller subsets based on identifying groups of data that approximate a normal distribution. These normal distribution (ND) subsets can then be analyzed individually while still representing the overall data set.
- The ND subsets are well-suited for distributed processing in a cloud computing environment, as each subset can be analyzed locally and in parallel. Experimental results show
Assigning Scores For Ordered Categorical Responses
This document summarizes a research article that proposes a new method for assigning scores to ordered categorical response variables in statistical analysis. Specifically, it discusses the ordered stereotype model, which allows for uneven spacing between categories of an ordinal variable through estimated score parameters. The article presents simulation studies showing the disadvantages of assuming equal spacing, and applies the ordered stereotype model to a real dataset, demonstrating non-equal spacing. It also proposes a new median measure for ordinal data based on estimated score parameters from the ordered stereotype model.
Two Means Independent Samples
This document discusses hypothesis testing and constructing confidence intervals for comparing two means from independent populations. It provides:
1. Requirements for using a z-test or t-test to compare two means, including that the samples must be independent and randomly selected, and meet certain size or normality criteria.
2. Formulas and steps for conducting a z-test when population variances are known, and a t-test when they are unknown, to test claims about differences in population means.
3. Instructions for using a calculator to perform two-sample z-tests, t-tests, and to construct confidence intervals for the difference between two means.
4. An example comparing hotel room rates using
Standing mat-study-texas-am-university-2013
The study evaluated 4 anti-fatigue mats (Mats A-D) and a no-mat control for their effects on worker height loss and flexibility changes over an 8-hour workday. Eighteen subjects were recruited from two industrial sites and used each mat/control for 5 days. Height and flexibility measurements were taken before and after each workday. Statistical analysis found that only Mat A (Let's Gel) produced significantly less height loss and greater flexibility increases compared to the no-mat control. The other mats were not significantly different than the control for either measurement. Mat A appears to be the most effective mat at reducing spinal compression and maintaining flexibility over an 8-hour workday.
Wooldridge (2011), Difference in difference estimation.pdf
This document discusses difference-in-differences (DD) estimation methods. It begins by outlining the basic DD methodology using two groups and two time periods. It then discusses extensions such as using multiple groups, time periods, and data sources. The document also covers issues like uncertainty estimation and the use of DD with a small number of groups. Overall, it provides an overview of DD estimation techniques and considerations for their application.
一比一原版(CU毕业证)卡尔顿大学毕业证如何办理
原件一模一样【微信：95270640】【卡尔顿大学毕业证CU学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔顿大学毕业证CU学位证本科学位证成绩单【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
原版办【微信号:BYZS866】【兰加拉学院毕业证(Langara毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
More Related Content
Similar to Inferantial statistic presentation about two sample t test
T-Test
The document discusses the t-test, which is a statistical method used to determine if there is a significant difference between the means of two groups. It can be used to compare the means of two independent groups, related groups, or a group's mean to a hypothesized population mean. There are assumptions that must be met for a t-test, including independent observations, normal distribution of data, and homogeneity of variances. The t-test calculates a t-score or t-value which is compared to a critical value to determine if the null hypothesis can be rejected.
Chapter 5 t-test
This chapter introduces the t-test, a statistical tool used to test for significant differences between the means of two groups. It discusses the independent t-test for comparing means of two independent samples, and the dependent or paired t-test for comparing means of the same sample tested twice. The chapter covers the assumptions of the t-test, how to formulate hypotheses, compute t-values, and interpret results based on critical values. Key aspects include the formula for computing the t-statistic and standard error, conducting significance tests using t-distribution tables, and checking assumptions such as normality and homogeneity of variance.
122416, 11(23 AMModule 8 Mastery Exercise SchoologyPa.docx
12/24/16, 11(23 AMModule 8: Mastery Exercise | Schoology
Page 1 of 3https://app.schoology.com/assignment/885059160/assessment
Statistics and SPSS: WINTER16-B-8-MIS445-1
Module 8: Mastery Exercise
Question 1 (1 point)
In a body weight loss trial, the calculated F-value was 5.91 and the tabulated F (0.95, 3, 16) = 3.2; what should be the conclusion?
a Since F-calculated, 5.91 is bigger than F-tabulated, 3.2, therefore, reject the null hypothesis that dietary treatments were
similar in reducing body weight.
b Accept the null hypothesis of no dietary treatments effects.
c Nothing can be calculated.
