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## Instructions Your final project should include Part A and all of the following:

Instructions
Your final project should include Part A and all of the following:
Sampling: A careful desсrіption of how the samples were obtained. Be very specific. Include sample sizes, the population of interest, and a desсrіption of the sample. Also, include a copy of the survey if you used one.
Data: The actual data and a summary of the counts.
Desсrіptive Statistics: Any desсrіptive statistics relevant to your project should be included.
You are required to give the mean, mode, median, and standard deviation of your data (2 sets if you are doing 2 means).
At least two graphs (such as box plots, scatter plots, stem-and-leaf, histograms, etc.) should be part of your project. The graphs can be a way to summarize desсrіptive statistics.
Hypotheses testing: Show your hypothesis test and discuss any conclusions they suggest
State the hypotheses you will test and formally write them in proper notation
Give the results (reject, fail to reject) and explain what that means in a practical way.
Conclusion:
Discuss the weaknesses of your study.
To what population do you feel comfortable extrapolating your results? Why?
Give suggestions for further work in the area of your topic. Be sure to reference the situation as you discuss your conclusion. (this is a great time to seek out similar published research on your topic, is yours in line with those results? If not why do you think that is?
Explain what leads you to the conclusion(s) that you have drawn.
What would you change if you started this project again? What considerations would you include? Include any areas of caution that a reader or user of your work should be aware of as they make decisions. Was the sample random, was the sample large enough, etc?
References in APA format.
This project provides you with an opportunity to pull together much of the statistics of this course and apply it to a topic of interest to you. You must gather your own data by observational study, controlled experiment, or survey. Data will need to be such that analysis can be done using the tools of this course. You will take the first steps towards applying Statistics to real-life situations. Consider subjects you are interested in or topics that you are curious about.
You are going to want to select a data set related to sports, real-estate, and/or crime statistics. Consider subjects you are interested in or topics that you are curious about. If you would like to choose your own topic, such as the field-specific examples below, please be sure to approve your topic with your instructor PRIOR to collecting data.
Field-specific examples:
Healthcare: Stress test score and blood pressure reading, cigarettes smoked per day, and lung cancer mortality
Criminal Justice: Incidents at a traffic intersection each year
Business: Mean school spending and socio-economic level
Electronics Engineering Technology: Machine setting and energy consumption
Computer Information Systems: Time of day and internet speeds

Categories

## Instructions Your final project should include Part A and all of the following:

Instructions
Your final project should include Part A and all of the following:
Sampling: A careful desсrіption of how the samples were obtained. Be very specific. Include sample sizes, the population of interest, and a desсrіption of the sample. Also, include a copy of the survey if you used one.
Data: The actual data and a summary of the counts.
Desсrіptive Statistics: Any desсrіptive statistics relevant to your project should be included.
You are required to give the mean, mode, median, and standard deviation of your data (2 sets if you are doing 2 means).
At least two graphs (such as box plots, scatter plots, stem-and-leaf, histograms, etc.) should be part of your project. The graphs can be a way to summarize desсrіptive statistics.
Hypotheses testing: Show your hypothesis test and discuss any conclusions they suggest
State the hypotheses you will test and formally write them in proper notation
Give the results (reject, fail to reject) and explain what that means in a practical way.
Conclusion:
Discuss the weaknesses of your study.
To what population do you feel comfortable extrapolating your results? Why?
Give suggestions for further work in the area of your topic. Be sure to reference the situation as you discuss your conclusion. (this is a great time to seek out similar published research on your topic, is yours in line with those results? If not why do you think that is?
Explain what leads you to the conclusion(s) that you have drawn.
What would you change if you started this project again? What considerations would you include? Include any areas of caution that a reader or user of your work should be aware of as they make decisions. Was the sample random, was the sample large enough, etc?
References in APA format.
This project provides you with an opportunity to pull together much of the statistics of this course and apply it to a topic of interest to you. You must gather your own data by observational study, controlled experiment, or survey. Data will need to be such that analysis can be done using the tools of this course. You will take the first steps towards applying Statistics to real-life situations. Consider subjects you are interested in or topics that you are curious about.
You are going to want to select a data set related to sports, real-estate, and/or crime statistics. Consider subjects you are interested in or topics that you are curious about. If you would like to choose your own topic, such as the field-specific examples below, please be sure to approve your topic with your instructor PRIOR to collecting data.
Field-specific examples:
Healthcare: Stress test score and blood pressure reading, cigarettes smoked per day, and lung cancer mortality
Criminal Justice: Incidents at a traffic intersection each year
Business: Mean school spending and socio-economic level
Electronics Engineering Technology: Machine setting and energy consumption
Computer Information Systems: Time of day and internet speeds

