17 multiple-linear regression
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The document reports on a multiple linear regression analysis conducted in SPSS to determine if the number of cyberbullying incidents and self-esteem scores can predict absenteeism. The analysis found that both cyberbullying incidents and self-esteem scores were statistically significant predictors of absenteeism, explaining 39.2% of the variance. The regression equation for predicting absenteeism is: Predicted Absenteeism = -.712 + .167(Cyberbullying Incidents) + .024(Self-Esteem Score).
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Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
El documento explica cuatro conceptos estadísticos: diferencia, relación, independencia y calidad de ajuste. La diferencia se refiere a comparar estadísticas entre grupos, la relación examina cómo cambian dos variables juntas, la independencia investiga si una variable depende de otra, y la calidad de ajuste compara resultados reales con expectativas.
PSY 527 Assessment Techniques Text Mastering Modern Psycholog.docx
PSY 527
Assessment Techniques
Text: Mastering Modern Psychological Testing: Theory and Methods
First Edition, 2012
ISBN 13: 978-0-205-48350-1
Author(s): Cecil R. Reynolds and Ronald B. Livingston
Publisher: Pearson
shapeType75fBehindDocument1pWrapPolygonVertices8;4;(21500,0);(0,0);(0,21477);(21500,21477)posrelh0posrelv0pib
Multiple Choice Questions (Enter your answers on the enclosed answer sheet)
l. Performance on pure tests are assessed based on time, while pure _
tests are assessed based on d iffic u Ity.
a. speed; power
b. power; speed
c. achievement; maximum performance
d. maximum performance; achievement
2. Which scores would be interpreted appropriately for measuring a student's mastery of a
specific domain of knowledge?
a. Norm-referenced scores
b. Criterion-referenced scores
c. Standardized-referenced scores
d. Projective-referenced scores
3. A classroom teacher gives her students a final exam that is the basis for 50 of their final
grade in the course. This is an example of which type of evaluation?
a. Projective eval uation
b. Summative evaluation
c. Formative evaluation
d. Feedback evaluation
4. The majority of assessment information collected by most teachers comes from:
a. professionally developed tests.
b. state-wide tests.
c. performance tests.
d. teacher made tests.
5. Susan has been evaluated and determined to be learning disabled. This is an example of:
a. assignment.
b. classification.
c. placement
d. selection.
49
shapeType75fBehindDocument1pWrapPolygonVertices8;4;(21500,0);(0,0);(0,21494);(21500,21494)posrelh0posrelv0pib
6. The Scholastic Achievement Test (SAT) is atn):
a. pure speed test.
b. maximum performance test.
c. typical response test.
d. projective test.
7. If the range of one or both variables is restricted, the resulting correlation coefficient will
likely:
a. be decreased.
b. be increased.
c. remain the same.
d. either increase or decrease depending on the calculations.
8.
Correlation imply causation.
a. does
b. does not
c. might, depending on the scale of measurement
d. none of the above
50
9. The correlation between two variables is 0.70. Using the concept of the coefficient of
determination, the proportion of variance that is determined or predictable from
the relationship between the two measures is:
a.
14.
b.
30.
c.
49.
d.
70.
10. A special mathematical procedure for predicting scores on one variable (criterion or Y)
given a score on another (predictor or X) is:
a. correlational analysis.
b. linear regression.
c. regression analysis.
d. prediction constant.
11. Why would a psychologist feel that the variance might be difficult to interpret?
a. It is a nonlinear transformation.
b. It is an area transformation.
c. It may be a negative number.
d. It uses squared raw score units.
shapeType75fBehindDocument1pWrapPolygonVertices8;4;(21500,0);(0,0);(0,21494);(21500,21494)posrelh0posrelv0pib
12. In a normal distributi ...
StatisticsSummer 2019Name Cindy CharlesMultiple choice s.docx
Statistics
Summer 2019
Name: Cindy Charles
Multiple choice section:
1. A researcher is interested in studying the eating behavior of a rats and selects a group of 25 rats to be tested in a research study. The group of 25 rates is an example of a
a. sample
.
b. statistic
c. population
d. parameter
2. A researcher uses an anonymous survey to investigate the study habits of the American college students. The entire group of American college students is an example of a
a. sample
.
b. statistic
c. population
d. parameter
3. A characteristic, usually a numerical value, that describes a sample is called a
a. sample
.
b. statistic
c. population
d. parameter
4. Determining the class standing for the graduating seniors at a high school would involve measurement on a(n) ____________ scale of measurement.
a. nominal
.
b. ordinal
c. interval
d. ratio
5. A researcher conducts a study to determine whether moderate doses of St. John’s Wort have any effect on memory in college students. For this study, with is the independent variable?
a. the amount of St. John’s Wort given to each participant.
b. the memory score for each participant
c. the group of college students
d. cannot answer without more information.
6. When we are interested in simply looking at the association or relationship between two contiguous variables as they exist naturally , the following research method is likely to be helpful:
a. correlational
b. experimental
c. quasi-experimental
d. non-parametric
7. In an experiment looking at the effect of eating varied levels of multiple portions of ice cream, daily, on blood serum cholesterol levels (HDL)
a. eating ice cream is the independent variable (IV) and HDL is the dependent variable (DV).
b. ice cream, the DV and HDL, the IV
c. neither, this is a correlational study
d. ice cream and HDL are both independent variables
8. “Girl, boy, girl, girl, boy, boy, boy, girl. So, there’s 4 girls and 4 boys.” Our enumerator is employing which of the following scales
a. ratio
b. sex differential
c. interval
d. nominal
9. “I came in first place in the “Best Statistics Professor” category. That clearly shows I am much better at this than most other professors on campus.”
a. This is a reasonable conclusion, since first place in Statistics must be a higher rating than, say, third place in Multicultural Education.
b. The speaker mistakenly attributes arithmetic characteristics to ordinal data.
c. The statement has absolutely no meaning, since we do not know the number of other professors on campus or teaching statistics. It represents an example of the absolute or ratio scale.
d. The interval scale applies here, because voters used a relative standar.
QUESTION 1In large samples, the sampling distribution of the ris.docx
QUESTION 1
In large samples, the sampling distribution of the risk difference is approximately ?
a-normal
b-
Skewed
c. t
d. F
0.67 points
QUESTION 2
Plus-four confidence interval method for a difference in proportions is accurate in samples as small as
a. 100 per group
b. 50 per group
c. 25 per group
d. 5 per group
0.67 points
QUESTION 3
Which of the following is not a requirement for consideration before continuing with the calculation of a sample size in an observational study observing the difference in incidence of disease X based on exposure?
a. Projected drop-out rate to inflate the sample size estimate based on the projected loss to follow-up in a study.
b. A predetermined power for the study to detect a difference when one actually exists.
c. The correlation coefficient between the two groups being compared.
d. The informed estimation of the incidence in both groups being compared.
0.67 points
QUESTION 4
Which of the following is not an example of systematic error in an observational study?
a. A cross-sectional study recruits participants that are willing to sign up outside of a major university and meet the inclusion and exclusion criteria to take part in the survey relating unsafe sex habits to STIs.
b. A researcher is interested in the relationship between coffee drinking and lung cancer, and after careful multivariate linear regression modeling determines that a significant percentage of the relationship is due to another variable, cigarette smoking.
c. An observational study recruits participants for a study looking at Alzheimer’s disease due to exposure to industrial hazards by asking participants to recall their exposure over the past 10 years.
d. A data-entry specialist responsible for adding in fasting glucose levels to a database accidentally skipped an observation during the input phase of data cleaning.
0.67 points
QUESTION 5
Proportions are tested for a significant _____ difference?
a. “random”
b. “nonrandom”
c. “indifferent”
d. “noisy”
0.67 points
QUESTION 6
What is not a method for testing proportions for significance?
a. z test (large sample)
b. Fisher’s exact procedure (small samples)
c. the chi-square test
d. the Wassermann test
0.67 points
QUESTION 7
In calculating for tests of proportions for small samples (fewer than 5 successes expected in either group), avoid the z test and use
a. the exact Fisher or Mid-P procedure
b. the asymptotic procedure
c. the random event generator
d. the special theory of relativity
0.67 points
QUESTION 8
Before conducting Fisher’s test, data are rearranged to form a
a. 1-by-2 table
b. 2-by-1 table
c. 2-by-2 table
d. A single row table
0.67 points
QUESTION 9
The best way to calculate a p value for a Fisher’s Exact is to
a. Use an adding machine
b. Use computer program
c. Call a statistician
d. Use pencil and paper
0.67 poi.
TitleABC123 Version X1Student Textbook Resource Acces.docx
Title
ABC/123 Version X
1
Student Textbook Resource Access
PSYCH/625 Version 2
1
University of Phoenix Material
PSYCH/625 Statistics for the Behavior Sciences
Text Resources for Students
Use the following links to access the data sets for the textbooks used in this course:
Click here to access the Sage materials
Statistics for People Who (Think They) Hate Statistics(5th ed.) (Salkind)
Click here to access the Pearson materials
Using SPSS for Windows and Macintosh. Analyzing and Understanding Data (6th ed.) (Green & Salkind)
Copyright © XXXX by University of Phoenix. All rights reserved.
Copyright © 2014 by University of Phoenix. All rights reserved.
Title
ABC/123 Version X
1
Time to Practice – Week Five
PSYCH/625 Version 2
1
University of Phoenix Material
Time to Practice – Week Five
Complete Parts A, B, and C below.
Part A
Some questions in Part A require that you access data from Statistics for People Who (Think They) Hate Statistics. This data is available on the student website under the Student Text Resources link.
1. Use the following data to answer Questions 1a and 1b.
Total no. of problems correct (out of a possible 20)
Attitude toward test taking (out of a possible 100)
17
94
13
73
12
59
15
80
16
93
14
85
16
66
16
79
18
77
19
91
a. Compute the Pearson product-moment correlation coefficient by hand and show all your work.
b. Construct a scatterplot for these 10 values by hand. Based on the scatterplot, would you predict the correlation to be direct or indirect? Why?