Question 2 (1 point)
Some of the assumptions, for the data used in ANOVA are _________.
a data follows a normal distribution
b population means have similar variance (or standard deviation)
c samples are randomly selected and independent of one another
d all of the above
Question 3 (1 point)
Researchers wish to examine the effectiveness of a new weight-loss pill. A total of 200 obese adults are randomly assigned to one of
four conditions: weight-loss pill alone, weight-loss pill with a low-fat diet, placebo pill alone, or placebo pill with a low-fat diet. The
weight loss after six months of treatment is recorded in pounds for each subject. To analyze this data, you would use __________.
a a z-test
b a t-test
c an ANOVA F test
d a Chi-square test
Question 4 (1 point)
A medical research team is interested in determining whether a new drug has an effect on creatine kinase (CK), which is often assayed in
blood tests as an indicator of myocardial infarction. A random selection of 20 patients from a pool of possible subjects is selected, and
each subject is given the medication. The subjects’ CK levels are observed initially, after three (3) weeks, and again after six (6) weeks.
The purpose is to study the CK levels over time. Here is a summary of the findings:
Time (weeks) Mean CK level (U/L) Standard devia9on (U/L)
0 121 20.37
3 106 16.09
6 100 10.21
In this example, we notice that ____________.
a the data shows very strong evidence of a violation of the assumption that the three populations have the same standard
deviation
Questions 1-10 of 10 | Page 1 of 1
https://app.schoology.com/course/885058852
12/24/16, 11(23 AMModule 8: Mastery Exercise | Schoology
Page 2 of 3https://app.schoology.com/assignment/885059160/assessment
b ANOVA cannot be used on this data because the sample sizes are much too small
c the assumption that the data is independent for the three time points is unreasonable because the same subjects were
observed each time
d there is no reason not to use ANOVA in this situation
Question 5 (1 point)
The degree of freedom for the total number of observations in ANOVA will be ________.
a total number of observations less one
b total number of observations less two
c total number of observations plus one
d total number of observations plus two
Question 6 (1 point)
How much corn should be plante ...
The following calendar-year information is taken from the December.docx
The following calendar-year information is taken from the December 31, 2011, adjusted trial balance and other records of Azalea Company.
1. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Materials used.
b. Factory overhead.
c. Total manufacturing costs.
d. Total cost of goods in process.
e. Cost of goods manufactured.
2. Check your cost of goods manufactured with the instructor. If it is correct, proceed to part (3).
3. Each team member is to be responsible for computing one of the following amounts. You are not to duplicate your teammates' work. Get any necessary amounts from teammates. Each member is to explain the computation to the team in preparation for reporting to class.
a. Net sales.
b. Cost of goods sold.
c. Gross profit.
d. Total operating expenses.
e. Net income or loss before taxes.
CALCULATE T TEST
Calculate the “t” value for independent groups for the following data using the formula provided in the attached word document. Using the raw measurement data presented, determine whether or not there exists a statistically significant difference between the salaries of female and male human resource managers using the appropriate t-test. Develop a testable hypothesis, confidence level, and degrees of freedom. Report the required “t” critical values based on the degrees of freedom. Show calculations.
Answer
The null hypothesis tested is
H0: There is no significant difference between the average salaries of female and male human resource managers. (µ1= µ2)
The alternative hypothesis is
H1: There is significant difference between the average salaries of female and male human resource managers. (µ1≠ µ2)
The test statistic used is
12
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2
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NN
DM
MM
tt
S
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Where
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S
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Here M1 = 62,200, M2 = 63,700
s1 = 9330.95, s2 = 6912.95
N1 = 10, N2 = 10 (See the excel sheet)
Then,
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22
(101)9330.95(101)6912.95
11
101021010
DM
S
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= 3672.267768
Therefore test statistic,
62,20063,700
3672.267768
t
-
=
= -0.408466946
Degrees of freedom = N1 + N2 – 2 = 10 + 10 – 2 = 18
Let the significance level be 0.05.