Categories

## For this assignment choose the dataset that you are interested in exploring furt

For this assignment choose the dataset that you are interested in exploring further:
County Health Rankings – need to do something different then what you did in assignment 5
World Development Indicators
Sports Data (one that we used in assignment 9) – need to do something different then what you did in assignment 9
You need to:
state a testable hypothesis
create a dataset to test the hypothesis with at least 50 observations. There was must at least 4 variables in your dataset
explain what each variable is (define it and explain why you used it). You need to include summary stats: min, max, mean (average)
use averageifs() at least twice when trying to test your hypothesis
make at least 2 plots that test your hypothesis
run a regression to test your hypothesis with at least 2 variables and discuss those results
write a summary of your results (be specific – include values and interpret)
describe one additional variable you would have liked to include that wasn’t available
discuss one shortcoming of the data that you used
No attachments yet
Use this website to gather your data:
https://www.countyhealthrankings.org/

Categories

## Assignment Instructions POWER ANALYSIS Assignment Instructions This is a three-p

Assignment Instructions
POWER ANALYSIS
Assignment Instructions
This is a three-part assignment in which you will demonstrate your ability to:
Analyze components of a t-test required for power analysis.
Compute and interpret a post-hoc power analysis.
Compute and interpret an a priori power analysis.
In addition to your statistical software, you will also use the G*Power 3 software to complete this assignment. Answer each question, providing statistical software or G* Power analysis output when necessary to support your answer.
Use the data file provided by your instructor for this assignment. You will be conducting a post-hoc power analysis and an a priori power analysis on an independent samples t-test with extra credit as the grouping variable (no ＝ 1; yes ＝ 2) and Total as the outcome variable. There are three sections of this assignment. After reporting the t-test results, you will then conduct a post-hoc power analysis followed by an a priori power analysis.
Section 1: Reporting the t-Test Results
Using the supplied data set, conduct an independent samples t-test with extra credit as the grouping variable (no ＝ 1; yes ＝ 2) and Total as the outcome variable.
Paste the output and then report:
The sample size for no (n1) and sample size for yes (n2).
The means for no (M1) and yes (M2) on Total.
The calculated mean difference (M1 – M2).
The standard deviations for no (s1) and yes (s2) on Total.
The Levene test (homogeneity of variance assumption) and interpretation.
t, degrees of freedom, t value, and p value. State whether or not to reject the null hypothesis. Interpret the results.
Calculate Cohen′s d effect size and interpret it. Specifically, if the homogeneity of variance assumption is met, calculate Cohen′s d as described below. Violation of the homogeneity of variance assumption requires calculation of Spooled. Homogeneity assumed:
Cohen′s d ＝ (M1 – M2) ÷ s1 or Cohen′s d ＝ (M1 – M2) ÷ s2.
To be comprehensive, report Cohen′s d based on a calculation with s1 and a calculation with s2. Round the effect size to two decimal places. Interpret Cohen′s d.
Section 2: Post-hoc Power Analysis
Open G*Power. Select the following options:
Test family ＝ t-tests.
Statistical test ＝ Means: Difference between two independent groups (two groups).
Type of power analysis ＝ Post hoc: Compute achieved power.
Tails ＝ Two.
Effect size d ＝ Cohen′s d obtained from Section 1 above (using either s1 or s2).
α err prob ＝ standard alpha level.
Sample size group 1 ＝ n1 from Section 1 above.
Sample size group 2 ＝ n2 from Section 1 above.
Click Calculate.
Provide a screenshot of your G*Power output. Report the observed power of this post-hoc power analysis. Interpret the level of power in terms of rejecting a null hypothesis. Do you have sufficient power to reject a false null hypothesis? Interpret power in terms of committing a Type II error.
Section 3: A Priori Power Analysis
In G*Power, now select:
Type of power analysis ＝ A priori: Compute required sample size.
Input effect size d from Section 1.
Specify α err prob.
Specify Power (1 – β) ＝ .80.
Set the Allocation ratio to 1 (that is, equal sample sizes).
Press Calculate.
Provide a screenshot of your G*Power output. Interpret the meaning of a .80 power value. Specifically, report the estimated n1, n2, and total N to achieve obtain a power of .80. How many total subjects (N) would be needed to obtain a power of .80? Would you have expected a required N of this size? Why or why not?
Next, in G*Power, change the Cohen′s d effect size value obtained in Section 1 and set it to .50 (conventional ″medium″ effect size). Click Calculate. How many total subjects (N) are needed to obtain a power of .80? Compare and contrast these two estimated Ns.
In conclusion, reflect on the importance of conducting an a priori power analysis in psychological research plans.
Written communication: Should be free of errors that detract from the overall message.
APA formatting: References and citations are formatted according to current APA style guidelines. Refer to Evidence and APA for more information on how to cite your sources.
Length: 8–10 double-spaced pages, in addition to the title page and references page.