2. Rank the following correlation coefficients on strength of their relationship (list the weakest first):
+.71
+.36
–.45
.47
–.62
3. Use IBM® SPSS® software to determine the correlation between hours of studying and grade point average for these honor students. Why is the correlation so low?
Hours of studying
GPA
23
3.95
12
3.90
15
4.00
14
3.76
16
3.97
21
3.89
14
3.66
11
3.91
18
3.80
9
3.89
4. Look at the following table. What type of correlation coefficient would you use to examine the relationship between sex (defined as male or female) and political affiliation?
How about family configuration (two-parent or single-parent) and high school GPA? Explain why you selected the answers you did.
Level of Measurement and Examples
Variable X
Variable Y
Type of correlation
Correlation being computed
Nominal (voting preference, such as Republican or Democrat)
Nominal (gender, such as male or female)
Phi coefficient
The correlation between voting preference and gender
Nominal (social class, such as high, medium, or low)
Ordinal (rank in high school graduating class)
Rank biserial coefficient
The correlation between social class and rank in high school
Nominal (family configuration, such as intact or single parent)
Interval (grade point average)
Point biserial
The correlation between family configuration and grade point average
Ordinal (height converted to rank)
Ordinal (weight converted to rank)
Spearman rank correlation coefficient
The correlation between hei ...
Part 1 Please circle the correct answer.1. Which of t.docx
Part 1: Please circle the correct answer.
1. Which of the following is most sensitive to outliers?
a. interquartile range b.standard deviation. c. median
d. mode
2. James and George took a math exam. George’s percentile score on the exam was 80; James’s
percentile score was 40 on the same test. We know that
a) George correctly answered twice as many questions than James. b) They both scored better than 40 of their classmates.
c) George correctly answered more questions than James. d) James did not pass the test.
3. Find the mean and standard deviation for a binomial experiment where n 260,
p 0.675 .
a) 260 , b) 260 ,
7.55
57.04
c) 175.50 ,
7.55
d) 97.50 ,
e) 175.50 ,
13.25
9.19
4. If we were to create a box plot, which of the following statement(s) would be true?
A. The median would be located somewhere within the box.
B. The median value would be larger than the third quartile value (Q3) C. The median would always be centered in the middle of the box.
D. The median would indicate where the 50th percentile would be.
a) A and B
b) All of them. c) A and D
d) C and D.
e) None of them.
5. Which of these statements are false?
a. There is a strong linear relationship between gender and height because we found a correlation of .55.
b. Plant height and leaf height were found to be negatively correlated because the correlation coefficient is -1.41.
c. Since the correlation between X and Y is 0, this means there is no relationship whatsoever between these two variables.
d. All of the above.
e. None of the above.
6. Some methods may be used to make a confidence interval wider or narrower. Circle the two
methods that would decrease the width of a confidence interval for a mean, if all else stays the same.
a. Increase the sample size. b. decrease the sample size.
c. increase the level of confidence. d. decrease the level of confidence.
7. Two researchers are going to take a sample of data from the same population of physics students. Researcher A will select a random sample of students from among all students taking physics. Researcher B's sample will consist only of the students in her class. Both researchers will construct a 95% confidence interval for the mean score on the physics final exam using their own sample data. Which researcher's method has a 95% chance of capturing the true mean of the population of all students taking physics?
a. Both methods have a 95% chance of capturing the true mean b. Researcher A
c. Research B
d. Neither
8. A 98% confidence interval indicates that:
a. 98% of the intervals constructed using this process based on samples from this population will include the population mean
b. 98% of the time the interval will include the sample mean
c. 98% of the possible population means will be included by the interval
9. A one-tailed hypothesis test is performed using a 0.10 level of significance. .
Hypothesis Assignment Help
I am Ann Brenda Mathews. Currently associated with statisticsassignmenthelp.com as a statistics assignment help Expert. After completing my Master’s in Statistics, at the University of Liverpool, I was in search of an opportunity that would expand my area of knowledge hence I decided to help students with their assignments. I have written several Hypothesis Assignments to help students overcome numerous difficulties.
Accuracy and errors
Error - What is it?
Standard Error of Measurement
Standard Deviation or Standard Error of Measurement
Why all the fuss about Error?
Sources of Error
Sources of Error Influencing various Reliability Coefficients
Band Interpretation
Recommended
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
El documento explica cuatro conceptos estadísticos: diferencia, relación, independencia y calidad de ajuste. La diferencia se refiere a comparar estadísticas entre grupos, la relación examina cómo cambian dos variables juntas, la independencia investiga si una variable depende de otra, y la calidad de ajuste compara resultados reales con expectativas.
PSY 527 Assessment Techniques Text Mastering Modern Psycholog.docx
PSY 527
Assessment Techniques
Text: Mastering Modern Psychological Testing: Theory and Methods
First Edition, 2012
ISBN 13: 978-0-205-48350-1
Author(s): Cecil R. Reynolds and Ronald B. Livingston
Publisher: Pearson
shapeType75fBehindDocument1pWrapPolygonVertices8;4;(21500,0);(0,0);(0,21477);(21500,21477)posrelh0posrelv0pib
Multiple Choice Questions (Enter your answers on the enclosed answer sheet)
l. Performance on pure tests are assessed based on time, while pure _
tests are assessed based on d iffic u Ity.
a. speed; power
b. power; speed
c. achievement; maximum performance
d. maximum performance; achievement
2. Which scores would be interpreted appropriately for measuring a student's mastery of a
specific domain of knowledge?
a. Norm-referenced scores
b. Criterion-referenced scores
c. Standardized-referenced scores
d. Projective-referenced scores
3. A classroom teacher gives her students a final exam that is the basis for 50 of their final
grade in the course. This is an example of which type of evaluation?
a. Projective eval uation
b. Summative evaluation
c. Formative evaluation
d. Feedback evaluation
4. The majority of assessment information collected by most teachers comes from:
a. professionally developed tests.
b. state-wide tests.
c. performance tests.
d. teacher made tests.
5. Susan has been evaluated and determined to be learning disabled. This is an example of:
a. assignment.
b. classification.
c. placement
d. selection.
49
shapeType75fBehindDocument1pWrapPolygonVertices8;4;(21500,0);(0,0);(0,21494);(21500,21494)posrelh0posrelv0pib
6. The Scholastic Achievement Test (SAT) is atn):
a. pure speed test.
b. maximum performance test.
c. typical response test.
d. projective test.
7. If the range of one or both variables is restricted, the resulting correlation coefficient will
likely:
a. be decreased.
b. be increased.
c. remain the same.
d. either increase or decrease depending on the calculations.
8.
Correlation imply causation.
a. does
b. does not
c. might, depending on the scale of measurement
d. none of the above
50
9. The correlation between two variables is 0.70. Using the concept of the coefficient of
determination, the proportion of variance that is determined or predictable from
the relationship between the two measures is:
a.
14.
b.
30.
c.
49.
d.
70.
10. A special mathematical procedure for predicting scores on one variable (criterion or Y)
given a score on another (predictor or X) is:
a. correlational analysis.
b. linear regression.
c. regression analysis.
d. prediction constant.
11. Why would a psychologist feel that the variance might be difficult to interpret?
a. It is a nonlinear transformation.
b. It is an area transformation.
c. It may be a negative number.
d. It uses squared raw score units.
shapeType75fBehindDocument1pWrapPolygonVertices8;4;(21500,0);(0,0);(0,21494);(21500,21494)posrelh0posrelv0pib
12. In a normal distributi ...
StatisticsSummer 2019Name Cindy CharlesMultiple choice s.docx
Statistics
Summer 2019
Name: Cindy Charles
Multiple choice section:
1. A researcher is interested in studying the eating behavior of a rats and selects a group of 25 rats to be tested in a research study. The group of 25 rates is an example of a
a. sample
.
b. statistic
c. population
d. parameter
2. A researcher uses an anonymous survey to investigate the study habits of the American college students. The entire group of American college students is an example of a
a. sample
.
b. statistic
c. population
d. parameter
3. A characteristic, usually a numerical value, that describes a sample is called a
a. sample
.
b. statistic
c. population
d. parameter
4. Determining the class standing for the graduating seniors at a high school would involve measurement on a(n) ____________ scale of measurement.
a. nominal
.
b. ordinal
c. interval
d. ratio
5. A researcher conducts a study to determine whether moderate doses of St. John’s Wort have any effect on memory in college students. For this study, with is the independent variable?
a. the amount of St. John’s Wort given to each participant.
b. the memory score for each participant
c. the group of college students
d. cannot answer without more information.
6. When we are interested in simply looking at the association or relationship between two contiguous variables as they exist naturally , the following research method is likely to be helpful:
a. correlational
b. experimental
c. quasi-experimental
d. non-parametric
7. In an experiment looking at the effect of eating varied levels of multiple portions of ice cream, daily, on blood serum cholesterol levels (HDL)
a. eating ice cream is the independent variable (IV) and HDL is the dependent variable (DV).
b. ice cream, the DV and HDL, the IV
c. neither, this is a correlational study
d. ice cream and HDL are both independent variables
8. “Girl, boy, girl, girl, boy, boy, boy, girl. So, there’s 4 girls and 4 boys.” Our enumerator is employing which of the following scales
a. ratio
b. sex differential
c. interval
d. nominal
9. “I came in first place in the “Best Statistics Professor” category. That clearly shows I am much better at this than most other professors on campus.”
a. This is a reasonable conclusion, since first place in Statistics must be a higher rating than, say, third place in Multicultural Education.
b. The speaker mistakenly attributes arithmetic characteristics to ordinal data.
c. The statement has absolutely no meaning, since we do not know the number of other professors on campus or teaching statistics. It represents an example of the absolute or ratio scale.
d. The interval scale applies here, because voters used a relative standar.
