Description
determine how the parabola has in common with the x-axis and whether its vertex lies above on or below the x-axis
y=4x^2-12x+12
User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.
Explanation & Answer
Answer:
here we can rewrite your quadratic function as below:
y = 4 (x-3/2)^2 +3
so the vertex is V = (3/2, 3)
that parabola doesn't intersect the x-axis
the discriminant is negative:
discriminant = 36 - 48 = -12
Completion Status:
100%
Review
Review
Anonymous
I was struggling with this subject, and this helped me a ton!
Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4
24/7 Homework Help
Stuck on a homework question? Our verified tutors can answer all questions, from basic math to advanced rocket science!
Most Popular Content
4 pages
Deliverable 03 Questions
1. Discuss the importance of constructing confidence intervals for the population mean by o What is the best point estimat ...
Deliverable 03 Questions
1. Discuss the importance of constructing confidence intervals for the population mean by o What is the best point estimate for the population mean? ...
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt ...
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt in table form:#2) For this dataset, what is our dependent variable? #3) How many independent variables do we have? #4) How many levels does each independent variable have (use the function unique(x) to check)? #5) Make a boxplot for this set of data (submit the image). What problem do you see?#6) What is our null hypothesis?#7) Now use the classic method to analyze this dataset using the format aov(x~factor(g)). # Save this as an object called hw1.anova. #NOTE: MAKE SURE TO USE factor() AROUND YOUR GROUPING VARIABLE SO IT IS TREATED AS A FACTOR, NOT AS A NUMERIC VARIABLE. # Then summarize these results using summary(hw1.anova). #8) Do we reject or do we fail to reject the null hypothesis?#9) Now let's use the t1way() function, which is based on trimmed means and can deal with heteroscedasticity.#Hint 1: First, reorganize your data using fac2list(x, g). Save your new list as hw1.list.#Hint 2: You will need to have loaded in the source code to use the t1way function.#10) Do we reject or do we fail to reject the null hypothesis from 1.9?----------------------------------------------------------------------------------------------------------------------------------------------------------Lab 10 lecture notes:#Lab 10#Lab 10-Contents#1. One-Way Independent Groups ANOVA (Equal Variance)#2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#---------------------------------------------------------------------------------# 1. One-Way Independent Groups ANOVA (Equal Variance)#--------------------------------------------------------------------------------- #Scenario for first exercise: # A professor is interested in the effect of visualization strategies#on test performance. In order to study this, he tells students in#his statistics class that they will have a 15 question exam in #two weeks. Then, he randomly assigns students to three groups. # # The first group is told to spend 15 min each day vizualizing #the outcome of getting an A on the test to vividly imagine #the exam with an "A" written on it and how great it will feel. # # The second group is a control group that does no visualization. ## The third group is told to spend 15 min each day visualizing#the process of studying for the exam: imagine the hours of studying,#reviewing their chapters, working through chapter problems, # quizzing themeselves, etc. # Two weeks later, the students take the exam and the professor # records how many questions the students answer correctly out of 15.#So, the groups are:#Group 1: Visualize Outcome (Grade)#Group 2: No visualization (Control)#Group 3: Visiualize Process (Studying)#######################################################Question: Are the groups here Independent?#######################################################We'll instroduce a few new terms: #Factor: A variable that consists of categories. #Levels: The categories of the Factor variable. #In our example above, the variable that contains#the groups is called "Group". #So, our factor is the variable "Group"#How many levels are there for the Group Factor?#Let's read in LAB10A.txtlab10a=read.table('LAB10A.txt', header=T)#While we can easily see the levels for the Group #factor we could also use a new command to figure out #the number of unique levels.