Categories

## Assignment Instructions POWER ANALYSIS Assignment Instructions This is a three-p

Assignment Instructions
POWER ANALYSIS
Assignment Instructions
This is a three-part assignment in which you will demonstrate your ability to:
Analyze components of a t-test required for power analysis.
Compute and interpret a post-hoc power analysis.
Compute and interpret an a priori power analysis.
In addition to your statistical software, you will also use the G*Power 3 software to complete this assignment. Answer each question, providing statistical software or G* Power analysis output when necessary to support your answer.
Use the data file provided by your instructor for this assignment. You will be conducting a post-hoc power analysis and an a priori power analysis on an independent samples t-test with extra credit as the grouping variable (no ＝ 1; yes ＝ 2) and Total as the outcome variable. There are three sections of this assignment. After reporting the t-test results, you will then conduct a post-hoc power analysis followed by an a priori power analysis.
Section 1: Reporting the t-Test Results
Using the supplied data set, conduct an independent samples t-test with extra credit as the grouping variable (no ＝ 1; yes ＝ 2) and Total as the outcome variable.
Paste the output and then report:
The sample size for no (n1) and sample size for yes (n2).
The means for no (M1) and yes (M2) on Total.
The calculated mean difference (M1 – M2).
The standard deviations for no (s1) and yes (s2) on Total.
The Levene test (homogeneity of variance assumption) and interpretation.
t, degrees of freedom, t value, and p value. State whether or not to reject the null hypothesis. Interpret the results.
Calculate Cohen′s d effect size and interpret it. Specifically, if the homogeneity of variance assumption is met, calculate Cohen′s d as described below. Violation of the homogeneity of variance assumption requires calculation of Spooled. Homogeneity assumed:
Cohen′s d ＝ (M1 – M2) ÷ s1 or Cohen′s d ＝ (M1 – M2) ÷ s2.
To be comprehensive, report Cohen′s d based on a calculation with s1 and a calculation with s2. Round the effect size to two decimal places. Interpret Cohen′s d.
Section 2: Post-hoc Power Analysis
Open G*Power. Select the following options:
Test family ＝ t-tests.
Statistical test ＝ Means: Difference between two independent groups (two groups).
Type of power analysis ＝ Post hoc: Compute achieved power.
Tails ＝ Two.
Effect size d ＝ Cohen′s d obtained from Section 1 above (using either s1 or s2).
α err prob ＝ standard alpha level.
Sample size group 1 ＝ n1 from Section 1 above.
Sample size group 2 ＝ n2 from Section 1 above.
Click Calculate.
Provide a screenshot of your G*Power output. Report the observed power of this post-hoc power analysis. Interpret the level of power in terms of rejecting a null hypothesis. Do you have sufficient power to reject a false null hypothesis? Interpret power in terms of committing a Type II error.
Section 3: A Priori Power Analysis
In G*Power, now select:
Type of power analysis ＝ A priori: Compute required sample size.
Input effect size d from Section 1.
Specify α err prob.
Specify Power (1 – β) ＝ .80.
Set the Allocation ratio to 1 (that is, equal sample sizes).
Press Calculate.
Provide a screenshot of your G*Power output. Interpret the meaning of a .80 power value. Specifically, report the estimated n1, n2, and total N to achieve obtain a power of .80. How many total subjects (N) would be needed to obtain a power of .80? Would you have expected a required N of this size? Why or why not?
Next, in G*Power, change the Cohen′s d effect size value obtained in Section 1 and set it to .50 (conventional ″medium″ effect size). Click Calculate. How many total subjects (N) are needed to obtain a power of .80? Compare and contrast these two estimated Ns.
In conclusion, reflect on the importance of conducting an a priori power analysis in psychological research plans.