QUESTION 1In large samples, the sampling distribution of the ris.docx
QUESTION 1
In large samples, the sampling distribution of the risk difference is approximately ?
a-normal
b-
Skewed
c. t
d. F
0.67 points
QUESTION 2
Plus-four confidence interval method for a difference in proportions is accurate in samples as small as
a. 100 per group
b. 50 per group
c. 25 per group
d. 5 per group
0.67 points
QUESTION 3
Which of the following is not a requirement for consideration before continuing with the calculation of a sample size in an observational study observing the difference in incidence of disease X based on exposure?
a. Projected drop-out rate to inflate the sample size estimate based on the projected loss to follow-up in a study.
b. A predetermined power for the study to detect a difference when one actually exists.
c. The correlation coefficient between the two groups being compared.
d. The informed estimation of the incidence in both groups being compared.
0.67 points
QUESTION 4
Which of the following is not an example of systematic error in an observational study?
a. A cross-sectional study recruits participants that are willing to sign up outside of a major university and meet the inclusion and exclusion criteria to take part in the survey relating unsafe sex habits to STIs.
b. A researcher is interested in the relationship between coffee drinking and lung cancer, and after careful multivariate linear regression modeling determines that a significant percentage of the relationship is due to another variable, cigarette smoking.
c. An observational study recruits participants for a study looking at Alzheimer’s disease due to exposure to industrial hazards by asking participants to recall their exposure over the past 10 years.
d. A data-entry specialist responsible for adding in fasting glucose levels to a database accidentally skipped an observation during the input phase of data cleaning.
0.67 points
QUESTION 5
Proportions are tested for a significant _____ difference?
a. “random”
b. “nonrandom”
c. “indifferent”
d. “noisy”
0.67 points
QUESTION 6
What is not a method for testing proportions for significance?
a. z test (large sample)
b. Fisher’s exact procedure (small samples)
c. the chi-square test
d. the Wassermann test
0.67 points
QUESTION 7
In calculating for tests of proportions for small samples (fewer than 5 successes expected in either group), avoid the z test and use
a. the exact Fisher or Mid-P procedure
b. the asymptotic procedure
c. the random event generator
d. the special theory of relativity
0.67 points
QUESTION 8
Before conducting Fisher’s test, data are rearranged to form a
a. 1-by-2 table
b. 2-by-1 table
c. 2-by-2 table
d. A single row table
0.67 points
QUESTION 9
The best way to calculate a p value for a Fisher’s Exact is to
a. Use an adding machine
b. Use computer program
c. Call a statistician
d. Use pencil and paper
0.67 poi.
TitleABC123 Version X1Student Textbook Resource Acces.docx
Title
ABC/123 Version X
1
Student Textbook Resource Access
PSYCH/625 Version 2
1
University of Phoenix Material
PSYCH/625 Statistics for the Behavior Sciences
Text Resources for Students
Use the following links to access the data sets for the textbooks used in this course:
Click here to access the Sage materials
Statistics for People Who (Think They) Hate Statistics(5th ed.) (Salkind)
Click here to access the Pearson materials
Using SPSS for Windows and Macintosh. Analyzing and Understanding Data (6th ed.) (Green & Salkind)
Copyright © XXXX by University of Phoenix. All rights reserved.
Copyright © 2014 by University of Phoenix. All rights reserved.
Title
ABC/123 Version X
1
Time to Practice – Week Five
PSYCH/625 Version 2
1
University of Phoenix Material
Time to Practice – Week Five
Complete Parts A, B, and C below.
Part A
Some questions in Part A require that you access data from Statistics for People Who (Think They) Hate Statistics. This data is available on the student website under the Student Text Resources link.
1. Use the following data to answer Questions 1a and 1b.
Total no. of problems correct (out of a possible 20)
Attitude toward test taking (out of a possible 100)
17
94
13
73
12
59
15
80
16
93
14
85
16
66
16
79
18
77
19
91
a. Compute the Pearson product-moment correlation coefficient by hand and show all your work.
b. Construct a scatterplot for these 10 values by hand. Based on the scatterplot, would you predict the correlation to be direct or indirect? Why?
2. Rank the following correlation coefficients on strength of their relationship (list the weakest first):
+.71
+.36
–.45
.47
–.62
3. Use IBM® SPSS® software to determine the correlation between hours of studying and grade point average for these honor students. Why is the correlation so low?
Hours of studying
GPA
23
3.95
12
3.90
15
4.00
14
3.76
16
3.97
21
3.89
14
3.66
11
3.91
18
3.80
9
3.89
4. Look at the following table. What type of correlation coefficient would you use to examine the relationship between sex (defined as male or female) and political affiliation?
How about family configuration (two-parent or single-parent) and high school GPA? Explain why you selected the answers you did.
Level of Measurement and Examples
Variable X
Variable Y
Type of correlation
Correlation being computed
Nominal (voting preference, such as Republican or Democrat)
Nominal (gender, such as male or female)
Phi coefficient
The correlation between voting preference and gender
Nominal (social class, such as high, medium, or low)
Ordinal (rank in high school graduating class)
Rank biserial coefficient
The correlation between social class and rank in high school
Nominal (family configuration, such as intact or single parent)
Interval (grade point average)
Point biserial
The correlation between family configuration and grade point average
Ordinal (height converted to rank)
Ordinal (weight converted to rank)
Spearman rank correlation coefficient
The correlation between hei ...
Part 1 Please circle the correct answer.1. Which of t.docx
Part 1: Please circle the correct answer.
1. Which of the following is most sensitive to outliers?
a. interquartile range b.standard deviation. c. median
d. mode
2. James and George took a math exam. George’s percentile score on the exam was 80; James’s
percentile score was 40 on the same test. We know that
a) George correctly answered twice as many questions than James. b) They both scored better than 40 of their classmates.
c) George correctly answered more questions than James. d) James did not pass the test.
3. Find the mean and standard deviation for a binomial experiment where n 260,
p 0.675 .
a) 260 , b) 260 ,
7.55
57.04
c) 175.50 ,
7.55
d) 97.50 ,
e) 175.50 ,
13.25
9.19
4. If we were to create a box plot, which of the following statement(s) would be true?
A. The median would be located somewhere within the box.
B. The median value would be larger than the third quartile value (Q3) C. The median would always be centered in the middle of the box.
D. The median would indicate where the 50th percentile would be.
a) A and B
b) All of them. c) A and D
d) C and D.
e) None of them.
5. Which of these statements are false?
a. There is a strong linear relationship between gender and height because we found a correlation of .55.
b. Plant height and leaf height were found to be negatively correlated because the correlation coefficient is -1.41.
c. Since the correlation between X and Y is 0, this means there is no relationship whatsoever between these two variables.
d. All of the above.
e. None of the above.
6. Some methods may be used to make a confidence interval wider or narrower. Circle the two
methods that would decrease the width of a confidence interval for a mean, if all else stays the same.
a. Increase the sample size. b. decrease the sample size.
c. increase the level of confidence. d. decrease the level of confidence.
7. Two researchers are going to take a sample of data from the same population of physics students. Researcher A will select a random sample of students from among all students taking physics. Researcher B's sample will consist only of the students in her class. Both researchers will construct a 95% confidence interval for the mean score on the physics final exam using their own sample data. Which researcher's method has a 95% chance of capturing the true mean of the population of all students taking physics?
a. Both methods have a 95% chance of capturing the true mean b. Researcher A
c. Research B
d. Neither
8. A 98% confidence interval indicates that:
a. 98% of the intervals constructed using this process based on samples from this population will include the population mean
b. 98% of the time the interval will include the sample mean
c. 98% of the possible population means will be included by the interval
9. A one-tailed hypothesis test is performed using a 0.10 level of significance. .
Hypothesis Assignment Help
I am Ann Brenda Mathews. Currently associated with statisticsassignmenthelp.com as a statistics assignment help Expert. After completing my Master’s in Statistics, at the University of Liverpool, I was in search of an opportunity that would expand my area of knowledge hence I decided to help students with their assignments. I have written several Hypothesis Assignments to help students overcome numerous difficulties.
Accuracy and errors
Error - What is it?
Standard Error of Measurement
Standard Deviation or Standard Error of Measurement
Why all the fuss about Error?
Sources of Error
Sources of Error Influencing various Reliability Coefficients
Band Interpretation
X18125514 ca2-statisticsfor dataanalytics
4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS
Discovering Statistics Using IBM SPSS Statistics 4th Edition Field Test Bank
Full download http://alibabadownload.com/product/discovering-statistics-using-ibm-spss-statistics-4th-edition-field-test-bank/
Discovering Statistics Using IBM SPSS Statistics 4th Edition Field Test Bank
C0364010013
This document discusses predictive models that can be used to understand student performance in post-graduate entrance examinations. It proposes using data mining techniques like cluster analysis, classification, item analysis, and regression analysis on past student exam scores to identify strengths and weaknesses among different student groups. A probabilistic model is also described for devising multiple choice questions based on the probability that students with different abilities will correctly answer questions of varying difficulty levels. The goal is to develop a balanced exam paper that limits random guessing and accurately tests student aptitude.
Cognitive Abilities Test Parent Presentation
The document summarizes information about the Cognitive Abilities Test (CogAT) administered to third grade students in the Wake County Public School System. It explains that the CogAT measures verbal, quantitative, and nonverbal reasoning and provides scores to help with educational planning, including identification for gifted programs. National age scores are provided on scales from 1 to 9 and as percentiles. The student's profile code indicates their pattern of strengths and weaknesses across the three sections. Scores at or above the 85th percentile make students eligible for further gifted testing using the Iowa Test of Basic Skills.
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent variable that we are most interested in describing are:
a.
frequency and average.
b.
average and correlation.
c.
central tendency and dispersion.
d.
histograms and polygons.
2. The ________________ is the sum of all the scores divided by the number of scores.
a.
median
b.
mean
c.
mode
d.
standard deviation
3. The generally preferred measure of central tendency is usually the
a.
range
b.
mean
c.
standard deviation
d.