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Number of Unique Levels: unique(data$variable)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#unique(lab10a$Group) #As we can see, there are 3 levels. 1, 2, and 3#Look at boxplot of each group using #boxplot(y~group, data=data)par(mfrow=c(1,1))boxplot(Score~Group, data=lab10a)#Do you think the means will be different (statistically)#between the groups?#Before we begin to test for differences between #the means, let's wrtie out our NUll #and Alternative Hyhpotheses#H0: The means are equal (mu1=mu2=mu3)#HA: At least one mean is different. #(eg. mu1 != mu2 OR mu1 != mu3 OR mu2 != mu3 )#To test the Hypothesis we can use the ANOVA function aov():#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## One-Way ANOVA: aov(y~factor(g), data)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##The aov() function assumes that the #variance is the same within each of the groups.mod1=aov(Score ~ factor(Group), data=lab10a)summary(mod1)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Do we Reject or Fail to Reject the Null?#Reject 0.00129 < .05 then Reject H0#What does this tell us? That the groups are different?#If so, how do we know which groups?#P-value we just got is called the Omnibus P-value, #which tells us that there are differences somewhere#With this P-value we often use the term #"Main Effect" to say that there is an effect of the#factor on the outcome.#In this instance we'd say that there is a Main Effect #of Group on the Score.#To Answer which groups are different, we need to first#conver the data into List Mode (a different way #of storing the data). We can convert the factor Group #to a list using the function fac2list(y, g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Convert Factors to List Data: fac2list(data$y, data$g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#listA=fac2list(lab10a$Score, lab10a$Group)listA #Once the data is in List Mode we have to use the#lincon() command from Dr. Wilcox's source code.#The lincon() package is used to compare the groups while#controlling for the experimentwise Type 1 error rate.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Compare Groups: lincon(list_name, tr=0.2)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##By default lincon() compares groups using 20% trimming. #We will set this to 0 for now:lincon(listA, tr=0) #result:# H0_1: mu1=mu2 --- p=0.32 ---Fail to reject# H0_2: mu1=mu3 --- p=0.0009 ---Reject# H0_3: mu2=mu3 --- p=0.008 ---Reject#---------------------------------------------------------------------------------# 2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#--------------------------------------------------------------------------------- # We just learned how to conduct a One-Way ANOVA # when the variances are equal within each group. # Now, we will learn how to conduct a One-Way ANOVA #for then the variance is not equal.# Let's start by reading in the LAB10B.txt datafile.lab10b=read.table('LAB10B.txt', header=T)# Then examine a boxplot of all of it.boxplot(Score~factor(Group), data=lab10b)# What do we notice about this boxplot?#-----# Let's start by running the equal variance ANOVA#on the data (which of course is WRONG!)mod2=aov(Score ~ factor(Group), data=lab10b) #---DON'Tsummary(mod2)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis# Do we Reject or Fail to Reject the Null?#Fail to reject: p-value=0.0895 > .05 !!!INCORRECT----#----# Now let's try to run the correct test that assumes #unequal variance. #We call this the Welch's test (just like in the t-test)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Welch's One-Way ANOVA: t1way(list_name, tr=0.20)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##In order to use this t1way function, #we will first need to convert the data to #List Mode using fac2list()listB=fac2list(lab10b$Score, lab10b$Group)t1way(listB, tr=0.2)# Do we Reject or Fail to Reject the Null?#Reject: p-value:0.04966583 <.05#Again, we can use the lincon() command to #find out Where the group differences are.#This time we will use the 20% trimming.lincon(listB, tr=0.2)# G1 and G2: p-value=0.92210409 > .05 Fail to reject# G1 and G3: p-value=0.19451518 > .05 Fail to reject#G2 and G3: p-value=0.03227316 < .05 Reject#
Week 5 – IA Analysis of Variance, statistics homework help
Week 5 – IA Analysis of Variance(OVERVIEW) (tohw) This Assignment has 3 parts (attached separately with this Overview ...
