Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
This Learning explains textbook classification and SPSS classification of Quantitative data and Qualitative data.
It clarifies that Quantitative data is also Continuous data and Qualitative data in SPSS is know as Categorical data.
SCALE measure in SPSS are used to set up Quantitative variables:
In SPSS, your Quantitative data is entered through creating variables with have SCALE measure and Categorical data of your research is dealt by creating variables that have ORDINAL and NOMINAL measures.
When you have data that is being measured on a standard metric scale, then it is good idea to use SCALE measurement for the variable in SPSS. For example, age, marks secured, height, weight, income etc are examples of Quantitative VARIABLE measured on SCALE measure.
ORDINAL and NOMINAL measures in SPSS are used to set up Qualitative variables:
Further is worth understanding that SCALE data can be measured in Interval Scale or a Ratio Scale.
RATIO SCALE measure of Quantitative variables:
A ratio scale has a true zero point in the measurement. For Example, Dividend paid on stock of a company say ABC Limited is $40 per share and that of XYZ is $30 per share. Here We are using Ratio scale of measurement as we can clearly not only tell that $40 is more that $30 and there is difference of $10 but there is also a true zero point as zero dividend means no dividend. So in Ratio Scale zero value makes sense and it represents absence of that particular phenomenon, for example zero dividend means absence of dividend or no dividends. So, there is a true zero in Ratio Scale.
INTERVAL SCALE measure of Quantitative variables:
In Interval Scale there is no true zero. Temperature in Fahrenheit and Celsius is a good example of interval scale because zero-degree temperature in Fahrenheit or Celsius does not mean absence of temperature.
ORDINAL measure for Qualitative variables/Categorical Data:
Nominal Measure in SPSS is used to set up categorical and Qualitative data. There are of course categories like 1-Sales, 2-Accounting, 3- Research etc. but these categories are not ranked, and we are not saying 3 (Research) is better than 2(Accounting). Similarly, we are not saying that 2 is better than 1 or Accounting is better than Sales. So, with Nominal measure data is categorized but not ranked. Moreover, variables like gender, 1-male 0-female can also be measured on Nominal Measures. Similarly, Religious affiliations and data about various geographical zone can also be measured on Nominal Scale. Similarly, marital status like 1-never married, 2-separated, 3-divorced,4-single etc can also be set on Nominal Measure.
With this understanding, you should set up your research variables accordingly in SPSS so that statistical analysis can be performed accordingly.
Multiple Linear Regression II and ANOVA IJames Neill
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
This Learning explains textbook classification and SPSS classification of Quantitative data and Qualitative data.
It clarifies that Quantitative data is also Continuous data and Qualitative data in SPSS is know as Categorical data.
SCALE measure in SPSS are used to set up Quantitative variables:
In SPSS, your Quantitative data is entered through creating variables with have SCALE measure and Categorical data of your research is dealt by creating variables that have ORDINAL and NOMINAL measures.
When you have data that is being measured on a standard metric scale, then it is good idea to use SCALE measurement for the variable in SPSS. For example, age, marks secured, height, weight, income etc are examples of Quantitative VARIABLE measured on SCALE measure.
ORDINAL and NOMINAL measures in SPSS are used to set up Qualitative variables:
Further is worth understanding that SCALE data can be measured in Interval Scale or a Ratio Scale.
RATIO SCALE measure of Quantitative variables:
A ratio scale has a true zero point in the measurement. For Example, Dividend paid on stock of a company say ABC Limited is $40 per share and that of XYZ is $30 per share. Here We are using Ratio scale of measurement as we can clearly not only tell that $40 is more that $30 and there is difference of $10 but there is also a true zero point as zero dividend means no dividend. So in Ratio Scale zero value makes sense and it represents absence of that particular phenomenon, for example zero dividend means absence of dividend or no dividends. So, there is a true zero in Ratio Scale.
INTERVAL SCALE measure of Quantitative variables:
In Interval Scale there is no true zero. Temperature in Fahrenheit and Celsius is a good example of interval scale because zero-degree temperature in Fahrenheit or Celsius does not mean absence of temperature.
ORDINAL measure for Qualitative variables/Categorical Data:
Nominal Measure in SPSS is used to set up categorical and Qualitative data. There are of course categories like 1-Sales, 2-Accounting, 3- Research etc. but these categories are not ranked, and we are not saying 3 (Research) is better than 2(Accounting). Similarly, we are not saying that 2 is better than 1 or Accounting is better than Sales. So, with Nominal measure data is categorized but not ranked. Moreover, variables like gender, 1-male 0-female can also be measured on Nominal Measures. Similarly, Religious affiliations and data about various geographical zone can also be measured on Nominal Scale. Similarly, marital status like 1-never married, 2-separated, 3-divorced,4-single etc can also be set on Nominal Measure.
With this understanding, you should set up your research variables accordingly in SPSS so that statistical analysis can be performed accordingly.