Median
4. Which of the following is the most useful descriptive statistic for measuring dispersion?
a.
Range
b.
Variance
c.
mean deviation
d.
standard deviation
5. The standard deviation is
a.
the square of the variance.
b.
the square root of the variance.
c.
smaller than the mean.
d.
the difference between the highest and lowest scores.
6. If the mean I.Q. is 100 and the standard deviation of I.Q. scores is 15, then an I.Q. of 130 will have a z score (or standard score) of
a.
1.00
b.
0.00
c.
2.00
d.
-2.00
7. Inferential statistics allow you to decide whether a difference between the experimental and the control group is due to _______________ or ________________.
a.
manipulation; chance
b.
manipulation; experimental error
c.
sampling error; independent variable
d.
independent variable; experimental error
8. The null hypothesis suggests that the two samples come from ___________ distribution(s), and the experimental hypothesis suggests that the two samples come from _____________ distribution(s).
a.
different; different
b.
different; the same
c.
the same; different
d.
the same; the same
9. The power of a statistical test refers to its ability to
a.
reject false null hypotheses.
b.
reject false experimental hypotheses.
c.
reject true null hypotheses.
d.
reject true experimental hypotheses.
10. Simple analysis of variance is used in designs having
a.
one independent variable
b.
more than one independent variable
c.
more than one independent variable (IV) but less than four IVs
d.
more than one dependent variable
11. The number of participants in a study is denoted by
a.
s.
b.
n.
c.
z.
d.
r.
12. A _____________ is a complete set of measurements.
a.
sample
b.
population
c.
random sampling
d.
parameter
13. _____________ is one way of ensuring that a sample is representative of the population.
a.
The two-tailed test
b.
The between-subjects design
c.
The sign test
d.
Random sampling
14. If we conduct an experiment on average young, white, college males, inferential statistics allow us to generalize to the population of
a.
average young, white, college males.
b.
college male students.
c.
college students.
d.
young adults.
15. If we apply an alpha level of .05, and there really is no effect of the experimental manipulation, then one should make a Type I error
a.
5% of the time.
b.
10% of the time.
c.
15% of the time.
d.
95% of the time.
16. Which of the following would be considered the most conservative alpha level ...
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent variable that we are most interested in describing are:
a.
frequency and average.
b.
average and correlation.
c.
central tendency and dispersion.
d.
histograms and polygons.
2. The ________________ is the sum of all the scores divided by the number of scores.
a.
median
b.
mean
c.
mode
d.
standard deviation
3. The generally preferred measure of central tendency is usually the
a.
range
b.
mean
c.
standard deviation
d.
Median
4. Which of the following is the most useful descriptive statistic for measuring dispersion?
a.
Range
b.
Variance
c.
mean deviation
d.
standard deviation
5. The standard deviation is
a.
the square of the variance.
b.
the square root of the variance.
c.
smaller than the mean.
d.
the difference between the highest and lowest scores.
6. If the mean I.Q. is 100 and the standard deviation of I.Q. scores is 15, then an I.Q. of 130 will have a z score (or standard score) of
a.
1.00
b.
0.00
c.
2.00
d.
-2.00
7. Inferential statistics allow you to decide whether a difference between the experimental and the control group is due to _______________ or ________________.
a.
manipulation; chance
b.
manipulation; experimental error
c.
sampling error; independent variable
d.
independent variable; experimental error
8. The null hypothesis suggests that the two samples come from ___________ distribution(s), and the experimental hypothesis suggests that the two samples come from _____________ distribution(s).
a.
different; different
b.
different; the same
c.
the same; different
d.
the same; the same
9. The power of a statistical test refers to its ability to
a.
reject false null hypotheses.
b.
reject false experimental hypotheses.
c.
reject true null hypotheses.
d.
reject true experimental hypotheses.
10. Simple analysis of variance is used in designs having
a.
one independent variable
b.
more than one independent variable
c.
more than one independent variable (IV) but less than four IVs
d.
more than one dependent variable
11. The number of participants in a study is denoted by
a.
s.
b.
n.
c.
z.
d.
r.
12. A _____________ is a complete set of measurements.
a.
sample
b.
population
c.
random sampling
d.
parameter
13. _____________ is one way of ensuring that a sample is representative of the population.
a.
The two-tailed test
b.
The between-subjects design
c.
The sign test
d.
Random sampling
14. If we conduct an experiment on average young, white, college males, inferential statistics allow us to generalize to the population of
a.
average young, white, college males.
b.
college male students.
c.
college students.
d.
young adults.
15. If we apply an alpha level of .05, and there really is no effect of the experimental manipulation, then one should make a Type I error
a.
5% of the time.
b.
10% of the time.
c.
15% of the time.
d.
95% of the time.
16. Which of the following would be considered the most conservative alpha level ...
multinomial-pdf.pdf
Multinomial logistic regression is an extension of binary logistic regression for outcomes with more than two unordered categories. It models the probabilities of the different categories using maximum likelihood estimation. Some examples of multinomial outcomes include blood type or candidate choice. The assumptions include no multicollinearity, no outliers, independence of observations, and linearity of relationships. The results are k-1 logistic regression models, where k is the number of categories. Interpretation includes pseudo R-square, p-values, and odds ratios. Resources for practicing on different datasets were also provided.
Alumni Donation - Complete exploration and analysis report
This study investigates the factors that influence alumni donations. We use exploratory data analysis and multi factor linear regression techniques to predict alumni giving rate. After analyzing relationships between the variables, we fit linear regression models and perform residual diagnosis to ensure that assumptions of linear regression are not violated. We find that lower student-faculty ratio leads to higher alumni donations and smaller class size is insignificant in predicting alumni donations. We include an additional parameter graduation rate, which is highly correlated with alumni giving rate and interaction between student-faculty ratio and dummy variable private in our final model. The final mode has an adjusted R-squared of 75.76%.
Regression techniques to study the student performance in post graduate exam...
This document discusses using linear regression techniques to analyze student performance on post-graduate entrance exams in Karnataka, India. Specifically, it uses a sample of 1,000 student records to model entrance exam scores (dependent variable) based on undergraduate degree scores (independent variable). The analysis finds a perfect correlation between the two sets of scores, likely because the degree scores were derived from entrance scores. The document concludes that regression can help categorize student groups and inform question selection to better test aptitude on entrance exams.
ECO 480 Econometrics I Problem Set 5 Due Monday, Nove.docx
ECO 480 Econometrics I
Problem Set 5
Due: Monday, November 30, 2015 (beginning of the class)
1
Instruction: The problem sets are designed to be difficult and very time-intensive, so plan ahead. The
problem sets consists of solving theoretical problems and analyzing real data. You may discuss the
questions with your classmates, but you are required to hand in your own independently written solutions,
do-files, and log-files. No late work will be accepted and I do NOT accept any electronic copy. All the
data necessary for the problem set is available under UBlearns.
Important: It is extremely important to write a clean well-commented program for transparency and
replication purposes. In any empirical work, you should always be able to reproduce your result from raw
data to support your claim.
What to hand in: Typed write-up answering the assigned questions and interpreting your findings, do-file,
and log-file (you MUST use Stata). For questions involving data analysis, you will NOT get any credit if
you do not provide a program code. You may NOT use Excel.
1. How are returns on common stocks in overseas markets related to returns in U.S. markets? Consider
measuring U.S. returns by the annual rate of returns on the Standard & Poor’s (S&P) 500 stock index
and overseas returns by the annual rate of returns on the Morgan Stanley Europe, Australia, Asia, Far
East (EAFE) index. Both are recorded in percent. Regressing the EAFE returns on the S&P 500
returns for the 20 years 1989 to 2008. Here is part of the output for this regression:
The regression equation is EAFE = -2.58 + 0.775 S&P
Analysis of Variance
Source df SS MS
Model 4560.6
Residual
Total 19 8556.0
a. Complete the analysis of variance table by filling in the missing entries. Show (Justify) how you
came up with that answer.
b. What are the values of the regression standard error and the R2?
c. Find the standard error for the least-squares slope. Show your work.
d. If I tell you that the standard deviation of the S&P 500 returns for these years is 19.99%. How
would you use this information to find the standard error for the least-squares slope you found in
part c?
e. Give a 95% confidence interval for the slope β1 of the population regression line. Show your
work.
ECO 480 Econometrics I
Problem Set 5
Due: Monday, November 30, 2015 (beginning of the class)
2
2. An electronic survey of 7,061 players of Guitar Hero and Rock Band reported that 67% of those who
do not currently play a musical instrument said that they are likely to begin playing a real musical
instrument in the next two years. The reports describing the survey do not give the number of
respondents who do not currently play a musical instrument.
a. Explain why it is important to know the number of respondents who do not currently play a
musical instrument.
b. Assume that half of the respondents do not curre ...
Lecture 6 Point and Interval Estimation.pptx
Point and Interval Estimation:
Objectives:
Apply the basics of inferential statistics in terms of point estimation.
Compute point and estimation of population means and confidence interval.
Interpret the results of point and interval estimation.
Estimation:
Estimating the value of parameter from the sample:
An aspect of inferential statistics.
Why to estimate: Population is large enough so we
can only estimate.
Types of estimation:
Point Estimation:
A specified number value (single value) that is an estimate of a population parameter. The point estimate of the population mean µ is the sample mean.
Interval Estimate:
Range of values to estimate about population parameter.
Confidence Interval Estimation:
Range of values to estimate about population parameter.
May contain the parameter or not (Degree of confidence).
Ranges between two values.
Example:
Age (in years) 4 BScN students: 20<µ < 25 or (22.5 +2.5)
FORMULA:
Point estimate (x) + Critical Value x Standard Error.
Confidence Interval is a particular interval of estimate.
Given that sample size is large, the 95% of the sample means taken from same population and same sample size will fall in + 1.96 SD of the population mean.
Three commonly used Confidence Intervals are 90%, 95% (by default) , and 99%.
Why not too small or too large confidence intervals?