Week 5 – IA Analysis of Variance, statistics homework help
Week 5 – IA Analysis of Variance(OVERVIEW) (tohw) This Assignment has 3 parts (attached separately with this Overview), 1. Part 1. Module 5 Problem Set (Attached) 2. Part 2. Module 5 SPSS Data Interpretation 3. Part 3. Statistical Tests 4. DQs (Sent under separate attachment) Statistical Tests Doctoral researchers must be able to understand statistical tests and select the appropriate test for the research they are conducting. This assignment will allow you to practice your skills in selecting and using the appropriate statistical tests for a given research study. General Information Use the following information to ensure successful completion of the assignment: Researchers wanted to explore self-esteem in adolescent boys and adolescent girls. Each respondent completed a 10-item self-esteem scale (they chose one rating for each item from a Likert-type scale, 1 = strongly disagree and 5 = strongly agree). The sum of the 10 ratings was each respondent's self-esteem score. Their results were: t = 2.01, d = .90 (40 girls, 40 boys). This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center. This assignment requires that at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source be included. Directions In an essay (250-500 words), use the scenario presented above to thoroughly answer the following questions: What statistical test did the researchers use to determine if there was a statistically significant difference in levels of self-esteem between the boys and the girls?What was the purpose of calculating a Cohen's d? When is a Cohen's d calculated? Interpret d=.90. What does it mean in this example?What if the researcher compared the adolescent boys before treatment and again after treating them for depression? What type of t-test would be most appropriate in this case, and why? Module 5 Problem Set The problems assigned here are intended to give you contextual experience with the types of statistics you will encounter as you conduct your dissertation research. Completing the assigned problems will increase your comfort level with these tools. General Requirements: Use the following information to ensure successful completion of the assignment: This assignment is self-scored. Refer to “Module 5 Problem Set.” Solutions are available. Directions: 1. Complete the problems in “Module 5 Problem Set.” 2. Check your solutions by comparing your answers to the “Module 5 ProblemSolutions” document. 3. Submit to the instructor a statement indicating that you have completed this assignment. Module 5 SPSS Data Interpretation A between-group one-way analysis of variance (ANOVA) compares the mean values of more than two groups. In this assignment, you will review the SPSS output for an ANOVA and use it to answer questions about the means of the groups. General Requirements: Use the following information to ensure successful completion of the assignment: Review "SPSS Access Instructions" for information on how to access SPSS for this assignment.Download “SPSS Data Set Legend" for use with this assignment.Download "Module 5 SPSS Output" for use with this assignment. Directions: Review the SPSS output file, which reports the results of the between-group (independent group) one-way ANOVA to see if the mean alcohol by volume (%) of the beer differs as a function of quality of the brand as rated by a beer expert (in 2012). Answer the following questions based on your observations of the SPSS output file: 1. Looking at the descriptives (first information), do you see differences in the mean alcohol contents for the three levels of quality? Explain. 2. Looking at the Test for Homogeneity of Variances (Levene Statistic), is it reasonable to proceed with the ANOVA? Is the assumption met, or violated? How do you know? 3. Looking at the results of the ANOVA, is there a significant difference in the mean alcohol content for beers in the three quality groups? How do you know? Write the results in the following format: F(df value) = ___, p value = ______. 4. The pairwise post hoc tests indicate which quality groups' means are statistically significantly different for the others. Using the results of the Tukey HSD post hoc test, what two quality rating groups had significantly different mean alcohol by volume levels? How do you know?
5 pages
HLT 362v Exercise 20 Answers
Which patient scored the highest on the preoperative CVLT Acquisition?
Which patient scored the lowest on postoperative ...
HLT 362v Exercise 20 Answers
Which patient scored the highest on the preoperative CVLT Acquisition?
Which patient scored the lowest on postoperative CVLT Retrieval? What was this patient’s T score?
Ashford University Week 6 Statistic Questions
Please find the questions that needs to be answered in the attachment. the X that are underline the line should be on top ...
Ashford University Week 6 Statistic Questions
Please find the questions that needs to be answered in the attachment. the X that are underline the line should be on top of the x's didn't have time to properly place them as I need this NOW, Thanks in advance.
Why are partial regression coefficients necessary in multiple regression?
Multiple variables each provide some of the same information.Each variable involved contributes a unique error component.E ...
Why are partial regression coefficients necessary in multiple regression?
Multiple variables each provide some of the same information.Each variable involved contributes a unique error component.Each variable involved is part of the intercept value.Multiple variables each must provide components of the confidence interval.
Similar Content
Solve |4x - 38| = |8x - 22|
Solve |4x - 38| = |8x - 22|...
Ohio State University Statistics & Math Worksheet
...
The tip of an airplane propeller is 3 meters long, rotating 500 times per minute. (Hint: r = 1.5m)
The tip of an airplane propeller is 3 meters long, rotating 500 times per minute. (Hint: r = 1.5m)...
Precalculus chapter 1
...
Georgia Institute of Technology Calculus Questions
I need help answereing some multipart calculs 3 question. All I ask is for you to be as detailed and as clear as can be....
Project Assignment: The County Fair has usually been held in mid July each year. I wonder if this
Project Assignment:The County Fair has usually been held in mid July each year. I wonder if this is the best time? Your co...