Rejection criteria: Reject the null hypothesis, if the calculated value of t is greater than the critical value of t at 0.05 significance level.
The critical values can be obtained from the student’s t tables with 18 d.f. at 0.05 significance level.
Upper critical value = 2.1
Lower critical value = -2.1
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Conclusion: Fails to reject the null hypothesis. The sample does not provide enough evidence to support the claim that there is significant difference ...
T‑tests
Commonly Used t‑tests in Medical Research
R. M. Pandey
Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
Two dependent samples (matched pairs)
This document discusses testing differences between two dependent samples using matched pairs. It provides examples of how to:
1) Calculate the differences between matched pairs and find the mean and standard deviation of the differences.
2) Use a t-test to determine if the mean difference is statistically significant and construct a 90% confidence interval for the true mean difference between two dependent samples.
3) Apply these methods to an example comparing cholesterol levels before and after a mineral supplement, testing the claim that the supplement changes cholesterol levels.
T_test_of_dependent_means (2).ppt
This document provides information about performing a paired t-test, including the steps and an example. A paired t-test is used to compare two related samples, such as pre-test and post-test scores from the same group. The steps are: formulate hypotheses, determine if the test is one-tailed or two-tailed, calculate the degrees of freedom, select the t-test, compute the test statistic, state the decision rule based on critical value, and make a conclusion. An example tests if a new diet significantly reduces weight by comparing weights before and after for 7 women.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS
11: MEDIA LIBRARY
Premium Videos
Core Concepts in Stats Video
· Testing the Difference Between Two Sample Means
Lightboard Lecture Video
· Independent t Tests
Time to Practice Video
· Chapter 11: Problem 5
Difficulty Scale
(A little longer than the previous chapter but basically the same kind of procedures and very similar questions. Not too hard, but you have to pay attention.)
WHAT YOU WILL LEARN IN THIS CHAPTER
· Using the t test for independent means when appropriate
· Computing the observed t value
· Interpreting the t value and understanding what it means
· Computing the effect size for a t test for independent means
INTRODUCTION TO THE T TEST FOR INDEPENDENT SAMPLES
Even though eating disorders are recognized for their seriousness, little research has been done that compares the prevalence and intensity of symptoms across different cultures. John P. Sjostedt, John F. Schumaker, and S. S. Nathawat undertook this comparison with groups of 297 Australian and 249 Indian university students. Each student was measured on the Eating Attitudes Test and the Goldfarb Fear of Fat Scale. High scores on both measures indicate the presence of an eating disorder. The groups’ scores were compared with one another. On a comparison of means between the Indian and the Australian participants, Indian students scored higher on both of the tests, and this was due mainly to the scores of women. The results for the Eating Attitudes Test were t(544) = −4.19, p < .0001, and the results for the Goldfarb Fear of Fat Scale were t(544) = −7.64, p < .0001.
Now just what does all this mean? Read on.
Why was the t test for independent means used? Sjostedt and his colleagues were interested in finding out whether there was a difference in the average scores of one (or more) variable(s) between the two groups. The t test is called independent because the two groups were not related in any way. Each participant in the study was tested only once. The researchers applied a t test for independent means, arriving at the conclusion that for each of the outcome variables, the differences between the two groups were significant at or beyond the .0001 level. Such a small chance of a Type I error means that there is very little probability that the difference in scores between the two groups was due to chance and not something like group membership, in this case representing nationality, culture, or ethnicity.
Want to know more? Go online or to the library and find …
Sjostedt, J. P., Schumaker, J. F., & Nathawat, S. S. (1998). Eating disorders among Indian and Australian university students. Journal of Social Psychology, 138(3), 351–357.
LIGHTBOARD LECTURE VIDEO
Independent t Tests
THE PATH TO WISDOM AND KNOWLEDGE
Here’s how you can use Figure 11.1, the flowchart introduced in Chapter 9, to select the appropriate test statistic, the t test for independent means. Follow along the highlighted sequence of steps in Figure 1.