Too wide: 99.9% Interval too broad
Too narrow: 80 % More uncertainty to have population mean.
The 99% of the sample means taken from same population and
same sample size will fall in + 2.575 SD of the population mean.
Interpretation:
99% probability that interval will enclose population parameter and 1% chance that it will not have population parameter.
Level of confidence: The level of certainty that the interval will have the true population mean.
Chances of Error: Chances that the interval will not cater the true parameter.
Sum of level of confidence and chances of error =100%
LR 9 Estimation.pdf
The document provides an overview of key concepts related to estimation in statistics, including:
- Estimation involves using sample data to estimate unknown population parameters. Common estimators include the sample mean, proportion, and standard deviation.
- There are two main types of estimates - point estimates and interval estimates. Point estimates are single values while interval estimates specify a range.
- The process of estimation involves identifying the parameter, selecting a random sample, choosing an estimator, and calculating the estimate.
- Estimates can differ from the true population value due to sampling error and non-sampling error. Bias occurs when the expected value of the estimate differs from the true parameter value.
2015_DSSG_high_school_Poster
This work uses machine learning models to predict whether students will graduate high school on time based on their longitudinal student records. The models generate risk scores and identify key risk factors for struggling students. These predictions help schools target interventions to improve students' academic trajectories and increase graduation rates. Evaluation shows the models predict off-track students with high precision, especially when predicting further in advance. This helps schools focus intervention efforts.
week 5.pptx
This document outlines the content of Module 3 of an analytical chemistry course, which covers inferential statistics. It includes lectures and practical computer sessions on confidence intervals, hypothesis testing, and statistical tests involving single and multiple samples. Students will learn about calculating confidence intervals for population means and variances, performing one-sample z-tests and t-tests, and using statistical tests like the t-test, paired t-test, and F-test for two samples. The module concludes with a midterm exam and recommends textbooks for further reading on introductory statistics and chemometrics. As homework, students will complete exercises applying these statistical concepts to practical chemistry problems involving confidence intervals, hypothesis testing, and error analysis.
Lecture 5: Interval Estimation
inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,
Uop qnt 565 final exam guide 2 new
This document provides an overview of a 30 question multiple choice exam on quantitative research methods. It covers topics such as research design, measurement, sampling, descriptive and inferential statistics, and interpreting results. The exam focuses on applying research methodology concepts to analyze business situations and problems.
Uop qnt 565 final exam guide 2 new
qnt 565,uop qnt 565,uop qnt 565 complete course,uop qnt 565 entire course,uop qnt 565 week 2,uop qnt 565 week 3,uop qnt 565 week 4,uop qnt 565 week 5,uop qnt 565 week 6,qnt 565 final exam guide 1 new,qnt 565 final exam guide 2 new,qnt 565 tutorials,qnt 565 assignments,qnt 565 help
STAT 200 Final ExaminationFall 2016 OL1US1Page 9 of 9STAT 200.docx
STAT 200 Final ExaminationFall 2016 OL1/US1Page 9 of 9
STAT 200 Introduction to Statistics Name______________________________
Final Examination: Fall 2016 OL1/US1 Instructor __________________________
Answer Sheet
Instructions:
This is an open-book exam. You may refer to your text and other course materials as you work on the exam, and you may use a calculator.
Record your answers and work in this document.
Answer all 20 questions. Make sure your answers are as complete as possible. Show all of your work and reasoning. In particular, when there are calculations involved, you must show how you come up with your answers with critical work and/or necessary tables. Answers that come straight from calculators, programs or software packages without explanation will not be accepted. If you need to use technology to aid in your calculation, you have to cite the source and explain how you get the results. For example, state the Excel function along with the required parameters when using Excel; describe the detailed steps when using a hand-held calculator; or provide the URL and detailed steps when using an online calculator, and so on.
Show all supporting work and write all answers in the spaces allotted on the following pages. You may type your work using plain-text formatting or an equation editor, or you may hand-write your work and scan it. In either case, show work neatly and correctly, following standard mathematical conventions. Each step should follow clearly and completely from the previous step. If necessary, you may attach extra pages.
You must complete the exam individually. Neither collaboration nor consultation with others is allowed. It is a violation of the UMUC Academic Dishonesty and Plagiarism policy to use unauthorized materials or work from others. Your exam will receive a zero grade unless you complete the following honor statement.
Please sign (or type) your name below the following honor statement:
I understand that it is a violation of the UMUC Academic Dishonesty and Plagiarism policy to use unauthorized materials or work from others. I promise that I did not discuss any aspect of this exam with anyone other than my instructor. I further promise that I neither gave nor received any unauthorized assistance on this exam, and that the work presented herein is entirely my own.
Name _____________________Date___________________
Record your answers and work.
Problem Number
Solution
1
Answer:
(a)
(b)
(c)
(d)
(e)
Justification:
2
Answer:
(a)
(b)
(c)
Justification:
3
Answer:
(a)
(b)
Justification:
4
Answer:
(a)
IQ Scores
Frequency
Relative Frequency
50 - 69
23
70 - 89
249
90 -109
0.450
110 - 129
130 - 149
25
Total
1000
(b)
(c)
Work for (a) and (b):
5
Answer:
(a)
(b)
(c)
Justification:
6
Answer:
(a)
(b)
Work for (a) and (b):
7
Answer:
(a)
(b)
Work for (b):
8
Answer:
(.
Sampling
This document provides an overview of key concepts in sampling, including population, sample, sampling frame, probability sampling, and non-probability sampling. It discusses the qualities of a probability sample, including how findings from a random sample can be generalized to the population. It also covers sample size considerations and different types of error in sampling, such as sampling error and non-sampling error.
Question 1 of 23 1.0 Points The chi-square goodness-of-f.docx
Question 1 of 23
1.0 Points
The chi-square goodness-of-fit test can be used to test for:
A.difference between population means
B.normality
C.significance of sample statistics
D.difference between population variances
Reset Selection
Question 2 of 23
1.0 Points
The data presented in the table below resulted from an experiment in which seeds of 5 different types were
planted and the number of seeds that germinated within 5 weeks after planting was recorded for each
seed type. At the .01 level of significance, is the proportion of seeds that germinate dependent on the
seed type?
Seed Type
Observed Frequencies
Germinated Failed to Germinate
1 31 7
2 57 33
3 87 60
4 52 44
5 10 19
A.Yes, because the test value 16.86 is greater than the critical value of 13.28
B.Yes, because the test value 16.86 is less than the critical value of 14.86
C.No, because the test value 16.86 is greater than the critical value of 13.28
D.No, because the test value 13.28 is less than the critical value of 16.86
Reset Selection
Part 2 of 16 -
https://edge.apus.edu/portal/tool/e0599bbb-37e1-42a1-b2bf-368ce3fe543c/jsf/delivery/beginTakingAssessment
https://edge.apus.edu/portal/tool/e0599bbb-37e1-42a1-b2bf-368ce3fe543c/jsf/delivery/beginTakingAssessment
Question 3 of 23
1.0 Points
In a simple linear regression analysis, the following sum of squares are produced:
= 400
= 80
= 320
The proportion of the variation in Y that is explained by the variation in X is:
A.80%
B.25%
C.20%
D.50%
Reset Selection
Question 4 of 23
1.0 Points
The marketing manager of a large supermarket chain would like to use shelf space to predict the sales of
pet food. For a random sample of 12 similar stores, she gathered the following information regarding the
shelf space, in feet, devoted to pet food and the weekly sales in hundreds of dollars. .
Store 1 2 3 4 5 6
Shelf Space 5 5 5 10 10 10
Weekly Sales 1.6 2.2 1.4 1.9 2.4 2.6
Store 7 8 9 10 11 12
Shelf Space 15 15 15 20 20 20
Weekly Sales 2.3 2.7 2.8 2.6 2.9 3.1
Compute the value of the sample correlation coefficient between weekly sales and shelf space.
A.0.308
https://edge.apus.edu/portal/tool/e0599bbb-37e1-42a1-b2bf-368ce3fe543c/jsf/delivery/beginTakingAssessment
B.0.827
C.0.652
D.0.684
Reset Selection
Question 5 of 23
1.0 Points
In regression analysis, the variable we are trying to explain or predict is called the
A.regression variable
B.independent variable
C.dependent variable
D.residual variable
Reset Selection
Part 3 of 16 -
Question 6 of 23
1.0 Points
An investor wants to compare the risks associated with two different stocks. One way to measure the risk
of a given stock is to measure the variation in the stock’s daily price changes.
In an effort to test the claim that the variance in the daily stock price changes for stock 1 is different from
the varian ...
Diff rel gof-fit - jejit - practice (5)
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
Learn About Range - Copyright updated
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
More Related Content
Similar to 17 multiple-linear regression
X18125514 ca2-statisticsfor dataanalytics
4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS
Discovering Statistics Using IBM SPSS Statistics 4th Edition Field Test Bank
Full download http://alibabadownload.com/product/discovering-statistics-using-ibm-spss-statistics-4th-edition-field-test-bank/
Discovering Statistics Using IBM SPSS Statistics 4th Edition Field Test Bank
C0364010013
This document discusses predictive models that can be used to understand student performance in post-graduate entrance examinations. It proposes using data mining techniques like cluster analysis, classification, item analysis, and regression analysis on past student exam scores to identify strengths and weaknesses among different student groups. A probabilistic model is also described for devising multiple choice questions based on the probability that students with different abilities will correctly answer questions of varying difficulty levels. The goal is to develop a balanced exam paper that limits random guessing and accurately tests student aptitude.
Cognitive Abilities Test Parent Presentation
The document summarizes information about the Cognitive Abilities Test (CogAT) administered to third grade students in the Wake County Public School System. It explains that the CogAT measures verbal, quantitative, and nonverbal reasoning and provides scores to help with educational planning, including identification for gifted programs. National age scores are provided on scales from 1 to 9 and as percentiles. The student's profile code indicates their pattern of strengths and weaknesses across the three sections. Scores at or above the 85th percentile make students eligible for further gifted testing using the Iowa Test of Basic Skills.