What is variable?
A variable is any entity that can take on different values. , so what does that mean? Anything that can vary can be consid...
Solution
...
Homework Assignment
1. There is a new drug that is used to treat leukemia. The following data represents the remission time in weeks for a ran...
Related Tags
Book Guides
Heart of Darkness
by Joseph Conrad
Big Little Lies
by Liane Moriarty
Daisy Miller
by Henry James
The Two Towers
by J. R. R. Tolkien
Calypso
by David Sedaris
The 7 Habits of Highly Effective People
by Stephen R. Covey
Blink
by Malcolm Gladwell
Dandelion Wine
by Ray Bradbury
2001 A Space Odyssey
by Arthur Clarke
Get 24/7
Homework help
Our tutors provide high quality explanations & answers.
Post question
Most Popular Content
4 pages
Deliverable 03 Questions
1. Discuss the importance of constructing confidence intervals for the population mean by o What is the best point estimat ...
Deliverable 03 Questions
1. Discuss the importance of constructing confidence intervals for the population mean by o What is the best point estimate for the population mean? ...
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt ...
Harvard University Lab homework using R
#Lab 10#274-Wilcox (Fall 2019)#Name:#Student ID:rm(list=ls())source('Rallfun-v33.txt')#1) Import the dataset lab10hw1.txt in table form:#2) For this dataset, what is our dependent variable? #3) How many independent variables do we have? #4) How many levels does each independent variable have (use the function unique(x) to check)? #5) Make a boxplot for this set of data (submit the image). What problem do you see?#6) What is our null hypothesis?#7) Now use the classic method to analyze this dataset using the format aov(x~factor(g)). # Save this as an object called hw1.anova. #NOTE: MAKE SURE TO USE factor() AROUND YOUR GROUPING VARIABLE SO IT IS TREATED AS A FACTOR, NOT AS A NUMERIC VARIABLE. # Then summarize these results using summary(hw1.anova). #8) Do we reject or do we fail to reject the null hypothesis?#9) Now let's use the t1way() function, which is based on trimmed means and can deal with heteroscedasticity.#Hint 1: First, reorganize your data using fac2list(x, g). Save your new list as hw1.list.#Hint 2: You will need to have loaded in the source code to use the t1way function.#10) Do we reject or do we fail to reject the null hypothesis from 1.9?----------------------------------------------------------------------------------------------------------------------------------------------------------Lab 10 lecture notes:#Lab 10#Lab 10-Contents#1. One-Way Independent Groups ANOVA (Equal Variance)#2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#---------------------------------------------------------------------------------# 1. One-Way Independent Groups ANOVA (Equal Variance)#--------------------------------------------------------------------------------- #Scenario for first exercise: # A professor is interested in the effect of visualization strategies#on test performance. In order to study this, he tells students in#his statistics class that they will have a 15 question exam in #two weeks. Then, he randomly assigns students to three groups. # # The first group is told to spend 15 min each day vizualizing #the outcome of getting an A on the test to vividly imagine #the exam with an "A" written on it and how great it will feel. # # The second group is a control group that does no visualization. ## The third group is told to spend 15 min each day visualizing#the process of studying for the exam: imagine the hours of studying,#reviewing their chapters, working through chapter problems, # quizzing themeselves, etc. # Two weeks later, the students take the exam and the professor # records how many questions the students answer correctly out of 15.#So, the groups are:#Group 1: Visualize Outcome (Grade)#Group 2: No visualization (Control)#Group 3: Visiualize Process (Studying)#######################################################Question: Are the groups here Independent?#######################################################We'll instroduce a few new terms: #Factor: A variable that consists of categories. #Levels: The categories of the Factor variable. #In our example above, the variable that contains#the groups is called "Group". #So, our factor is the variable "Group"#How many levels are there for the Group Factor?#Let's read in LAB10A.txtlab10a=read.table('LAB10A.txt', header=T)#While we can easily see the levels for the Group #factor we could also use a new command to figure out #the number of unique levels.