11 T(EA) FOR TWO TESTS BETWEEN THE MEANS OF DIFFERENT GROUPS11 .docx
A study compared eating disorder symptoms between 297 Australian and 249 Indian university students using the Eating Attitudes Test and Goldfarb Fear of Fat Scale. Indian students scored higher on both tests, especially women. Statistical analysis found the differences were highly significant (p < .0001) between the groups. However, the small effect size (-0.14) suggests the actual magnitude of the difference between memory technique groups was likely small.
Spss2 comparing means_two_groups
This document discusses different types of t-tests used to compare means: one sample t-tests, independent samples t-tests, and paired samples t-tests. It provides examples and steps for conducting each type of t-test in SPSS. Key points include that one sample t-tests compare a sample mean to a known value, independent samples t-tests compare means between two unrelated groups, and paired samples t-tests compare means within the same group across two time points or conditions. The document also outlines assumptions, how to interpret output and p-values, and how to report results for each t-test. Three cases are presented to demonstrate application of each t-test type.
Innovative sample size methods for adaptive clinical trials webinar web ver...
View the video here:
https://www.statsols.com/webinar/innovative-sample-size-methods-for-adaptive-clinical-trials
Given the high failure rates and the increased costs of clinical trials, researchers need innovative design strategies to best optimize financial resources and reduce the risk to patients.
Adaptive designs are emerging as a way to reduce risk and cost associated with clinical trials. The FDA recently published guidance (Innovative Cures Act) and are actively encouraging sponsors to use Adaptive trials.
Adaptive design is a clinical trial design that allows adaptations or modifications to aspects of the trial after its initiation without undermining the validity and integrity of the trial.
In this webinar, Ronan will demonstrate nQuery's new Adaptive module focusing on Sample Size Re-Estimation & Group-Sequential Design.
In this webinar you will learn about:
The pros and cons of adaptive designs
Sample Size Re-Estimation
Group-Sequential Design
Conditional Power
Predictive Power
Prueba z en ingles
This document defines and explains how to use the Z test, a statistical test used to determine if there are significant differences between population or sample proportions or means. It outlines the assumptions, factors, and applications of the Z test, provides an example problem, and concludes that the Z test is useful for biomedical analysis due to its ability to provide concrete numbers and conclusions from variable data in samples of people or groups.
البحث العلمي المنشور بين الجيد والرديء
The document discusses a lecture titled "The difference between good and poor scientific research publication" given at the Research Center for Medical and Health Studies at King Saud University. It provides contact information for the lecturer, Dr. Abdulmohsen Al-Aqeil, an associate professor in the Department of Clinical Pharmacy at the College of Pharmacy. The document announces voting for the lecture using an online platform and thanks the audience.
Two Means, Two Dependent Samples, Matched Pairs
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
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一比一原版(CU毕业证)卡尔顿大学毕业证如何办理
原件一模一样【微信：95270640】【卡尔顿大学毕业证CU学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
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6:个人职称评审加20分
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8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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一比一原版兰加拉学院毕业证(Langara毕业证书)学历如何办理
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【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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https://www.youtube.com/@FLaNK-Stack
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Media monitoring in veterinary medicien
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版制作【微信:41543339】【利兹贝克特大学毕业证(LeedsBeckett毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版制作【微信:41543339】【多伦多大学毕业证(UofT毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
原版办【微信号:BYZS866】【英属哥伦比亚大学毕业证(UBC毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
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一比一原版(harvard毕业证书）哈佛大学毕业证如何办理
一模一样【微信：A575476】【(harvard毕业证书）哈佛大学毕业证成绩单Offer】【微信：A575476】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Population Growth in Bataan: The effects of population growth around rural pl...
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Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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Inferantial statistic presentation about two sample t test
- 1. Two Sample T test Prepared by: Salman And Muhammad Talha Teacher Name: Amad Khalil Subject: Inferential Statistics
- 2. Outlines T-test Types Advantages of two sample T-test Disadvantages of Two sample T-Test Example Hypothesis Testing Conclusion
- 3. T-Test: A t-test is a statistical method used to determine if there is a significant difference between the means of two groups. One-Sample T-Test: Used when comparing the mean of a sample to a known value or hypothesized population mean. For example, you might use a one-sample t-test to determine if the average height of a group of individuals is significantly different from a known average height. Two-Sample T-Test: Used when comparing the means of two independent groups. For instance, in a clinical trial, you might use a two-sample t-test to determine if there is a significant difference in the effectiveness of a new drug compared to a placebo.