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent variable that we are most interested in describing are:
a.
frequency and average.
b.
average and correlation.
c.
central tendency and dispersion.
d.
histograms and polygons.
2. The ________________ is the sum of all the scores divided by the number of scores.
a.
median
b.
mean
c.
mode
d.
standard deviation
3. The generally preferred measure of central tendency is usually the
a.
range
b.
mean
c.
standard deviation
d.
Median
4. Which of the following is the most useful descriptive statistic for measuring dispersion?
a.
Range
b.
Variance
c.
mean deviation
d.
standard deviation
5. The standard deviation is
a.
the square of the variance.
b.
the square root of the variance.
c.
smaller than the mean.
d.
the difference between the highest and lowest scores.
6. If the mean I.Q. is 100 and the standard deviation of I.Q. scores is 15, then an I.Q. of 130 will have a z score (or standard score) of
a.
1.00
b.
0.00
c.
2.00
d.
-2.00
7. Inferential statistics allow you to decide whether a difference between the experimental and the control group is due to _______________ or ________________.
a.
manipulation; chance
b.
manipulation; experimental error
c.
sampling error; independent variable
d.
independent variable; experimental error
8. The null hypothesis suggests that the two samples come from ___________ distribution(s), and the experimental hypothesis suggests that the two samples come from _____________ distribution(s).
a.
different; different
b.
different; the same
c.
the same; different
d.
the same; the same
9. The power of a statistical test refers to its ability to
a.
reject false null hypotheses.
b.
reject false experimental hypotheses.
c.
reject true null hypotheses.
d.
reject true experimental hypotheses.
10. Simple analysis of variance is used in designs having
a.
one independent variable
b.
more than one independent variable
c.
more than one independent variable (IV) but less than four IVs
d.
more than one dependent variable
11. The number of participants in a study is denoted by
a.
s.
b.
n.
c.
z.
d.
r.
12. A _____________ is a complete set of measurements.
a.
sample
b.
population
c.
random sampling
d.
parameter
13. _____________ is one way of ensuring that a sample is representative of the population.
a.
The two-tailed test
b.
The between-subjects design
c.
The sign test
d.
Random sampling
14. If we conduct an experiment on average young, white, college males, inferential statistics allow us to generalize to the population of
a.
average young, white, college males.
b.
college male students.
c.
college students.
d.
young adults.
15. If we apply an alpha level of .05, and there really is no effect of the experimental manipulation, then one should make a Type I error
a.
5% of the time.
b.
10% of the time.
c.
15% of the time.
d.
95% of the time.
16. Which of the following would be considered the most conservative alpha level ...
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent variable that we are most interested in describing are:
a.
frequency and average.
b.
average and correlation.
c.
central tendency and dispersion.
d.
histograms and polygons.
2. The ________________ is the sum of all the scores divided by the number of scores.
a.
median
b.
mean
c.
mode
d.
standard deviation
3. The generally preferred measure of central tendency is usually the
a.
range
b.
mean
c.
standard deviation
d.
Median
4. Which of the following is the most useful descriptive statistic for measuring dispersion?
a.
Range
b.
Variance
c.
mean deviation
d.
standard deviation
5. The standard deviation is
a.
the square of the variance.
b.
the square root of the variance.
c.
smaller than the mean.
d.
the difference between the highest and lowest scores.
6. If the mean I.Q. is 100 and the standard deviation of I.Q. scores is 15, then an I.Q. of 130 will have a z score (or standard score) of
a.
1.00
b.
0.00
c.
2.00
d.
-2.00
7. Inferential statistics allow you to decide whether a difference between the experimental and the control group is due to _______________ or ________________.
a.
manipulation; chance
b.
manipulation; experimental error
c.
sampling error; independent variable
d.
independent variable; experimental error
8. The null hypothesis suggests that the two samples come from ___________ distribution(s), and the experimental hypothesis suggests that the two samples come from _____________ distribution(s).
a.
different; different
b.
different; the same
c.
the same; different
d.
the same; the same
9. The power of a statistical test refers to its ability to
a.
reject false null hypotheses.
b.
reject false experimental hypotheses.
c.
reject true null hypotheses.
d.
reject true experimental hypotheses.
10. Simple analysis of variance is used in designs having
a.
one independent variable
b.
more than one independent variable
c.
more than one independent variable (IV) but less than four IVs
d.
more than one dependent variable
11. The number of participants in a study is denoted by
a.
s.
b.
n.
c.
z.
d.
r.
12. A _____________ is a complete set of measurements.
a.
sample
b.
population
c.
random sampling
d.
parameter
13. _____________ is one way of ensuring that a sample is representative of the population.
a.
The two-tailed test
b.
The between-subjects design
c.
The sign test
d.
Random sampling
14. If we conduct an experiment on average young, white, college males, inferential statistics allow us to generalize to the population of
a.
average young, white, college males.
b.
college male students.
c.
college students.
d.
young adults.
15. If we apply an alpha level of .05, and there really is no effect of the experimental manipulation, then one should make a Type I error
a.
5% of the time.
b.
10% of the time.
c.
15% of the time.
d.
95% of the time.
16. Which of the following would be considered the most conservative alpha level ...
multinomial-pdf.pdf
Multinomial logistic regression is an extension of binary logistic regression for outcomes with more than two unordered categories. It models the probabilities of the different categories using maximum likelihood estimation. Some examples of multinomial outcomes include blood type or candidate choice. The assumptions include no multicollinearity, no outliers, independence of observations, and linearity of relationships. The results are k-1 logistic regression models, where k is the number of categories. Interpretation includes pseudo R-square, p-values, and odds ratios. Resources for practicing on different datasets were also provided.
Alumni Donation - Complete exploration and analysis report
This study investigates the factors that influence alumni donations. We use exploratory data analysis and multi factor linear regression techniques to predict alumni giving rate. After analyzing relationships between the variables, we fit linear regression models and perform residual diagnosis to ensure that assumptions of linear regression are not violated. We find that lower student-faculty ratio leads to higher alumni donations and smaller class size is insignificant in predicting alumni donations. We include an additional parameter graduation rate, which is highly correlated with alumni giving rate and interaction between student-faculty ratio and dummy variable private in our final model. The final mode has an adjusted R-squared of 75.76%.
Regression techniques to study the student performance in post graduate exam...
This document discusses using linear regression techniques to analyze student performance on post-graduate entrance exams in Karnataka, India. Specifically, it uses a sample of 1,000 student records to model entrance exam scores (dependent variable) based on undergraduate degree scores (independent variable). The analysis finds a perfect correlation between the two sets of scores, likely because the degree scores were derived from entrance scores. The document concludes that regression can help categorize student groups and inform question selection to better test aptitude on entrance exams.
ECO 480 Econometrics I Problem Set 5 Due Monday, Nove.docx
ECO 480 Econometrics I
Problem Set 5
Due: Monday, November 30, 2015 (beginning of the class)
1
Instruction: The problem sets are designed to be difficult and very time-intensive, so plan ahead. The
problem sets consists of solving theoretical problems and analyzing real data. You may discuss the
questions with your classmates, but you are required to hand in your own independently written solutions,
do-files, and log-files. No late work will be accepted and I do NOT accept any electronic copy. All the
data necessary for the problem set is available under UBlearns.
Important: It is extremely important to write a clean well-commented program for transparency and
replication purposes. In any empirical work, you should always be able to reproduce your result from raw
data to support your claim.
What to hand in: Typed write-up answering the assigned questions and interpreting your findings, do-file,
and log-file (you MUST use Stata). For questions involving data analysis, you will NOT get any credit if
you do not provide a program code. You may NOT use Excel.
1. How are returns on common stocks in overseas markets related to returns in U.S. markets? Consider
measuring U.S. returns by the annual rate of returns on the Standard & Poor’s (S&P) 500 stock index
and overseas returns by the annual rate of returns on the Morgan Stanley Europe, Australia, Asia, Far
East (EAFE) index. Both are recorded in percent. Regressing the EAFE returns on the S&P 500
returns for the 20 years 1989 to 2008. Here is part of the output for this regression:
The regression equation is EAFE = -2.58 + 0.775 S&P
Analysis of Variance
Source df SS MS
Model 4560.6
Residual
Total 19 8556.0
a. Complete the analysis of variance table by filling in the missing entries. Show (Justify) how you
came up with that answer.
b. What are the values of the regression standard error and the R2?
c. Find the standard error for the least-squares slope. Show your work.
d. If I tell you that the standard deviation of the S&P 500 returns for these years is 19.99%. How
would you use this information to find the standard error for the least-squares slope you found in
part c?
e. Give a 95% confidence interval for the slope β1 of the population regression line. Show your
work.
ECO 480 Econometrics I
Problem Set 5
Due: Monday, November 30, 2015 (beginning of the class)
2
2. An electronic survey of 7,061 players of Guitar Hero and Rock Band reported that 67% of those who
do not currently play a musical instrument said that they are likely to begin playing a real musical
instrument in the next two years. The reports describing the survey do not give the number of
respondents who do not currently play a musical instrument.
a. Explain why it is important to know the number of respondents who do not currently play a
musical instrument.
b. Assume that half of the respondents do not curre ...
Lecture 6 Point and Interval Estimation.pptx
Point and Interval Estimation:
Objectives:
Apply the basics of inferential statistics in terms of point estimation.
Compute point and estimation of population means and confidence interval.
Interpret the results of point and interval estimation.
Estimation:
Estimating the value of parameter from the sample:
An aspect of inferential statistics.
Why to estimate: Population is large enough so we
can only estimate.
Types of estimation:
Point Estimation:
A specified number value (single value) that is an estimate of a population parameter. The point estimate of the population mean µ is the sample mean.
Interval Estimate:
Range of values to estimate about population parameter.
Confidence Interval Estimation:
Range of values to estimate about population parameter.
May contain the parameter or not (Degree of confidence).