Hypothesis is usually considered as the principal instrument in research and quality control. Its main function is to suggest new experiments and observations. In fact, many experiments are carried out with the deliberate object of testing hypothesis. Decision makers often face situations wherein they are interested in testing hypothesis on the basis of available information and then take decisions on the basis of such testing. In Six –Sigma methodology, hypothesis testing is a tool of substance and used in analysis phase of the six sigma project so that improvement can be done in right direction
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
Test of significance (t-test, proportion test, chi-square test)Ramnath Takiar
The presentation discusses the concept of test of significance including the test of significance examples of t-test, proportion test and chi-square test.
Testing hypothesis (methods of testing the statement of organizations)syedahadisa929
My ppt is about the testing hypothesis which is used in statistics to check whether the statement of company, organization, or institution is true or false
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
2. Note – the reporting format shown in this learning
module is for APA. For other formats consult specific
format guides.
3. Note – the reporting format shown in this learning
module is for APA. For other formats consult specific
format guides.
It is also recommended to consult the latest APA
manual to compare what is described in this learning
module with the most updated formats for APA.
5. You can report data from your own experience by using
the template below.
6. You can report data from your own experience by using
the template below.
“A single sample t-test was conducted to to determine if
a statistically significant difference existed between
(insert the DV measure) from a (Insert a description of
the Sample) ________and (Insert the Population).”
7. You can report data from your own experience by using
the template below.
“A single sample t-test was conducted to to determine if
a statistically significant difference existed between
(insert the DV measure) from a (Insert a description of
the Sample) ________and (Insert the Population).”
Here is an example:
8. You can report data from your own experience by using
the template below.
“A single sample t-test was conducted to to determine if
a statistically significant difference existed between
(insert the DV measure) from a (Insert a description of
the Sample) ________and (Insert the Population).”
Here is an example:
“A single sample t-test was conducted to determine if a
statistically significant difference existed between IQ
scores from a sample used in the study and the general
population.”
10. Here is how the results for a single-sample t-test are
reported in APA.
11. Here is how the results for a single-sample t-test are
reported in APA.
Students taking statistics courses in psychology at the
University of Washington reported studying similar
hours for tests (M = 121, SD = 14.2) compared to UW
college students in general, t(33) = 2.10, p = .034.
12. Here is how the results for a single-sample t-test are
reported in APA.
Students taking statistics courses in psychology at the
University of Washington reported studying similar
hours for tests (M = 121, SD = 14.2) compared to UW
college students in general, t(33) = 2.10, p = .034.
Here is a template:
13. Here is how the results for a single-sample t-test are
reported in APA.
Students taking statistics courses in psychology at the
University of Washington reported studying similar
hours for tests (M = 121, SD = 14.2) compared to UW
college students in general, t(33) = 2.10, p = .034.
Here is a template:
[Describe the single sample] [Report the results] (M =
[ ], SD = [ ]) than [Insert population], t( ) = [ ], p = [ ].
14. Here is how the results for a single-sample t-test are
reported in APA.
Students taking statistics courses in psychology at the
University of Washington reported studying similar
hours for tests (M = 121, SD = 14.2) compared to UW
college students in general, t(33) = 2.10, p = .034.
Here is a template:
[Describe the single sample] [Report the results] (M =
[ ], SD = [ ]) than [Insert population], t( ) = [ ], p = [ ].
Just fill in the blanks by using the SPSS output.
15. Let’s start by filling in the Mean and Standard Deviation
for each condition.
16. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [ ], SD = [ ]) than
the general population, t( ) = [ ], p = [ ].
17. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [ ], SD = [ ]) than
the general population, t( ) = [ ], p = [ ].
18. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [ ]) than
the general population, t( ) = [ ], p = [ ].
19. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [ ]) than
the general population, t( ) = [ ], p = [ ].
20. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [ ]) than
the general population, t( ) = [ ], p = [ ].
21. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t( ) = [ ], p = [ ].
22. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t( ) = [ ], p = [ ].
Degrees of
freedom (N-1)
23. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t(22) = [ ], p = [ ].
Degrees of
freedom (N-1)
24. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t(22) = [ ], p = [ ].
One-Sample Test
Test Value = 100
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Broccoli_Sample 7.859 22 .000 19.95652 14.6901 25.2229
25. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t(22) = [7.86], p = [ ].
One-Sample Test
Test Value = 100
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Broccoli_Sample 7.859 22 .000 19.95652 14.6901 25.2229
26. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t(22) = [7.86], p = [ ].
One-Sample Test
Test Value = 100
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Broccoli_Sample 7.859 22 .000 19.95652 14.6901 25.2229
27. Let’s start by filling in the Mean and Standard Deviation
for each condition.
Persons who eat broccoli regularly received statistically
significantly higher IQ scores (M = [120], SD = [12.2])
than the general population, t(22) = [7.86], p = [0.000].
One-Sample Test
Test Value = 100
t df Sig. (2-tailed) Mean Difference
95% Confidence Interval of the
Difference
Lower Upper
Broccoli_Sample 7.859 22 .000 19.95652 14.6901 25.2229