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Number of Unique Levels: unique(data$variable)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#unique(lab10a$Group) #As we can see, there are 3 levels. 1, 2, and 3#Look at boxplot of each group using #boxplot(y~group, data=data)par(mfrow=c(1,1))boxplot(Score~Group, data=lab10a)#Do you think the means will be different (statistically)#between the groups?#Before we begin to test for differences between #the means, let's wrtie out our NUll #and Alternative Hyhpotheses#H0: The means are equal (mu1=mu2=mu3)#HA: At least one mean is different. #(eg. mu1 != mu2 OR mu1 != mu3 OR mu2 != mu3 )#To test the Hypothesis we can use the ANOVA function aov():#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## One-Way ANOVA: aov(y~factor(g), data)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##The aov() function assumes that the #variance is the same within each of the groups.mod1=aov(Score ~ factor(Group), data=lab10a)summary(mod1)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis#Do we Reject or Fail to Reject the Null?#Reject 0.00129 < .05 then Reject H0#What does this tell us? That the groups are different?#If so, how do we know which groups?#P-value we just got is called the Omnibus P-value, #which tells us that there are differences somewhere#With this P-value we often use the term #"Main Effect" to say that there is an effect of the#factor on the outcome.#In this instance we'd say that there is a Main Effect #of Group on the Score.#To Answer which groups are different, we need to first#conver the data into List Mode (a different way #of storing the data). We can convert the factor Group #to a list using the function fac2list(y, g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Convert Factors to List Data: fac2list(data$y, data$g)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^#listA=fac2list(lab10a$Score, lab10a$Group)listA #Once the data is in List Mode we have to use the#lincon() command from Dr. Wilcox's source code.#The lincon() package is used to compare the groups while#controlling for the experimentwise Type 1 error rate.#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Compare Groups: lincon(list_name, tr=0.2)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##By default lincon() compares groups using 20% trimming. #We will set this to 0 for now:lincon(listA, tr=0) #result:# H0_1: mu1=mu2 --- p=0.32 ---Fail to reject# H0_2: mu1=mu3 --- p=0.0009 ---Reject# H0_3: mu2=mu3 --- p=0.008 ---Reject#---------------------------------------------------------------------------------# 2. One-Way Independent Groups ANOVA (Unequal Variance-Welch's Test)#--------------------------------------------------------------------------------- # We just learned how to conduct a One-Way ANOVA # when the variances are equal within each group. # Now, we will learn how to conduct a One-Way ANOVA #for then the variance is not equal.# Let's start by reading in the LAB10B.txt datafile.lab10b=read.table('LAB10B.txt', header=T)# Then examine a boxplot of all of it.boxplot(Score~factor(Group), data=lab10b)# What do we notice about this boxplot?#-----# Let's start by running the equal variance ANOVA#on the data (which of course is WRONG!)mod2=aov(Score ~ factor(Group), data=lab10b) #---DON'Tsummary(mod2)#A) If pval < alpha, then Reject the Null Hypothesis#B) If pval > alpha, then Fail to Reject the Null Hypothesis# Do we Reject or Fail to Reject the Null?#Fail to reject: p-value=0.0895 > .05 !!!INCORRECT----#----# Now let's try to run the correct test that assumes #unequal variance. #We call this the Welch's test (just like in the t-test)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^## Welch's One-Way ANOVA: t1way(list_name, tr=0.20)#^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^##In order to use this t1way function, #we will first need to convert the data to #List Mode using fac2list()listB=fac2list(lab10b$Score, lab10b$Group)t1way(listB, tr=0.2)# Do we Reject or Fail to Reject the Null?#Reject: p-value:0.04966583 <.05#Again, we can use the lincon() command to #find out Where the group differences are.#This time we will use the 20% trimming.lincon(listB, tr=0.2)# G1 and G2: p-value=0.92210409 > .05 Fail to reject# G1 and G3: p-value=0.19451518 > .05 Fail to reject#G2 and G3: p-value=0.03227316 < .05 Reject#
Week 5 – IA Analysis of Variance, statistics homework help
Week 5 – IA Analysis of Variance(OVERVIEW) (tohw) This Assignment has 3 parts (attached separately with this Overview ...