Ranges between two values.
Example:
Age (in years) 4 BScN students: 20<µ < 25 or (22.5 +2.5)
FORMULA:
Point estimate (x) + Critical Value x Standard Error.
Confidence Interval is a particular interval of estimate.
Given that sample size is large, the 95% of the sample means taken from same population and same sample size will fall in + 1.96 SD of the population mean.
Three commonly used Confidence Intervals are 90%, 95% (by default) , and 99%.
Why not too small or too large confidence intervals?
Too wide: 99.9% Interval too broad
Too narrow: 80 % More uncertainty to have population mean.
The 99% of the sample means taken from same population and
same sample size will fall in + 2.575 SD of the population mean.
Interpretation:
99% probability that interval will enclose population parameter and 1% chance that it will not have population parameter.
Level of confidence: The level of certainty that the interval will have the true population mean.
Chances of Error: Chances that the interval will not cater the true parameter.
Sum of level of confidence and chances of error =100%
LR 9 Estimation.pdf
The document provides an overview of key concepts related to estimation in statistics, including:
- Estimation involves using sample data to estimate unknown population parameters. Common estimators include the sample mean, proportion, and standard deviation.
- There are two main types of estimates - point estimates and interval estimates. Point estimates are single values while interval estimates specify a range.
- The process of estimation involves identifying the parameter, selecting a random sample, choosing an estimator, and calculating the estimate.
- Estimates can differ from the true population value due to sampling error and non-sampling error. Bias occurs when the expected value of the estimate differs from the true parameter value.
2015_DSSG_high_school_Poster
This work uses machine learning models to predict whether students will graduate high school on time based on their longitudinal student records. The models generate risk scores and identify key risk factors for struggling students. These predictions help schools target interventions to improve students' academic trajectories and increase graduation rates. Evaluation shows the models predict off-track students with high precision, especially when predicting further in advance. This helps schools focus intervention efforts.
week 5.pptx
This document outlines the content of Module 3 of an analytical chemistry course, which covers inferential statistics. It includes lectures and practical computer sessions on confidence intervals, hypothesis testing, and statistical tests involving single and multiple samples. Students will learn about calculating confidence intervals for population means and variances, performing one-sample z-tests and t-tests, and using statistical tests like the t-test, paired t-test, and F-test for two samples. The module concludes with a midterm exam and recommends textbooks for further reading on introductory statistics and chemometrics. As homework, students will complete exercises applying these statistical concepts to practical chemistry problems involving confidence intervals, hypothesis testing, and error analysis.
Lecture 5: Interval Estimation
inferential statistics, statistical inference, language technology, interval estimation, confidence interval, standard error, confidence level, z critical value, confidence interval for proportion, confidence interval for the mean, multiplier,
Uop qnt 565 final exam guide 2 new
This document provides an overview of a 30 question multiple choice exam on quantitative research methods. It covers topics such as research design, measurement, sampling, descriptive and inferential statistics, and interpreting results. The exam focuses on applying research methodology concepts to analyze business situations and problems.
Uop qnt 565 final exam guide 2 new
qnt 565,uop qnt 565,uop qnt 565 complete course,uop qnt 565 entire course,uop qnt 565 week 2,uop qnt 565 week 3,uop qnt 565 week 4,uop qnt 565 week 5,uop qnt 565 week 6,qnt 565 final exam guide 1 new,qnt 565 final exam guide 2 new,qnt 565 tutorials,qnt 565 assignments,qnt 565 help
STAT 200 Final ExaminationFall 2016 OL1US1Page 9 of 9STAT 200.docx
STAT 200 Final ExaminationFall 2016 OL1/US1Page 9 of 9
STAT 200 Introduction to Statistics Name______________________________
Final Examination: Fall 2016 OL1/US1 Instructor __________________________
Answer Sheet
Instructions:
This is an open-book exam. You may refer to your text and other course materials as you work on the exam, and you may use a calculator.
Record your answers and work in this document.
Answer all 20 questions. Make sure your answers are as complete as possible. Show all of your work and reasoning. In particular, when there are calculations involved, you must show how you come up with your answers with critical work and/or necessary tables. Answers that come straight from calculators, programs or software packages without explanation will not be accepted. If you need to use technology to aid in your calculation, you have to cite the source and explain how you get the results. For example, state the Excel function along with the required parameters when using Excel; describe the detailed steps when using a hand-held calculator; or provide the URL and detailed steps when using an online calculator, and so on.
Show all supporting work and write all answers in the spaces allotted on the following pages. You may type your work using plain-text formatting or an equation editor, or you may hand-write your work and scan it. In either case, show work neatly and correctly, following standard mathematical conventions. Each step should follow clearly and completely from the previous step. If necessary, you may attach extra pages.
You must complete the exam individually. Neither collaboration nor consultation with others is allowed. It is a violation of the UMUC Academic Dishonesty and Plagiarism policy to use unauthorized materials or work from others. Your exam will receive a zero grade unless you complete the following honor statement.
Please sign (or type) your name below the following honor statement:
I understand that it is a violation of the UMUC Academic Dishonesty and Plagiarism policy to use unauthorized materials or work from others. I promise that I did not discuss any aspect of this exam with anyone other than my instructor. I further promise that I neither gave nor received any unauthorized assistance on this exam, and that the work presented herein is entirely my own.
Name _____________________Date___________________
Record your answers and work.
Problem Number
Solution
1
Answer:
(a)
(b)
(c)
(d)
(e)
Justification:
2
Answer:
(a)
(b)
(c)
Justification:
3
Answer:
(a)
(b)
Justification:
4
Answer:
(a)
IQ Scores
Frequency
Relative Frequency
50 - 69
23
70 - 89
249
90 -109
0.450
110 - 129
130 - 149
25
Total
1000
(b)
(c)
Work for (a) and (b):
5
Answer:
(a)
(b)
(c)
Justification:
6
Answer:
(a)
(b)
Work for (a) and (b):
7
Answer:
(a)
(b)
Work for (b):
8
Answer:
(.
Sampling
This document provides an overview of key concepts in sampling, including population, sample, sampling frame, probability sampling, and non-probability sampling. It discusses the qualities of a probability sample, including how findings from a random sample can be generalized to the population. It also covers sample size considerations and different types of error in sampling, such as sampling error and non-sampling error.
Question 1 of 23 1.0 Points The chi-square goodness-of-f.docx
Question 1 of 23
1.0 Points
The chi-square goodness-of-fit test can be used to test for:
A.difference between population means
B.normality
C.significance of sample statistics
D.difference between population variances
Reset Selection
Question 2 of 23
1.0 Points
The data presented in the table below resulted from an experiment in which seeds of 5 different types were
planted and the number of seeds that germinated within 5 weeks after planting was recorded for each
seed type. At the .01 level of significance, is the proportion of seeds that germinate dependent on the
seed type?
Seed Type
Observed Frequencies
Germinated Failed to Germinate
1 31 7
2 57 33
3 87 60
4 52 44
5 10 19
A.Yes, because the test value 16.86 is greater than the critical value of 13.28
B.Yes, because the test value 16.86 is less than the critical value of 14.86
C.No, because the test value 16.86 is greater than the critical value of 13.28
D.No, because the test value 13.28 is less than the critical value of 16.86
Reset Selection
Part 2 of 16 -
https://edge.apus.edu/portal/tool/e0599bbb-37e1-42a1-b2bf-368ce3fe543c/jsf/delivery/beginTakingAssessment
https://edge.apus.edu/portal/tool/e0599bbb-37e1-42a1-b2bf-368ce3fe543c/jsf/delivery/beginTakingAssessment
Question 3 of 23
1.0 Points
In a simple linear regression analysis, the following sum of squares are produced:
= 400
= 80
= 320
The proportion of the variation in Y that is explained by the variation in X is:
A.80%
B.25%
C.20%
D.50%
Reset Selection
Question 4 of 23
1.0 Points
The marketing manager of a large supermarket chain would like to use shelf space to predict the sales of
pet food. For a random sample of 12 similar stores, she gathered the following information regarding the
shelf space, in feet, devoted to pet food and the weekly sales in hundreds of dollars. .
Store 1 2 3 4 5 6
Shelf Space 5 5 5 10 10 10
Weekly Sales 1.6 2.2 1.4 1.9 2.4 2.6
Store 7 8 9 10 11 12
Shelf Space 15 15 15 20 20 20
Weekly Sales 2.3 2.7 2.8 2.6 2.9 3.1
Compute the value of the sample correlation coefficient between weekly sales and shelf space.
A.0.308
https://edge.apus.edu/portal/tool/e0599bbb-37e1-42a1-b2bf-368ce3fe543c/jsf/delivery/beginTakingAssessment
B.0.827
C.0.652
D.0.684
Reset Selection
Question 5 of 23
1.0 Points
In regression analysis, the variable we are trying to explain or predict is called the
A.regression variable
B.independent variable
C.dependent variable
D.residual variable
Reset Selection
Part 3 of 16 -
Question 6 of 23
1.0 Points
An investor wants to compare the risks associated with two different stocks. One way to measure the risk
of a given stock is to measure the variation in the stock’s daily price changes.
In an effort to test the claim that the variance in the daily stock price changes for stock 1 is different from
the varian ...
Similar to 17 multiple-linear regression (20)
Discovering Statistics Using IBM SPSS Statistics 4th Edition Field Test Bank
Discovering Statistics Using IBM SPSS Statistics 4th Edition Field Test Bank
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent v
1. The two properties about a set of measurements of a dependent v
Alumni Donation - Complete exploration and analysis report
Alumni Donation - Complete exploration and analysis report
Regression techniques to study the student performance in post graduate exam...
Regression techniques to study the student performance in post graduate exam...