- 4. Advantages of Two-Sample T-Test: Simplicity: The two-sample t-test is relatively straightforward and easy to understand, making it accessible to a wide range of researchers and analysts. Small Sample Sizes: It can be effective even with small sample sizes, provided the data is approximately normally distributed.
- 5. Disadvantages of Two-Sample T-Test: Equal Variances: The standard t-test assumes equal variances between the two groups. If variances are significantly different, alternative versions of the test (such as the Welch's t-test) may be more appropriate. Non-Normal Data: The two-sample t-test is less accurate when dealing with strongly non- normal data, especially if sample sizes are small.
- 6. Formula 𝑇 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒 = 𝑑 𝑆.𝐸 = ∑(𝑥−𝑦) 𝑛 𝑆.𝐷 𝑛 𝑆. 𝐷 = (d – d)2 𝑛−1
- 7. Year Inflation Rate (Pakistan) Inflation Rate (Bangladesh) 2008 12.0 8.9 2009 19.6 7.6 2010 10.1 6.8 2011 13.7 10.9 2012 11 8.9 2013 7.4 6.8 2014 8.6 7.3 2015 4.5 6.4 2016 2.9 5.9 2017 4.1 5.4 2018 3.9 5.8 2019 6.7 5.5 2020 10.7 5.6 2021 8.9 5.6 2022 12.1 6 2023 29.2 6.2
- 8. Question Is there a significant difference in the mean inflation rates between Pakistan and Bangladesh for the years 2008 to 2023? Perform a two-sample t-test to compare the inflation rates of both countries, with the null hypothesis stating no significant difference and the alternative hypothesis suggesting otherwise. Use a significance level of 0.05.
- 9. Year Inflation Rate (Pakistan) (X) Inflation Rate (Bangladesh) (Y) X-Y(d) d - d (d – d)2 2008 12.0 8.9 3.1 -0.3875 0.150 2009 19.6 7.6 12 8.5125 72.463 2010 10.1 6.8 3.3 -0.1875 0.035 2011 13.7 10.9 2.8 -0.6875 0.472 2012 11 8.9 2.1 -1.3875 1.925 2013 7.4 6.8 0.6 -2.8875 8.337 2014 8.6 7.3 1.3 -2.1875 4.785 2015 4.5 6.4 -1.9 -5.3875 29.025 2016 2.9 5.9 -3 -6.4875 42.087 2017 4.1 5.4 -1.3 -4.7875 22.920 2018 3.9 5.8 -1.9 -5.3875 29.025 2019 6.7 5.5 1.2 -2.2875 5.232 2020 10.7 5.6 5.1 1.6125 2.6 2021 8.9 5.6 3.3 -0.1875 0.035 2022 12.1 6 6.1 2.6125 6.825 2023 29.2 6.2 23 19.5125 380.737 ∑d=55.8 d = 55.8/16=3.4875 ∑(d – d)2 = 606.658
- 10. Step 1 Ho : μ1 = μ2 HA : μ1 ≠ μ2 Step 2 𝑆. 𝐷 = (d – d)2 𝑛−1 = 606.58 16−1 = 40.438 = 6.359 𝑇 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑒 = 𝑑 𝑆.𝐸 = ∑(𝑥−𝑦) 𝑛 𝑆.𝐷 𝑛 = 55.8 16 6.359 16 = 3.4875 1.58975 = 2.1937
- 11. Step 3 df = 16-1 = 15 Step 4 T(15) (0.05) = ± 2.131
- 12. Conclusion After conducting a two-sample t-test with a significance level of 0.05 to compare the mean inflation rates between Pakistan and Bangladesh for the years 2008 to 2023, we reject the null hypothesis. There is sufficient evidence to conclude that there is a significant difference in the mean inflation rates between the two countries during this period. Source https://en.wikipedia.org/wiki/Economy_of_Pakistan https://en.wikipedia.org/wiki/Economy_of_Bangladesh