Week 5 – IA Analysis of Variance, statistics homework help
Week 5 – IA Analysis of Variance(OVERVIEW) (tohw) This Assignment has 3 parts (attached separately with this Overview), 1. Part 1. Module 5 Problem Set (Attached) 2. Part 2. Module 5 SPSS Data Interpretation 3. Part 3. Statistical Tests 4. DQs (Sent under separate attachment) Statistical Tests Doctoral researchers must be able to understand statistical tests and select the appropriate test for the research they are conducting. This assignment will allow you to practice your skills in selecting and using the appropriate statistical tests for a given research study. General Information Use the following information to ensure successful completion of the assignment: Researchers wanted to explore self-esteem in adolescent boys and adolescent girls. Each respondent completed a 10-item self-esteem scale (they chose one rating for each item from a Likert-type scale, 1 = strongly disagree and 5 = strongly agree). The sum of the 10 ratings was each respondent's self-esteem score. Their results were: t = 2.01, d = .90 (40 girls, 40 boys). This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center. This assignment requires that at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source be included. Directions In an essay (250-500 words), use the scenario presented above to thoroughly answer the following questions: What statistical test did the researchers use to determine if there was a statistically significant difference in levels of self-esteem between the boys and the girls?What was the purpose of calculating a Cohen's d? When is a Cohen's d calculated? Interpret d=.90. What does it mean in this example?What if the researcher compared the adolescent boys before treatment and again after treating them for depression? What type of t-test would be most appropriate in this case, and why? Module 5 Problem Set The problems assigned here are intended to give you contextual experience with the types of statistics you will encounter as you conduct your dissertation research. Completing the assigned problems will increase your comfort level with these tools. General Requirements: Use the following information to ensure successful completion of the assignment: This assignment is self-scored. Refer to “Module 5 Problem Set.” Solutions are available. Directions: 1. Complete the problems in “Module 5 Problem Set.” 2. Check your solutions by comparing your answers to the “Module 5 ProblemSolutions” document. 3. Submit to the instructor a statement indicating that you have completed this assignment. Module 5 SPSS Data Interpretation A between-group one-way analysis of variance (ANOVA) compares the mean values of more than two groups. In this assignment, you will review the SPSS output for an ANOVA and use it to answer questions about the means of the groups. General Requirements: Use the following information to ensure successful completion of the assignment: Review "SPSS Access Instructions" for information on how to access SPSS for this assignment.Download “SPSS Data Set Legend" for use with this assignment.Download "Module 5 SPSS Output" for use with this assignment. Directions: Review the SPSS output file, which reports the results of the between-group (independent group) one-way ANOVA to see if the mean alcohol by volume (%) of the beer differs as a function of quality of the brand as rated by a beer expert (in 2012). Answer the following questions based on your observations of the SPSS output file: 1. Looking at the descriptives (first information), do you see differences in the mean alcohol contents for the three levels of quality? Explain. 2. Looking at the Test for Homogeneity of Variances (Levene Statistic), is it reasonable to proceed with the ANOVA? Is the assumption met, or violated? How do you know? 3. Looking at the results of the ANOVA, is there a significant difference in the mean alcohol content for beers in the three quality groups? How do you know? Write the results in the following format: F(df value) = ___, p value = ______. 4. The pairwise post hoc tests indicate which quality groups' means are statistically significantly different for the others. Using the results of the Tukey HSD post hoc test, what two quality rating groups had significantly different mean alcohol by volume levels? How do you know?
5 pages
HLT 362v Exercise 20 Answers
Which patient scored the highest on the preoperative CVLT Acquisition?
Which patient scored the lowest on postoperative ...
HLT 362v Exercise 20 Answers
Which patient scored the highest on the preoperative CVLT Acquisition?
Which patient scored the lowest on postoperative CVLT Retrieval? What was this patient’s T score?
Ashford University Week 6 Statistic Questions
Please find the questions that needs to be answered in the attachment. the X that are underline the line should be on top ...
Ashford University Week 6 Statistic Questions
Please find the questions that needs to be answered in the attachment. the X that are underline the line should be on top of the x's didn't have time to properly place them as I need this NOW, Thanks in advance.
Why are partial regression coefficients necessary in multiple regression?
Multiple variables each provide some of the same information.Each variable involved contributes a unique error component.E ...
Why are partial regression coefficients necessary in multiple regression?
Multiple variables each provide some of the same information.Each variable involved contributes a unique error component.Each variable involved is part of the intercept value.Multiple variables each must provide components of the confidence interval.
Earn money selling
your Study Documents