ECO 480 Econometrics I Problem Set 5 Due Monday, Nove.docx
ECO 480 Econometrics I Problem Set 5 Due Monday, Nove.docx
STAT 200 Final ExaminationFall 2016 OL1US1Page 9 of 9STAT 200.docx
STAT 200 Final ExaminationFall 2016 OL1US1Page 9 of 9STAT 200.docx
Question 1 of 23 1.0 Points The chi-square goodness-of-f.docx
Question 1 of 23 1.0 Points The chi-square goodness-of-f.docx
More from Ken Plummer
Diff rel gof-fit - jejit - practice (5)
The document discusses the differences between questions of difference, relationship, and goodness of fit. It provides examples to illustrate each type of question. A question of difference compares two or more groups on some outcome, like comparing younger and older drivers' average driving speeds. A question of relationship examines whether a change in one variable causes a change in another, such as the relationship between age and flexibility. A question of goodness of fit assesses how well a claim matches reality, such as whether a salesman's claim of software effectiveness fits the results of user testing.
Learn About Range - Copyright updated
This document provides examples of questions that ask for the lowest and highest number in a set of data. The questions ask for the difference between the state with the lowest and highest church attendance, the students with the highest and lowest test scores, and the slowest and fastest versions of a vehicle model.
Inferential vs descriptive tutorial of when to use - Copyright Updated
The document discusses the differences between descriptive and inferential statistics. Descriptive statistics are used to describe characteristics of a whole population, while inferential statistics are used when the whole population cannot be measured and conclusions are drawn from a sample to generalize to the larger population. Examples are provided to illustrate when each type of statistic would be used. Key differences include descriptive statistics examining entire populations while inferential statistics examine samples that aim to infer conclusions about populations.
Diff rel ind-fit practice - Copyright Updated
The document provides explanations and examples for different types of statistical questions:
- Difference questions compare two or more groups on an outcome.
- Relationship questions examine if a change in one variable is associated with a change in another variable.
- Independence questions determine if two variables with multiple levels are independent of each other.
- Goodness of fit questions assess how well a claim matches reality.
Examples are given for each type of question to illustrate key concepts like comparing groups, examining associations between variables, assessing independence, and evaluating how a claim fits observed data.
Normal or skewed distributions (inferential) - Copyright updated
- The document discusses determining whether distributions are normal or skewed
- A distribution is considered skewed if the skewness value divided by the standard error of skewness is less than -2 or greater than 2
- For the old car data set in the example, the skewness value of -4.26 divided by the standard error is less than -2, so this distribution is negatively skewed
- The new car data set skewness value of -1.69 divided by the standard error is between -2 and 2, so this distribution is normal
Normal or skewed distributions (descriptive both2) - Copyright updated
The document discusses normal and skewed distributions and how to identify them. It provides examples of measuring forearm circumference of golf players and IQs of cats and dogs. The forearm circumference data is normally distributed while the dog IQ data is left skewed based on the skewness statistics provided. Therefore, at least one of the distributions (dog IQs) is skewed.
Nature of the data practice - Copyright updated
The document discusses different types of data:
- Scaled data provides exact amounts like 12.5 feet or 140 miles per hour.
- Ordinal or ranked data provides comparative amounts like 1st, 2nd, 3rd place.
- Nominal data names or categorizes values like Republican or Democrat.
- Nominal proportional data are simply percentages like Republican 45% or Democrat 55%.
Nature of the data (spread) - Copyright updated
The document discusses scaled and ordinal data. Scaled data can be measured in exact amounts like distances and speeds. Ordinal data provides comparative amounts by ranking items, like the top 3 states in terms of well-being. Examples ask the reader to identify if data is scaled or ordinal, like driving speeds which are scaled, or baby weight percentiles which are ordinal as they compare weights.
Mode practice 1 - Copyright updated
The document is a series of questions and examples that explain what it means for a question to ask about the "most frequent response". It provides examples of questions asking about the highest/most number of something based on data in tables or lists. It then asks a series of questions to determine if they are asking about the most frequent/common response based on the data given.
Nature of the data (descriptive) - Copyright updated
The document discusses two types of data: scaled data and ordinal data. Scaled data can be measured in exact amounts with equal intervals between values. Ordinal or ranked data provides comparative amounts but not necessarily equal intervals. Several examples are provided to illustrate the difference, including driving speed, states ranked by well-being, and elephant weights. Practice questions are also included for the reader to determine if data examples provided are scaled or ordinal.
Dichotomous or scaled
The document discusses whether variables are dichotomous or scaled when calculating correlations. It provides examples of correlations between ACT scores and whether students attended private or public school. One example has ACT scores as a scaled variable and school type as dichotomous. Another has lower and higher ACT scores as dichotomous and school type as dichotomous. It emphasizes determining if variables are both dichotomous, or if one is dichotomous and one is scaled.
Skewed less than 30 (ties)
The document discusses the correlation between ACT scores and a measure of school belongingness. It determines that one of the variables, which has a sample size less than 30, is skewed and has many ties. As a result, a non-parametric test should be used to analyze the relationship between the two variables.
Skewed sample size less than 30
The document discusses using parametric versus non-parametric tests based on sample size for skewed distributions. For skewed distributions with a sample size less than 30, a non-parametric test is recommended. For skewed distributions with a sample size greater than or equal to 30, a parametric test is recommended. It provides examples analyzing the correlation between ACT scores and sense of school belongingness using both approaches.
Ordinal (ties)
The document discusses whether there are many ties or few/no ties within the variables of the relationship question "What is the correlation between ACT rankings (ordinal) and sense of school belongingness (scaled 1-10)?". It determines that ACT rankings, being ordinal, have many ties, while sense of school belongingness, being on a scale of 1-10, may have many or few ties depending on how scores are distributed.
Ordinal and nominal
The document discusses identifying whether variables in statistical analyses are ordinal or nominal. It provides examples of relationships between variables such as ACT rankings and sense of school belongingness, daily social media use and sense of well-being, and private/public school enrollment and sense of well-being. It asks the reader to identify if variables in examples like running speed and shoe/foot size or LSAT scores and test anxiety are ordinal or nominal.
Relationship covariates
The document discusses covariates and their impact on relationships between variables. It defines a covariate as a variable that is controlled for or eliminated from a study. It explains that if a covariate is related to one of the variables in the relationship being examined, it can impact the strength of that relationship. Examples are provided to demonstrate when a question involves a covariate or not.
Relationship nature of data
This document discusses the nature of variables in relationship questions. It can be determined that the variables are either both scaled, at least one is ordinal, or at least one is nominal. Examples of different relationship questions are provided that fall into each of these categories. The document also provides practice questions for the user to determine which category the variables fall into.
Number of variables (predictive)
The document discusses the number of variables involved in research questions. It explains that many relationship questions deal with two variables, such as gender predicting driving speed. However, some questions deal with three or more variables, for example gender and age predicting driving speed. The document asks the reader to identify whether example research questions involve two or three or more variables.
Levels of the iv
The document discusses independent and dependent variables in research questions. It provides examples to illustrate that an independent variable has at least two levels and may have more, such as religious affiliation having two levels (Western religion and Eastern religion) or company type having three levels (Company X, Company Y, Company Z). It then provides a practice example about employee satisfaction rates among morning, afternoon, and evening shifts, identifying shift status as the independent variable with three levels.
Independent variables (2)
The document discusses independent variables and how they relate to research questions. It provides examples of questions with one independent variable, two independent variables, and zero independent variables. An independent variable influences or impacts a dependent variable. Questions are presented about employee satisfaction rates, agent commissions, training proficiency, and cyberbullying incidents to illustrate different numbers of independent variables.
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Inferential vs descriptive tutorial of when to use - Copyright Updated
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Normal or skewed distributions (inferential) - Copyright updated
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more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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17 multiple-linear regression
- 1. Running & Reporting a Multiple Linear Regression in SPSS - Example The Study: Are the number of cyberbullying incidents a student experiences and there absenteeism significant predictors of self-esteem scores? Decision Path: Inferential / Relationship / Predictive / Three Variables= Multiple-Linear Regression The Hypothesis: GPA and years in academic clubs are significant predictors of ACT scores. The Null-hypothesis: GPA and years in academic clubs are NOT significant predictors of ACT scores. Question: Do we have enough evidence to reject the null hypothesis? The Decisionrule: If the probability that we are wrong is .05 or 5 out of 100 times we will reject the null-hypothesis or in other words, accept the hypothesis.
- 9. Results ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 25.272 2 12.636 58.669 .000b Residual 38.122 177 .215 Total 63.394 179 a. DependentVariable:Absenteeism b. Predictors:(Constant),Self_Esteem_Index, Cyberbullying_incidents Report: Based on the results of the study, the number of cyberbullying incidents experienced by students and their self-esteem index scores are statistically significant predictors of absenteeism F(2) = 58.669 p = .000.
- 10. Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) -.712 .267 -2.673 .008 Cyberbullying_incidents .167 .030 .398 5.550 .000 Self_Esteem_Index .024 .006 .311 4.341 .000 a. Dependent Variable:Absenteeism The undstandardized coefficients in the regression equation were each statistically significant: constant p = .008, regression coefficient for cyberbullying p = .000, and regression coefficient for self-esteem index scores p = .000. Therefore, student’s predicted absenteeism are equal to -.712 + .167 (incidents of cyberbullying) + .024 (self-esteem). Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .631a .399 .392 .464 a. Predictors:(Constant), Self_Esteem_Index,Cyberbullying_incidents 39.2% of the variance in absenteeism are explained by the variance in cyberbullying incidents and self-esteem scores.
- 11. Therefore, here are the final results: Based on the results of the study, the number of cyberbullying incidents experienced by students and their self-esteem index scores are statistically significant predictors of absenteeism F(2) = 58.669 p = .000. The undstandardized coefficients in the regression equation were each statistically significant: constant p = .008, regression coefficient for cyberbullying p = .000, and regression coefficient for self-esteem index scores p = .000. Therefore, student’s predicted absenteeism are equal to -.712 + .167 (incidents of cyberbullying) + .024 (self-esteem). 39.2% of the variance in absenteeism are explained by the variance in cyberbullying incidents and self-esteem scores.